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| author | Johannes Ranke <jranke@uni-bremen.de> | 2021-10-05 17:33:52 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2021-10-05 17:33:52 +0200 |
| commit | ff83d8b2ba623513d92ac90fac4a1b0ec98c2cb5 (patch) | |
| tree | 7811a1e083951d84bd54bfce9bb62af640da329e /docs/dev | |
| parent | 121e38654eea03c91aa117da58fc342feff01756 (diff) | |
Update docs
Diffstat (limited to 'docs/dev')
| -rw-r--r-- | docs/dev/articles/web_only/dimethenamid_2018.html | 12 | ||||
| -rw-r--r-- | docs/dev/pkgdown.yml | 2 | ||||
| -rw-r--r-- | docs/dev/reference/Rplot001.png | bin | 43816 -> 1011 bytes | |||
| -rw-r--r-- | docs/dev/reference/Rplot002.png | bin | 16858 -> 59380 bytes | |||
| -rw-r--r-- | docs/dev/reference/Rplot003.png | bin | 28838 -> 59146 bytes | |||
| -rw-r--r-- | docs/dev/reference/dimethenamid_2018.html | 463 | ||||
| -rw-r--r-- | docs/dev/reference/mixed.html | 2 | ||||
| -rw-r--r-- | docs/dev/reference/mkinmod.html | 4 | ||||
| -rw-r--r-- | docs/dev/reference/nlme-1.png | bin | 68789 -> 68943 bytes | |||
| -rw-r--r-- | docs/dev/reference/nlme-2.png | bin | 92904 -> 94409 bytes | |||
| -rw-r--r-- | docs/dev/reference/nlme.html | 16 | ||||
| -rw-r--r-- | docs/dev/reference/nlmixr.mmkin.html | 9800 | ||||
| -rw-r--r-- | docs/dev/reference/plot.mixed.mmkin.html | 10 | ||||
| -rw-r--r-- | docs/dev/reference/saem.html | 388 | ||||
| -rw-r--r-- | docs/dev/reference/summary.nlmixr.mmkin.html | 790 | ||||
| -rw-r--r-- | docs/dev/reference/summary.saem.mmkin.html | 270 | ||||
| -rw-r--r-- | docs/dev/reference/tffm0.html | 2 |
17 files changed, 10190 insertions, 1569 deletions
diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html index aa84435d..13b0f98e 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018.html +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 27 September 2021, built on 27 Sep 2021</h4> + <h4 class="date">Last change 27 September 2021, built on 05 Okt 2021</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/dimethenamid_2018.rmd"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small> <div class="hidden name"><code>dimethenamid_2018.rmd</code></div> @@ -151,20 +151,20 @@ error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><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="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png" width="700"></p> <p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><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="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png" width="700"></p> <p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><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="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png" width="700"></p> <p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> <p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><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="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png" width="700"></p> </div> <div id="nonlinear-mixed-effects-models" class="section level2"> @@ -205,7 +205,7 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> <p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><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_parent_nlme_dfop_tc</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png" width="700"></p> </div> <div id="saemix" class="section level3"> diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index a975d20f..15028215 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -11,7 +11,7 @@ articles: web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html web_only/dimethenamid_2018: dimethenamid_2018.html -last_built: 2021-09-28T09:12Z +last_built: 2021-10-05T14:56Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png Binary files differindex c1666cff..17a35806 100644 --- a/docs/dev/reference/Rplot001.png +++ b/docs/dev/reference/Rplot001.png diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png Binary files differindex 32c64fcd..b538b8d5 100644 --- a/docs/dev/reference/Rplot002.png +++ b/docs/dev/reference/Rplot002.png diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png Binary files differindex 5726488c..c90dd2e2 100644 --- a/docs/dev/reference/Rplot003.png +++ b/docs/dev/reference/Rplot003.png diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 919e9363..60c15ade 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -222,7 +222,7 @@ specific pieces of information in the comments.</p> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO3+"</span> <span class='op'>=</span> <span class='va'>dfop_sfo3_plus</span><span class='op'>)</span>, <span class='va'>dmta_ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> <span class='fu'><a href='nlmixr.mmkin.html'>nlmixr_model</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span><span class='op'>)</span> -</div><div class='output co'>#> <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'>#> function () +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='warning'>Warning: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#> function () #> { #> ini({ #> DMTA_0 = 98.7132391714013 @@ -263,9 +263,9 @@ specific pieces of information in the comments.</p> #> k1 = exp(log_k1 + eta.log_k1) #> k2 = exp(log_k2 + eta.log_k2) #> g = expit(g_qlogis + eta.g_qlogis) -#> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) -#> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) -#> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) #> f_DMTA_to_M23 = f_DMTA_tffm0_1 #> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1) #> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * @@ -289,205 +289,122 @@ specific pieces of information in the comments.</p> #> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559e97ac0></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> +#> <environment: 0x555559d89920></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> <span class='va'>f_dmta_nlmixr_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>)</span> -</div><div class='output co'>#> <span class='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='warning'>Warning: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 #> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 #> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 -#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:09 #> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 -#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:06 #> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 -#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span> -#> <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#> <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 | DMTA_0 | 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 |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high | -#> |.....................| o1 | o2 | o3 | o4 | -#> |.....................| 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='output co'>#> user system elapsed -#> 230.015 8.962 238.957 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> -</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 -#> mkin version used for pre-fitting: 1.1.0 -#> R version used for fitting: 4.1.1 -#> Date of fit: Thu Sep 16 14:06:55 2021 -#> Date of summary: Thu Sep 16 14:06:55 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: -#> 563 observations of 4 variable(s) grouped in 6 datasets -#> -#> Degradation model predictions using RxODE -#> -#> Fitted in 238.792 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 -#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 -#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7554 -2.2352 -3.7758 0.4363 -#> -#> Mean of starting values for error model parameters: -#> sigma_low rsd_high -#> 0.70012 0.02577 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood calculated by focei -#> AIC BIC logLik -#> 1918 2014 -937.2 -#> -#> Optimised parameters: -#> est. lower upper -#> DMTA_0 98.7132 98.6801 98.7464 -#> log_k_M23 -3.9216 -3.9235 -3.9198 -#> log_k_M27 -4.3306 -4.3326 -4.3286 -#> log_k_M31 -4.2442 -4.2461 -4.2422 -#> log_k1 -2.2352 -2.2364 -2.2340 -#> log_k2 -3.7758 -3.7776 -3.7740 -#> g_qlogis 0.4363 0.4358 0.4368 -#> f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903 -#> f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776 -#> f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392 -#> -#> Correlation: -#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs -#> log_k_M23 0 -#> log_k_M27 0 0 -#> log_k_M31 0 0 0 -#> log_k1 0 0 0 0 -#> log_k2 0 0 0 0 0 -#> g_qlogis 0 0 0 0 0 0 -#> f_DMTA_tffm0_1_qlogis 0 0 0 0 0 0 0 -#> f_DMTA_tffm0_2_qlogis 0 0 0 0 0 0 0 -#> f_DMTA_tffm0_3_qlogis 0 0 0 0 0 0 0 -#> 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 -#> f_DMTA_tffm0_3_qlogis 0 0 -#> -#> Random effects (omega): -#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 2.327 0.0000 0.0000 0.0000 -#> eta.log_k_M23 0.000 0.5493 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.0000 0.8552 0.0000 -#> eta.log_k_M31 0.000 0.0000 0.0000 0.7457 -#> eta.log_k1 0.000 0.0000 0.0000 0.0000 -#> eta.log_k2 0.000 0.0000 0.0000 0.0000 -#> eta.g_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.log_k1 eta.log_k2 eta.g_qlogis -#> eta.DMTA_0 0.000 0.000 0.000 -#> eta.log_k_M23 0.000 0.000 0.000 -#> eta.log_k_M27 0.000 0.000 0.000 -#> eta.log_k_M31 0.000 0.000 0.000 -#> eta.log_k1 0.901 0.000 0.000 -#> eta.log_k2 0.000 1.577 0.000 -#> eta.g_qlogis 0.000 0.000 3.102 -#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000 -#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000 -#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000 -#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis -#> eta.DMTA_0 0.0 0.0 -#> eta.log_k_M23 0.0 0.0 -#> eta.log_k_M27 0.0 0.0 -#> eta.log_k_M31 0.0 0.0 -#> eta.log_k1 0.0 0.0 -#> eta.log_k2 0.0 0.0 -#> eta.g_qlogis 0.0 0.0 -#> eta.f_DMTA_tffm0_1_qlogis 0.3 0.0 -#> eta.f_DMTA_tffm0_2_qlogis 0.0 0.3 -#> eta.f_DMTA_tffm0_3_qlogis 0.0 0.0 -#> eta.f_DMTA_tffm0_3_qlogis -#> eta.DMTA_0 0.0 -#> eta.log_k_M23 0.0 -#> eta.log_k_M27 0.0 -#> eta.log_k_M31 0.0 -#> eta.log_k1 0.0 -#> eta.log_k2 0.0 -#> eta.g_qlogis 0.0 -#> eta.f_DMTA_tffm0_1_qlogis 0.0 -#> eta.f_DMTA_tffm0_2_qlogis 0.0 -#> eta.f_DMTA_tffm0_3_qlogis 0.3 -#> -#> Variance model: -#> sigma_low rsd_high -#> 0.70012 0.02577 -#> -#> Backtransformed parameters: -#> est. lower upper -#> DMTA_0 98.71324 98.68012 98.74636 -#> k_M23 0.01981 0.01977 0.01985 -#> k_M27 0.01316 0.01313 0.01319 -#> k_M31 0.01435 0.01432 0.01438 -#> f_DMTA_to_M23 0.10993 NA NA -#> f_DMTA_to_M27 0.09049 NA NA -#> f_DMTA_to_M31 0.08414 NA NA -#> k1 0.10698 0.10685 0.10710 -#> k2 0.02292 0.02288 0.02296 -#> g 0.60738 0.60725 0.60751 -#> -#> Resulting formation fractions: -#> ff -#> DMTA_M23 0.10993 -#> DMTA_M27 0.09049 -#> DMTA_M31 0.08414 -#> DMTA_sink 0.71543 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 10.72 60.1 18.09 6.48 30.24 -#> M23 34.99 116.2 NA NA NA -#> M27 52.67 175.0 NA NA NA -#> M31 48.31 160.5 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> -</div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span> +#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(DMTA);</span> +#> <span class='message'>cmt(M23);</span> +#> <span class='message'>cmt(M27);</span> +#> <span class='message'>cmt(M31);</span> +#> <span class='message'>rx_expr_14~ETA[1]+THETA[1];</span> +#> <span class='message'>DMTA(0)=rx_expr_14;</span> +#> <span class='message'>rx_expr_15~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_16~ETA[7]+THETA[7];</span> +#> <span class='message'>rx_expr_17~ETA[6]+THETA[6];</span> +#> <span class='message'>rx_expr_24~exp(rx_expr_15);</span> +#> <span class='message'>rx_expr_25~exp(rx_expr_17);</span> +#> <span class='message'>rx_expr_29~t*rx_expr_24;</span> +#> <span class='message'>rx_expr_30~t*rx_expr_25;</span> +#> <span class='message'>rx_expr_31~exp(-(rx_expr_16));</span> +#> <span class='message'>rx_expr_35~1+rx_expr_31;</span> +#> <span class='message'>rx_expr_40~1/(rx_expr_35);</span> +#> <span class='message'>rx_expr_42~(rx_expr_40);</span> +#> <span class='message'>rx_expr_43~1-rx_expr_42;</span> +#> <span class='message'>d/dt(DMTA)=-DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span> +#> <span class='message'>rx_expr_18~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_26~exp(rx_expr_18);</span> +#> <span class='message'>d/dt(M23)=-rx_expr_26*M23+DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_1/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span> +#> <span class='message'>rx_expr_19~ETA[3]+THETA[3];</span> +#> <span class='message'>rx_expr_20~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_21~1-f_DMTA_tffm0_1;</span> +#> <span class='message'>rx_expr_27~exp(rx_expr_19);</span> +#> <span class='message'>rx_expr_28~exp(rx_expr_20);</span> +#> <span class='message'>d/dt(M27)=-rx_expr_27*M27+rx_expr_28*M31+DMTA*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_2/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span> +#> <span class='message'>rx_expr_22~1-f_DMTA_tffm0_2;</span> +#> <span class='message'>d/dt(M31)=-rx_expr_28*M31+DMTA*(rx_expr_22)*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_3/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span> +#> <span class='message'>rx_expr_0~CMT==4;</span> +#> <span class='message'>rx_expr_1~CMT==2;</span> +#> <span class='message'>rx_expr_2~CMT==1;</span> +#> <span class='message'>rx_expr_3~CMT==3;</span> +#> <span class='message'>rx_expr_4~1-(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~1-(rx_expr_1);</span> +#> <span class='message'>rx_expr_6~1-(rx_expr_3);</span> +#> <span class='message'>rx_yj_~(rx_expr_4)*((2*(rx_expr_5)*(rx_expr_2)+2*(rx_expr_1))*(rx_expr_6)+2*(rx_expr_3))+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_7~(rx_expr_1);</span> +#> <span class='message'>rx_expr_8~(rx_expr_3);</span> +#> <span class='message'>rx_expr_9~(rx_expr_0);</span> +#> <span class='message'>rx_expr_13~(rx_expr_5);</span> +#> <span class='message'>rx_expr_32~rx_expr_13*(rx_expr_2);</span> +#> <span class='message'>rx_lambda_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span> +#> <span class='message'>rx_hi_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_10~M31*(rx_expr_0);</span> +#> <span class='message'>rx_expr_11~M27*(rx_expr_3);</span> +#> <span class='message'>rx_expr_12~M23*(rx_expr_1);</span> +#> <span class='message'>rx_expr_23~DMTA*(rx_expr_5);</span> +#> <span class='message'>rx_expr_36~rx_expr_23*(rx_expr_2);</span> +#> <span class='message'>rx_pred_=(rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)));</span> +#> <span class='message'>rx_expr_33~Rx_pow_di(THETA[12],2);</span> +#> <span class='message'>rx_expr_34~Rx_pow_di(THETA[11],2);</span> +#> <span class='message'>rx_r_=(rx_expr_4)*((rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6)),2)+rx_expr_34)*(rx_expr_3)+((rx_expr_1)*(rx_expr_33*Rx_pow_di(((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)+(rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)*(rx_expr_5)*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_33*Rx_pow_di(((rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))),2)+rx_expr_34);</span> +#> <span class='message'>DMTA_0=THETA[1];</span> +#> <span class='message'>log_k_M23=THETA[2];</span> +#> <span class='message'>log_k_M27=THETA[3];</span> +#> <span class='message'>log_k_M31=THETA[4];</span> +#> <span class='message'>log_k1=THETA[5];</span> +#> <span class='message'>log_k2=THETA[6];</span> +#> <span class='message'>g_qlogis=THETA[7];</span> +#> <span class='message'>f_DMTA_tffm0_1_qlogis=THETA[8];</span> +#> <span class='message'>f_DMTA_tffm0_2_qlogis=THETA[9];</span> +#> <span class='message'>f_DMTA_tffm0_3_qlogis=THETA[10];</span> +#> <span class='message'>sigma_low=THETA[11];</span> +#> <span class='message'>rsd_high=THETA[12];</span> +#> <span class='message'>eta.DMTA_0=ETA[1];</span> +#> <span class='message'>eta.log_k_M23=ETA[2];</span> +#> <span class='message'>eta.log_k_M27=ETA[3];</span> +#> <span class='message'>eta.log_k_M31=ETA[4];</span> +#> <span class='message'>eta.log_k1=ETA[5];</span> +#> <span class='message'>eta.log_k2=ETA[6];</span> +#> <span class='message'>eta.g_qlogis=ETA[7];</span> +#> <span class='message'>eta.f_DMTA_tffm0_1_qlogis=ETA[8];</span> +#> <span class='message'>eta.f_DMTA_tffm0_2_qlogis=ETA[9];</span> +#> <span class='message'>eta.f_DMTA_tffm0_3_qlogis=ETA[10];</span> +#> <span class='message'>DMTA_0_model=rx_expr_14;</span> +#> <span class='message'>k_M23=rx_expr_26;</span> +#> <span class='message'>k_M27=rx_expr_27;</span> +#> <span class='message'>k_M31=rx_expr_28;</span> +#> <span class='message'>k1=rx_expr_24;</span> +#> <span class='message'>k2=rx_expr_25;</span> +#> <span class='message'>g=1/(rx_expr_35);</span> +#> <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[8]+THETA[8])));</span> +#> <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[9]+THETA[9])));</span> +#> <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[10]+THETA[10])));</span> +#> <span class='message'>f_DMTA_to_M23=f_DMTA_tffm0_1;</span> +#> <span class='message'>f_DMTA_to_M27=(rx_expr_21)*f_DMTA_tffm0_2;</span> +#> <span class='message'>f_DMTA_to_M31=(rx_expr_22)*(rx_expr_21)*f_DMTA_tffm0_3;</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_DMTA_tffm0_1" "f_DMTA_tffm0_2" "f_DMTA_tffm0_3" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 121.4 8.294 129.7</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 121.5 8.294 129.9</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in summary(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in plot(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found</span></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> <span class='va'>f_dmta_saemix</span> <span class='op'><-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:06:56 2021" +#> [1] "Tue Oct 5 16:58:50 2021" #> .... #> Minimisation finished -#> [1] "Thu Sep 16 14:25:28 2021"</div><div class='output co'>#> user system elapsed -#> 1176.278 0.021 1176.388 </div><div class='input'> +#> [1] "Tue Oct 5 17:17:24 2021"</div><div class='output co'>#> user system elapsed +#> 1181.365 0.031 1181.470 </div><div class='input'> <span class='co'># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span> <span class='co'># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span> <span class='co'># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span> @@ -498,174 +415,10 @@ specific pieces of information in the comments.</p> <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: 98.3400 -3.5096 -3.3392 -3.7596 -2.2055 -2.7755 1.0281 -2.7872 -2.7223 -2.8341 2.6422 0.7027 0.8124 0.7085 0.8560 1.4980 3.2777 0.3063 0.2850 0.2850 4.1120 0.3716 4.4582 0.3994 4.4820 0.4025 3.7803 0.5780 -#> 500: 97.8212 -4.4030 -4.0872 -4.1289 -2.8278 -4.3505 2.6614 -2.1252 -2.1308 -2.0749 2.9463 1.2933 0.2802 0.3467 0.4814 0.7877 3.0743 0.1508 0.1523 0.3155 0.9557 0.0333 0.4787 0.1073 0.6826 0.0707 0.7849 0.0356</div><div class='output co'>#> <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 -#> 800.784 3.715 149.687 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='warning'>Warning: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='error'>Error in eval(substitute(expr), data, enclos = parent.frame()): Cannot run SAEM since some of the parameters are not mu-referenced (eta.f_DMTA_tffm0_1_qlogis, eta.f_DMTA_tffm0_2_qlogis, eta.f_DMTA_tffm0_3_qlogis)</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.849 0.016 0.864</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.041 0.016 1.058</span></div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> </div><div class='output co'>#> <span class='error'>Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> -</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 -#> mkin version used for pre-fitting: 1.1.0 -#> R version used for fitting: 4.1.1 -#> Date of fit: Thu Sep 16 14:29:02 2021 -#> Date of summary: Thu Sep 16 14:29:02 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: -#> 563 observations of 4 variable(s) grouped in 6 datasets -#> -#> Degradation model predictions using RxODE -#> -#> Fitted in 149.421 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 -#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 -#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7554 -2.2352 -3.7758 0.4363 -#> -#> Mean of starting values for error model parameters: -#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.70012 0.02577 0.70012 0.02577 0.70012 -#> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.02577 0.70012 0.02577 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood calculated by focei -#> AIC BIC logLik -#> 1953 2074 -948.3 -#> -#> Optimised parameters: -#> est. lower upper -#> DMTA_0 97.821 95.862 99.780 -#> log_k_M23 -4.403 -5.376 -3.430 -#> log_k_M27 -4.087 -4.545 -3.629 -#> log_k_M31 -4.129 -4.639 -3.618 -#> log_k1 -2.828 -3.389 -2.266 -#> log_k2 -4.351 -5.472 -3.229 -#> g_qlogis 2.661 0.824 4.499 -#> f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801 -#> f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794 -#> f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610 -#> -#> Correlation: -#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs -#> log_k_M23 -0.019 -#> log_k_M27 -0.028 0.004 -#> log_k_M31 -0.019 0.003 0.075 -#> log_k1 0.038 -0.004 -0.006 -0.003 -#> log_k2 0.046 0.011 0.008 0.009 0.068 -#> g_qlogis -0.067 0.004 0.006 0.001 -0.076 -0.409 -#> f_DMTA_tffm0_1_qlogis -0.062 0.055 0.006 0.004 -0.008 -0.004 0.012 -#> f_DMTA_tffm0_2_qlogis -0.062 0.010 0.058 -0.034 -0.008 -0.007 0.014 -#> f_DMTA_tffm0_3_qlogis -0.052 0.009 0.056 0.071 -0.006 -0.001 0.008 -#> 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 0.017 -#> f_DMTA_tffm0_3_qlogis 0.014 -0.005 -#> -#> Random effects (omega): -#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 2.946 0.000 0.0000 0.0000 -#> eta.log_k_M23 0.000 1.293 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.000 0.2802 0.0000 -#> eta.log_k_M31 0.000 0.000 0.0000 0.3467 -#> 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 0.4814 0.0000 0.000 -#> eta.log_k2 0.0000 0.7877 0.000 -#> eta.g_qlogis 0.0000 0.0000 3.074 -#> 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 0.1508 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1523 -#> 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 0.3155 -#> -#> Variance model: -#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.95572 0.03325 0.47871 0.10733 0.68264 -#> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.07072 0.78486 0.03557 -#> -#> Backtransformed parameters: -#> est. lower upper -#> DMTA_0 97.82122 95.862233 99.78020 -#> k_M23 0.01224 0.004625 0.03239 -#> k_M27 0.01679 0.010615 0.02654 -#> k_M31 0.01610 0.009664 0.02683 -#> f_DMTA_to_M23 0.10668 NA NA -#> f_DMTA_to_M27 0.09481 NA NA -#> f_DMTA_to_M31 0.08908 NA NA -#> k1 0.05914 0.033731 0.10370 -#> k2 0.01290 0.004204 0.03958 -#> g 0.93471 0.695081 0.98900 -#> -#> Resulting formation fractions: -#> ff -#> DMTA_M23 0.10668 -#> DMTA_M27 0.09481 -#> DMTA_M31 0.08908 -#> DMTA_sink 0.70943 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 12.57 45.43 13.67 11.72 53.73 -#> M23 56.63 188.11 NA NA NA -#> M27 41.29 137.18 NA NA NA -#> M31 43.05 143.01 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> -</div><div class='img'><img src='dimethenamid_2018-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> +</div><div class='output co'>#> <span class='error'>Error in summary(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in plot(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found</span></div><div class='input'><span class='co'># }</span> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/mixed.html b/docs/dev/reference/mixed.html index 338480ee..4c849671 100644 --- a/docs/dev/reference/mixed.html +++ b/docs/dev/reference/mixed.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 6478cda4..60a0af96 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -344,7 +344,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> parent <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, full_name <span class='op'>=</span> <span class='st'>"Test compound"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, full_name <span class='op'>=</span> <span class='st'>"Metabolite M1"</span><span class='op'>)</span>, name <span class='op'>=</span> <span class='st'>"SFO_SFO"</span>, dll_dir <span class='op'>=</span> <span class='va'>DLL_dir</span>, unload <span class='op'>=</span> <span class='cn'>TRUE</span>, overwrite <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/RtmpTzKqT5/file7993c266a7f90.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> +</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/RtmpWsX48Z/file1029f864aada00.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> <span class='fu'><a href='https://rdrr.io/r/base/readRDS.html'>saveRDS</a></span><span class='op'>(</span><span class='va'>SFO_SFO.2</span>, file <span class='op'>=</span> <span class='st'>"~/SFO_SFO.rds"</span><span class='op'>)</span> <span class='co'># Terminate the R session here if you would like to check, and then do</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://pkgdown.jrwb.de/mkin/'>mkin</a></span><span class='op'>)</span> @@ -393,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x555559365690></div><div class='input'> +#> <environment: 0x55555cc38878></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 e4bc2fde..365aaef0 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 31910ce4..40841404 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 500ac391..5c88b69d 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -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 -#> 289.8295 299.4886 -139.9148 +#> 300.6824 310.2426 -145.3412 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 1.839278 0.6988919 3.059894 +#> StdDev: 1.697361 0.6801209 3.666073 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 100.52780 1.3507449 47 74.42397 0 -#> log_k_parent_sink -3.08477 0.4124053 47 -7.47995 0 +#> parent_0 100.99378 1.3890416 46 72.70753 0 +#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.019 +#> log_k_parent_sink 0.027 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -2.22350411 -0.51546184 0.04803417 0.55987705 3.49178405 +#> Min Q1 Med Q3 Max +#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 #> -#> Number of Observations: 51 +#> Number of Observations: 50 #> 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/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html index 61f5ac07..27b5ed0f 100644 --- a/docs/dev/reference/nlmixr.mmkin.html +++ b/docs/dev/reference/nlmixr.mmkin.html @@ -351,12 +351,12 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlmixr_fomc_saem_tc</span><span class='op'>)</span> </div><div class='img'><img src='nlmixr.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='va'>sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fomc_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>dfop_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> + A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='va'>fomc_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, + A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='va'>dfop_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, + A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> + <span class='va'>f_mmkin_const</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'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>sfo_sfo</span>, <span class='st'>"FOMC-SFO"</span> <span class='op'>=</span> <span class='va'>fomc_sfo</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'>"const"</span><span class='op'>)</span> @@ -367,497 +367,9226 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. <span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>sfo_sfo</span>, <span class='st'>"FOMC-SFO"</span> <span class='op'>=</span> <span class='va'>fomc_sfo</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'>"tc"</span><span class='op'>)</span> +<span class='fu'>nlmixr_model</span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>Constant variance for more than one variable is not supported for est = 'saem'</span> +#> <span class='message'>Changing the error model to 'obs' (variance by observed variable)</span></div><div class='output co'>#> function () +#> { +#> ini({ +#> parent_0 = 86.5344031312703 +#> eta.parent_0 ~ 4.15825368312402 +#> log_k_parent = -3.20701116105339 +#> eta.log_k_parent ~ 1.51881531595261 +#> log_k_A1 = -4.56730447776105 +#> eta.log_k_A1 ~ 0.560590264281928 +#> f_parent_qlogis = -0.334081143921924 +#> eta.f_parent_qlogis ~ 1.14983591785967 +#> sigma_parent = 4.31472323222676 +#> sigma_A1 = 4.31472323222676 +#> }) +#> model({ +#> parent_0_model = parent_0 + eta.parent_0 +#> parent(0) = parent_0_model +#> k_parent = exp(log_k_parent + eta.log_k_parent) +#> k_A1 = exp(log_k_A1 + eta.log_k_A1) +#> f_parent_to_A1 = expit(f_parent_qlogis + eta.f_parent_qlogis) +#> d/dt(parent) = -k_parent * parent +#> d/dt(A1) = +f_parent_to_A1 * k_parent * parent - k_A1 * +#> A1 +#> parent ~ add(sigma_parent) +#> A1 ~ add(sigma_A1) +#> }) +#> } +#> <environment: 0x5555669693c0></div><div class='input'> <span class='co'># A single constant variance is currently only possible with est = 'focei' in nlmixr</span> <span class='va'>f_nlmixr_sfo_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'>"SFO-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> -#> <span class='message'>parent(0)=rx_expr_6;</span> -#> <span class='message'>rx_expr_7~ETA[2]+THETA[2];</span> -#> <span class='message'>rx_expr_10~exp(rx_expr_7);</span> -#> <span class='message'>d/dt(parent)=-rx_expr_10*parent;</span> -#> <span class='message'>rx_expr_8~ETA[3]+THETA[3];</span> -#> <span class='message'>rx_expr_11~exp(rx_expr_8);</span> -#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_10*parent*f_parent_to_A1;</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_13~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_13+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_13+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_9~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_14~rx_expr_9*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_14)*(rx_expr_0)+(rx_expr_4+rx_expr_14)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_expr_12~Rx_pow_di(THETA[5],2);</span> -#> <span class='message'>rx_r_=(rx_expr_0)*rx_expr_12+(rx_expr_2)*(rx_expr_1)*rx_expr_12;</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_parent=THETA[2];</span> -#> <span class='message'>log_k_A1=THETA[3];</span> -#> <span class='message'>f_parent_qlogis=THETA[4];</span> -#> <span class='message'>sigma=THETA[5];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_parent=ETA[2];</span> -#> <span class='message'>eta.log_k_A1=ETA[3];</span> -#> <span class='message'>eta.f_parent_qlogis=ETA[4];</span> -#> <span class='message'>parent_0_model=rx_expr_6;</span> -#> <span class='message'>k_parent=rx_expr_10;</span> -#> <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: 5.549 0.41 5.959</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> -#> <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[5]+THETA[5];</span> -#> <span class='message'>rx_expr_13~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_15~t*rx_expr_13;</span> -#> <span class='message'>rx_expr_16~1+rx_expr_15;</span> -#> <span class='message'>rx_expr_18~rx_expr_7-(rx_expr_8);</span> -#> <span class='message'>rx_expr_20~exp(rx_expr_18);</span> -#> <span class='message'>d/dt(parent)=-rx_expr_20*parent/(rx_expr_16);</span> -#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> -#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> -#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_20*parent*f_parent_to_A1/(rx_expr_16);</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_14~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_14+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_14+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_17~rx_expr_10*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_17)*(rx_expr_0)+(rx_expr_4+rx_expr_17)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_expr_12~Rx_pow_di(THETA[6],2);</span> -#> <span class='message'>rx_r_=(rx_expr_0)*rx_expr_12+(rx_expr_2)*(rx_expr_1)*rx_expr_12;</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=THETA[2];</span> -#> <span class='message'>f_parent_qlogis=THETA[3];</span> -#> <span class='message'>log_alpha=THETA[4];</span> -#> <span class='message'>log_beta=THETA[5];</span> -#> <span class='message'>sigma=THETA[6];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=ETA[2];</span> -#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> -#> <span class='message'>eta.log_alpha=ETA[4];</span> -#> <span class='message'>eta.log_beta=ETA[5];</span> -#> <span class='message'>parent_0_model=rx_expr_6;</span> -#> <span class='message'>k_A1=rx_expr_11;</span> -#> <span class='message'>alpha=exp(rx_expr_7);</span> -#> <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: 6.93 0.367 7.293</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> -#> <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_18~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_20~1+rx_expr_18;</span> -#> <span class='message'>rx_expr_25~1/(rx_expr_20);</span> -#> <span class='message'>rx_expr_27~(rx_expr_25);</span> -#> <span class='message'>rx_expr_28~1-rx_expr_27;</span> -#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_20)+exp(rx_expr_9-rx_expr_16)*(rx_expr_28))/(exp(-t*rx_expr_12)/(rx_expr_20)+exp(-t*rx_expr_13)*(rx_expr_28));</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(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_20)+exp(rx_expr_9-rx_expr_16)*(rx_expr_28))/(exp(-t*rx_expr_12)/(rx_expr_20)+exp(-t*rx_expr_13)*(rx_expr_28));</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_19~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_19+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_19+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_23~rx_expr_11*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_23)*(rx_expr_0)+(rx_expr_4+rx_expr_23)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_expr_17~Rx_pow_di(THETA[7],2);</span> -#> <span class='message'>rx_r_=(rx_expr_0)*rx_expr_17+(rx_expr_2)*(rx_expr_1)*rx_expr_17;</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=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=THETA[7];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=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_A1=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_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: 15.39 1.223 16.61</span></div><div class='input'> +</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_parent | log_k_A1 |f_parent_qlogis | +#> |.....................| sigma | o1 | o2 | o3 | +#> <span style='text-decoration: underline;'>|.....................| o4 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 756.06625 | 1.000 | -0.9701 | -1.000 | -0.9071 | +#> |.....................| -0.8050 | -0.8844 | -0.8800 | -0.8744 | +#> <span style='text-decoration: underline;'>|.....................| -0.8785 |...........|...........|...........|</span> +#> | U| 756.06625 | 86.53 | -3.207 | -4.567 | -0.3341 | +#> |.....................| 4.315 | 0.7003 | 0.9008 | 1.156 | +#> <span style='text-decoration: underline;'>|.....................| 0.9657 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 756.06625</span> | 86.53 | 0.04048 | 0.01039 | 0.4172 | +#> |.....................| 4.315 | 0.7003 | 0.9008 | 1.156 | +#> <span style='text-decoration: underline;'>|.....................| 0.9657 |...........|...........|...........|</span> +#> | G| Gill Diff. | 59.54 | 0.01874 | 0.7243 | 0.3705 | +#> |.....................| -28.18 | 5.148 | 2.958 | -8.197 | +#> <span style='text-decoration: underline;'>|.....................| -5.917 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 3309.1113 | 0.1102 | -0.9704 | -1.011 | -0.9126 | +#> |.....................| -0.3838 | -0.9613 | -0.9242 | -0.7519 | +#> <span style='text-decoration: underline;'>|.....................| -0.7901 |...........|...........|...........|</span> +#> | U| 3309.1113 | 9.535 | -3.207 | -4.578 | -0.3359 | +#> |.....................| 5.223 | 0.6464 | 0.8610 | 1.297 | +#> <span style='text-decoration: underline;'>|.....................| 1.051 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 3309.1113</span> | 9.535 | 0.04047 | 0.01027 | 0.4168 | +#> |.....................| 5.223 | 0.6464 | 0.8610 | 1.297 | +#> <span style='text-decoration: underline;'>|.....................| 1.051 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 782.04188 | 0.9110 | -0.9702 | -1.001 | -0.9076 | +#> |.....................| -0.7629 | -0.8921 | -0.8844 | -0.8621 | +#> <span style='text-decoration: underline;'>|.....................| -0.8697 |...........|...........|...........|</span> +#> | U| 782.04188 | 78.83 | -3.207 | -4.568 | -0.3343 | +#> |.....................| 4.406 | 0.6949 | 0.8968 | 1.170 | +#> <span style='text-decoration: underline;'>|.....................| 0.9742 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 782.04188</span> | 78.83 | 0.04048 | 0.01037 | 0.4172 | +#> |.....................| 4.406 | 0.6949 | 0.8968 | 1.170 | +#> <span style='text-decoration: underline;'>|.....................| 0.9742 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 755.73406 | 0.9909 | -0.9701 | -1.000 | -0.9071 | +#> |.....................| -0.8007 | -0.8851 | -0.8804 | -0.8731 | +#> <span style='text-decoration: underline;'>|.....................| -0.8776 |...........|...........|...........|</span> +#> | U| 755.73406 | 85.75 | -3.207 | -4.567 | -0.3341 | +#> |.....................| 4.324 | 0.6997 | 0.9004 | 1.157 | +#> <span style='text-decoration: underline;'>|.....................| 0.9666 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 755.73406</span> | 85.75 | 0.04048 | 0.01038 | 0.4172 | +#> |.....................| 4.324 | 0.6997 | 0.9004 | 1.157 | +#> <span style='text-decoration: underline;'>|.....................| 0.9666 |...........|...........|...........|</span> +#> | F| Forward Diff. | -16.83 | 0.07808 | 0.6495 | 0.3224 | +#> |.....................| -27.54 | 3.811 | 2.903 | -8.359 | +#> <span style='text-decoration: underline;'>|.....................| -5.718 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 755.49648 | 0.9959 | -0.9702 | -1.000 | -0.9072 | +#> |.....................| -0.7924 | -0.8863 | -0.8813 | -0.8706 | +#> <span style='text-decoration: underline;'>|.....................| -0.8759 |...........|...........|...........|</span> +#> | U| 755.49648 | 86.18 | -3.207 | -4.568 | -0.3341 | +#> |.....................| 4.342 | 0.6989 | 0.8996 | 1.160 | +#> <span style='text-decoration: underline;'>|.....................| 0.9682 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 755.49648</span> | 86.18 | 0.04048 | 0.01038 | 0.4172 | +#> |.....................| 4.342 | 0.6989 | 0.8996 | 1.160 | +#> <span style='text-decoration: underline;'>|.....................| 0.9682 |...........|...........|...........|</span> +#> | F| Forward Diff. | 25.35 | 0.04484 | 0.6934 | 0.3535 | +#> |.....................| -25.80 | 4.244 | 2.831 | -8.249 | +#> <span style='text-decoration: underline;'>|.....................| -5.719 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 755.31010 | 0.9891 | -0.9702 | -1.000 | -0.9073 | +#> |.....................| -0.7855 | -0.8874 | -0.8820 | -0.8684 | +#> <span style='text-decoration: underline;'>|.....................| -0.8744 |...........|...........|...........|</span> +#> | U| 755.3101 | 85.59 | -3.207 | -4.568 | -0.3342 | +#> |.....................| 4.357 | 0.6981 | 0.8989 | 1.163 | +#> <span style='text-decoration: underline;'>|.....................| 0.9697 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 755.3101</span> | 85.59 | 0.04048 | 0.01038 | 0.4172 | +#> |.....................| 4.357 | 0.6981 | 0.8989 | 1.163 | +#> <span style='text-decoration: underline;'>|.....................| 0.9697 |...........|...........|...........|</span> +#> | F| Forward Diff. | -31.39 | 0.08909 | 0.6380 | 0.3185 | +#> |.....................| -24.71 | 3.519 | 2.751 | -7.972 | +#> <span style='text-decoration: underline;'>|.....................| -5.525 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 755.09582 | 0.9961 | -0.9702 | -1.001 | -0.9074 | +#> |.....................| -0.7787 | -0.8884 | -0.8828 | -0.8661 | +#> <span style='text-decoration: underline;'>|.....................| -0.8728 |...........|...........|...........|</span> +#> | U| 755.09582 | 86.20 | -3.207 | -4.568 | -0.3342 | +#> |.....................| 4.372 | 0.6974 | 0.8982 | 1.165 | +#> <span style='text-decoration: underline;'>|.....................| 0.9712 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 755.09582</span> | 86.20 | 0.04047 | 0.01038 | 0.4172 | +#> |.....................| 4.372 | 0.6974 | 0.8982 | 1.165 | +#> <span style='text-decoration: underline;'>|.....................| 0.9712 |...........|...........|...........|</span> +#> | F| Forward Diff. | 26.63 | 0.04269 | 0.6973 | 0.3604 | +#> |.....................| -23.22 | 4.086 | 2.689 | -8.043 | +#> <span style='text-decoration: underline;'>|.....................| -5.569 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 754.90743 | 0.9894 | -0.9702 | -1.001 | -0.9075 | +#> |.....................| -0.7716 | -0.8897 | -0.8836 | -0.8636 | +#> <span style='text-decoration: underline;'>|.....................| -0.8711 |...........|...........|...........|</span> +#> | U| 754.90743 | 85.62 | -3.207 | -4.568 | -0.3342 | +#> |.....................| 4.387 | 0.6966 | 0.8975 | 1.168 | +#> <span style='text-decoration: underline;'>|.....................| 0.9729 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 754.90743</span> | 85.62 | 0.04047 | 0.01038 | 0.4172 | +#> |.....................| 4.387 | 0.6966 | 0.8975 | 1.168 | +#> <span style='text-decoration: underline;'>|.....................| 0.9729 |...........|...........|...........|</span> +#> | F| Forward Diff. | -27.88 | 0.08581 | 0.6437 | 0.3265 | +#> |.....................| -22.15 | 3.354 | 2.606 | -7.748 | +#> <span style='text-decoration: underline;'>|.....................| -5.369 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 754.70769 | 0.9959 | -0.9702 | -1.001 | -0.9076 | +#> |.....................| -0.7645 | -0.8908 | -0.8845 | -0.8610 | +#> <span style='text-decoration: underline;'>|.....................| -0.8693 |...........|...........|...........|</span> +#> | U| 754.70769 | 86.18 | -3.207 | -4.568 | -0.3343 | +#> |.....................| 4.402 | 0.6958 | 0.8967 | 1.171 | +#> <span style='text-decoration: underline;'>|.....................| 0.9747 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 754.70769</span> | 86.18 | 0.04047 | 0.01037 | 0.4172 | +#> |.....................| 4.402 | 0.6958 | 0.8967 | 1.171 | +#> <span style='text-decoration: underline;'>|.....................| 0.9747 |...........|...........|...........|</span> +#> | F| Forward Diff. | 25.01 | 0.04305 | 0.6984 | 0.3661 | +#> |.....................| -20.67 | 3.871 | 2.535 | -7.809 | +#> <span style='text-decoration: underline;'>|.....................| -5.388 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 754.52507 | 0.9898 | -0.9703 | -1.001 | -0.9078 | +#> |.....................| -0.7574 | -0.8922 | -0.8854 | -0.8580 | +#> <span style='text-decoration: underline;'>|.....................| -0.8672 |...........|...........|...........|</span> +#> | U| 754.52507 | 85.65 | -3.207 | -4.569 | -0.3343 | +#> |.....................| 4.417 | 0.6948 | 0.8958 | 1.175 | +#> <span style='text-decoration: underline;'>|.....................| 0.9766 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 754.52507</span> | 85.65 | 0.04047 | 0.01037 | 0.4172 | +#> |.....................| 4.417 | 0.6948 | 0.8958 | 1.175 | +#> <span style='text-decoration: underline;'>|.....................| 0.9766 |...........|...........|...........|</span> +#> | F| Forward Diff. | -24.90 | 0.08308 | 0.6490 | 0.3352 | +#> |.....................| -19.59 | 3.181 | 2.445 | -7.663 | +#> <span style='text-decoration: underline;'>|.....................| -5.179 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 754.34076 | 0.9957 | -0.9703 | -1.002 | -0.9079 | +#> |.....................| -0.7502 | -0.8935 | -0.8864 | -0.8548 | +#> <span style='text-decoration: underline;'>|.....................| -0.8650 |...........|...........|...........|</span> +#> | U| 754.34076 | 86.16 | -3.207 | -4.569 | -0.3344 | +#> |.....................| 4.433 | 0.6939 | 0.8950 | 1.178 | +#> <span style='text-decoration: underline;'>|.....................| 0.9787 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 754.34076</span> | 86.16 | 0.04047 | 0.01037 | 0.4172 | +#> |.....................| 4.433 | 0.6939 | 0.8950 | 1.178 | +#> <span style='text-decoration: underline;'>|.....................| 0.9787 |...........|...........|...........|</span> +#> | F| Forward Diff. | 23.15 | 0.04366 | 0.6990 | 0.3728 | +#> |.....................| -18.16 | 3.647 | 2.362 | -7.534 | +#> <span style='text-decoration: underline;'>|.....................| -5.170 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 754.16941 | 0.9900 | -0.9703 | -1.002 | -0.9081 | +#> |.....................| -0.7432 | -0.8951 | -0.8875 | -0.8512 | +#> <span style='text-decoration: underline;'>|.....................| -0.8626 |...........|...........|...........|</span> +#> | U| 754.16941 | 85.67 | -3.207 | -4.569 | -0.3344 | +#> |.....................| 4.448 | 0.6928 | 0.8940 | 1.182 | +#> <span style='text-decoration: underline;'>|.....................| 0.9811 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 754.16941</span> | 85.67 | 0.04047 | 0.01036 | 0.4172 | +#> |.....................| 4.448 | 0.6928 | 0.8940 | 1.182 | +#> <span style='text-decoration: underline;'>|.....................| 0.9811 |...........|...........|...........|</span> +#> | F| Forward Diff. | -22.36 | 0.07996 | 0.6524 | 0.3446 | +#> |.....................| -17.12 | 3.002 | 2.262 | -7.362 | +#> <span style='text-decoration: underline;'>|.....................| -4.949 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 754.00081 | 0.9955 | -0.9704 | -1.002 | -0.9083 | +#> |.....................| -0.7363 | -0.8967 | -0.8886 | -0.8472 | +#> <span style='text-decoration: underline;'>|.....................| -0.8599 |...........|...........|...........|</span> +#> | U| 754.00081 | 86.14 | -3.207 | -4.570 | -0.3345 | +#> |.....................| 4.463 | 0.6916 | 0.8930 | 1.187 | +#> <span style='text-decoration: underline;'>|.....................| 0.9836 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 754.00081</span> | 86.14 | 0.04047 | 0.01036 | 0.4171 | +#> |.....................| 4.463 | 0.6916 | 0.8930 | 1.187 | +#> <span style='text-decoration: underline;'>|.....................| 0.9836 |...........|...........|...........|</span> +#> | F| Forward Diff. | 21.00 | 0.04440 | 0.6979 | 0.3804 | +#> |.....................| -15.79 | 3.414 | 2.168 | -7.205 | +#> <span style='text-decoration: underline;'>|.....................| -4.903 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 753.84435 | 0.9903 | -0.9704 | -1.003 | -0.9086 | +#> |.....................| -0.7296 | -0.8985 | -0.8898 | -0.8427 | +#> <span style='text-decoration: underline;'>|.....................| -0.8570 |...........|...........|...........|</span> +#> | U| 753.84435 | 85.70 | -3.207 | -4.570 | -0.3346 | +#> |.....................| 4.477 | 0.6903 | 0.8919 | 1.192 | +#> <span style='text-decoration: underline;'>|.....................| 0.9865 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.84435</span> | 85.70 | 0.04047 | 0.01036 | 0.4171 | +#> |.....................| 4.477 | 0.6903 | 0.8919 | 1.192 | +#> <span style='text-decoration: underline;'>|.....................| 0.9865 |...........|...........|...........|</span> +#> | F| Forward Diff. | -19.93 | 0.07681 | 0.6538 | 0.3555 | +#> |.....................| -14.84 | 2.820 | 2.056 | -6.999 | +#> <span style='text-decoration: underline;'>|.....................| -4.662 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 753.69372 | 0.9952 | -0.9704 | -1.003 | -0.9089 | +#> |.....................| -0.7234 | -0.9005 | -0.8911 | -0.8377 | +#> <span style='text-decoration: underline;'>|.....................| -0.8537 |...........|...........|...........|</span> +#> | U| 753.69372 | 86.12 | -3.207 | -4.571 | -0.3347 | +#> |.....................| 4.491 | 0.6890 | 0.8908 | 1.198 | +#> <span style='text-decoration: underline;'>|.....................| 0.9897 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.69372</span> | 86.12 | 0.04046 | 0.01035 | 0.4171 | +#> |.....................| 4.491 | 0.6890 | 0.8908 | 1.198 | +#> <span style='text-decoration: underline;'>|.....................| 0.9897 |...........|...........|...........|</span> +#> | F| Forward Diff. | 18.81 | 0.04462 | 0.6942 | 0.3896 | +#> |.....................| -13.66 | 3.180 | 1.953 | -6.807 | +#> <span style='text-decoration: underline;'>|.....................| -4.573 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 753.55534 | 0.9906 | -0.9705 | -1.004 | -0.9093 | +#> |.....................| -0.7176 | -0.9027 | -0.8924 | -0.8322 | +#> <span style='text-decoration: underline;'>|.....................| -0.8502 |...........|...........|...........|</span> +#> | U| 753.55534 | 85.72 | -3.207 | -4.571 | -0.3348 | +#> |.....................| 4.503 | 0.6875 | 0.8896 | 1.204 | +#> <span style='text-decoration: underline;'>|.....................| 0.9931 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.55534</span> | 85.72 | 0.04046 | 0.01034 | 0.4171 | +#> |.....................| 4.503 | 0.6875 | 0.8896 | 1.204 | +#> <span style='text-decoration: underline;'>|.....................| 0.9931 |...........|...........|...........|</span> +#> | F| Forward Diff. | -17.61 | 0.07313 | 0.6517 | 0.3679 | +#> |.....................| -12.86 | 2.639 | 1.835 | -6.564 | +#> <span style='text-decoration: underline;'>|.....................| -4.309 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 753.42478 | 0.9950 | -0.9706 | -1.005 | -0.9097 | +#> |.....................| -0.7124 | -0.9049 | -0.8937 | -0.8262 | +#> <span style='text-decoration: underline;'>|.....................| -0.8464 |...........|...........|...........|</span> +#> | U| 753.42478 | 86.11 | -3.207 | -4.572 | -0.3350 | +#> |.....................| 4.515 | 0.6859 | 0.8884 | 1.211 | +#> <span style='text-decoration: underline;'>|.....................| 0.9967 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.42478</span> | 86.11 | 0.04046 | 0.01034 | 0.4170 | +#> |.....................| 4.515 | 0.6859 | 0.8884 | 1.211 | +#> <span style='text-decoration: underline;'>|.....................| 0.9967 |...........|...........|...........|</span> +#> | F| Forward Diff. | 16.74 | 0.04433 | 0.6853 | 0.4002 | +#> |.....................| -11.89 | 2.952 | 1.729 | -6.336 | +#> <span style='text-decoration: underline;'>|.....................| -4.181 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 18</span>| 753.30602 | 0.9909 | -0.9706 | -1.006 | -0.9103 | +#> |.....................| -0.7078 | -0.9075 | -0.8949 | -0.8197 | +#> <span style='text-decoration: underline;'>|.....................| -0.8425 |...........|...........|...........|</span> +#> | U| 753.30602 | 85.74 | -3.207 | -4.573 | -0.3352 | +#> |.....................| 4.525 | 0.6841 | 0.8873 | 1.219 | +#> <span style='text-decoration: underline;'>|.....................| 1.001 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.30602</span> | 85.74 | 0.04046 | 0.01033 | 0.4170 | +#> |.....................| 4.525 | 0.6841 | 0.8873 | 1.219 | +#> <span style='text-decoration: underline;'>|.....................| 1.001 |...........|...........|...........|</span> +#> | F| Forward Diff. | -15.54 | 0.06924 | 0.6430 | 0.3812 | +#> |.....................| -11.26 | 2.462 | 1.618 | -6.066 | +#> <span style='text-decoration: underline;'>|.....................| -3.903 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 19</span>| 753.19508 | 0.9949 | -0.9707 | -1.007 | -0.9109 | +#> |.....................| -0.7036 | -0.9102 | -0.8961 | -0.8129 | +#> <span style='text-decoration: underline;'>|.....................| -0.8385 |...........|...........|...........|</span> +#> | U| 753.19508 | 86.09 | -3.208 | -4.574 | -0.3354 | +#> |.....................| 4.533 | 0.6822 | 0.8862 | 1.227 | +#> <span style='text-decoration: underline;'>|.....................| 1.004 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.19508</span> | 86.09 | 0.04045 | 0.01032 | 0.4169 | +#> |.....................| 4.533 | 0.6822 | 0.8862 | 1.227 | +#> <span style='text-decoration: underline;'>|.....................| 1.004 |...........|...........|...........|</span> +#> | F| Forward Diff. | 14.90 | 0.04352 | 0.6689 | 0.4113 | +#> |.....................| -10.49 | 2.732 | 1.522 | -5.813 | +#> <span style='text-decoration: underline;'>|.....................| -3.751 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 20</span>| 753.09443 | 0.9911 | -0.9708 | -1.008 | -0.9117 | +#> |.....................| -0.7001 | -0.9132 | -0.8972 | -0.8058 | +#> <span style='text-decoration: underline;'>|.....................| -0.8346 |...........|...........|...........|</span> +#> | U| 753.09443 | 85.77 | -3.208 | -4.575 | -0.3356 | +#> |.....................| 4.541 | 0.6801 | 0.8852 | 1.235 | +#> <span style='text-decoration: underline;'>|.....................| 1.008 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.09443</span> | 85.77 | 0.04045 | 0.01031 | 0.4169 | +#> |.....................| 4.541 | 0.6801 | 0.8852 | 1.235 | +#> <span style='text-decoration: underline;'>|.....................| 1.008 |...........|...........|...........|</span> +#> | F| Forward Diff. | -13.80 | 0.06521 | 0.6240 | 0.3942 | +#> |.....................| -10.02 | 2.285 | 1.423 | -5.526 | +#> <span style='text-decoration: underline;'>|.....................| -3.476 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 21</span>| 753.00021 | 0.9948 | -0.9709 | -1.009 | -0.9127 | +#> |.....................| -0.6968 | -0.9163 | -0.8982 | -0.7985 | +#> <span style='text-decoration: underline;'>|.....................| -0.8307 |...........|...........|...........|</span> +#> | U| 753.00021 | 86.08 | -3.208 | -4.576 | -0.3360 | +#> |.....................| 4.548 | 0.6779 | 0.8843 | 1.243 | +#> <span style='text-decoration: underline;'>|.....................| 1.012 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 753.00021</span> | 86.08 | 0.04045 | 0.01029 | 0.4168 | +#> |.....................| 4.548 | 0.6779 | 0.8843 | 1.243 | +#> <span style='text-decoration: underline;'>|.....................| 1.012 |...........|...........|...........|</span> +#> | F| Forward Diff. | 13.31 | 0.04216 | 0.6406 | 0.4217 | +#> |.....................| -9.402 | 2.517 | 1.347 | -5.262 | +#> <span style='text-decoration: underline;'>|.....................| -3.321 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 22</span>| 752.91432 | 0.9914 | -0.9710 | -1.010 | -0.9139 | +#> |.....................| -0.6939 | -0.9197 | -0.8991 | -0.7911 | +#> <span style='text-decoration: underline;'>|.....................| -0.8272 |...........|...........|...........|</span> +#> | U| 752.91432 | 85.79 | -3.208 | -4.578 | -0.3364 | +#> |.....................| 4.555 | 0.6755 | 0.8835 | 1.252 | +#> <span style='text-decoration: underline;'>|.....................| 1.015 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.91432</span> | 85.79 | 0.04044 | 0.01028 | 0.4167 | +#> |.....................| 4.555 | 0.6755 | 0.8835 | 1.252 | +#> <span style='text-decoration: underline;'>|.....................| 1.015 |...........|...........|...........|</span> +#> | F| Forward Diff. | -12.35 | 0.06128 | 0.5909 | 0.4053 | +#> |.....................| -9.027 | 2.101 | 1.271 | -4.717 | +#> <span style='text-decoration: underline;'>|.....................| -3.067 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 23</span>| 752.83200 | 0.9948 | -0.9711 | -1.012 | -0.9155 | +#> |.....................| -0.6906 | -0.9238 | -0.9000 | -0.7843 | +#> <span style='text-decoration: underline;'>|.....................| -0.8235 |...........|...........|...........|</span> +#> | U| 752.832 | 86.09 | -3.208 | -4.580 | -0.3369 | +#> |.....................| 4.561 | 0.6727 | 0.8827 | 1.260 | +#> <span style='text-decoration: underline;'>|.....................| 1.019 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.832</span> | 86.09 | 0.04044 | 0.01026 | 0.4166 | +#> |.....................| 4.561 | 0.6727 | 0.8827 | 1.260 | +#> <span style='text-decoration: underline;'>|.....................| 1.019 |...........|...........|...........|</span> +#> | F| Forward Diff. | 12.74 | 0.03978 | 0.5956 | 0.4312 | +#> |.....................| -8.422 | 2.296 | 1.202 | -4.471 | +#> <span style='text-decoration: underline;'>|.....................| -2.914 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 24</span>| 752.75140 | 0.9918 | -0.9713 | -1.014 | -0.9179 | +#> |.....................| -0.6872 | -0.9288 | -0.9011 | -0.7785 | +#> <span style='text-decoration: underline;'>|.....................| -0.8198 |...........|...........|...........|</span> +#> | U| 752.7514 | 85.82 | -3.208 | -4.582 | -0.3377 | +#> |.....................| 4.569 | 0.6692 | 0.8818 | 1.266 | +#> <span style='text-decoration: underline;'>|.....................| 1.022 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.7514</span> | 85.82 | 0.04043 | 0.01024 | 0.4164 | +#> |.....................| 4.569 | 0.6692 | 0.8818 | 1.266 | +#> <span style='text-decoration: underline;'>|.....................| 1.022 |...........|...........|...........|</span> +#> | F| Forward Diff. | -10.02 | 0.05546 | 0.5361 | 0.4172 | +#> |.....................| -7.958 | 1.872 | 1.117 | -4.424 | +#> <span style='text-decoration: underline;'>|.....................| -2.664 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 25</span>| 752.68235 | 0.9947 | -0.9715 | -1.016 | -0.9205 | +#> |.....................| -0.6845 | -0.9329 | -0.9018 | -0.7712 | +#> <span style='text-decoration: underline;'>|.....................| -0.8173 |...........|...........|...........|</span> +#> | U| 752.68235 | 86.07 | -3.208 | -4.584 | -0.3386 | +#> |.....................| 4.575 | 0.6663 | 0.8811 | 1.275 | +#> <span style='text-decoration: underline;'>|.....................| 1.025 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.68235</span> | 86.07 | 0.04042 | 0.01022 | 0.4162 | +#> |.....................| 4.575 | 0.6663 | 0.8811 | 1.275 | +#> <span style='text-decoration: underline;'>|.....................| 1.025 |...........|...........|...........|</span> +#> | F| Forward Diff. | 10.53 | 0.03715 | 0.5273 | 0.4360 | +#> |.....................| -7.447 | 2.014 | 1.063 | -3.990 | +#> <span style='text-decoration: underline;'>|.....................| -2.556 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 26</span>| 752.62160 | 0.9918 | -0.9717 | -1.019 | -0.9237 | +#> |.....................| -0.6821 | -0.9370 | -0.9025 | -0.7637 | +#> <span style='text-decoration: underline;'>|.....................| -0.8151 |...........|...........|...........|</span> +#> | U| 752.6216 | 85.83 | -3.209 | -4.586 | -0.3397 | +#> |.....................| 4.580 | 0.6635 | 0.8804 | 1.284 | +#> <span style='text-decoration: underline;'>|.....................| 1.027 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.6216</span> | 85.83 | 0.04042 | 0.01020 | 0.4159 | +#> |.....................| 4.580 | 0.6635 | 0.8804 | 1.284 | +#> <span style='text-decoration: underline;'>|.....................| 1.027 |...........|...........|...........|</span> +#> | F| Forward Diff. | -10.27 | 0.05173 | 0.4657 | 0.4178 | +#> |.....................| -7.153 | 1.648 | 1.004 | -3.701 | +#> <span style='text-decoration: underline;'>|.....................| -2.385 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 27</span>| 752.55758 | 0.9944 | -0.9719 | -1.021 | -0.9287 | +#> |.....................| -0.6786 | -0.9418 | -0.9036 | -0.7591 | +#> <span style='text-decoration: underline;'>|.....................| -0.8121 |...........|...........|...........|</span> +#> | U| 752.55758 | 86.05 | -3.209 | -4.588 | -0.3413 | +#> |.....................| 4.587 | 0.6600 | 0.8795 | 1.289 | +#> <span style='text-decoration: underline;'>|.....................| 1.030 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.55758</span> | 86.05 | 0.04040 | 0.01017 | 0.4155 | +#> |.....................| 4.587 | 0.6600 | 0.8795 | 1.289 | +#> <span style='text-decoration: underline;'>|.....................| 1.030 |...........|...........|...........|</span> +#> | F| Forward Diff. | 7.976 | 0.03464 | 0.4539 | 0.4351 | +#> |.....................| -6.545 | 1.728 | 0.9236 | -3.536 | +#> <span style='text-decoration: underline;'>|.....................| -2.257 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 28</span>| 752.50465 | 0.9921 | -0.9722 | -1.023 | -0.9345 | +#> |.....................| -0.6755 | -0.9456 | -0.9043 | -0.7539 | +#> <span style='text-decoration: underline;'>|.....................| -0.8090 |...........|...........|...........|</span> +#> | U| 752.50465 | 85.85 | -3.209 | -4.590 | -0.3432 | +#> |.....................| 4.594 | 0.6574 | 0.8788 | 1.295 | +#> <span style='text-decoration: underline;'>|.....................| 1.033 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.50465</span> | 85.85 | 0.04039 | 0.01015 | 0.4150 | +#> |.....................| 4.594 | 0.6574 | 0.8788 | 1.295 | +#> <span style='text-decoration: underline;'>|.....................| 1.033 |...........|...........|...........|</span> +#> | F| Forward Diff. | -8.947 | 0.04577 | 0.4043 | 0.4205 | +#> |.....................| -6.122 | 1.399 | 0.8644 | -3.339 | +#> <span style='text-decoration: underline;'>|.....................| -2.062 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 29</span>| 752.46010 | 0.9944 | -0.9724 | -1.024 | -0.9405 | +#> |.....................| -0.6742 | -0.9477 | -0.9048 | -0.7467 | +#> <span style='text-decoration: underline;'>|.....................| -0.8068 |...........|...........|...........|</span> +#> | U| 752.4601 | 86.05 | -3.209 | -4.591 | -0.3452 | +#> |.....................| 4.597 | 0.6559 | 0.8784 | 1.303 | +#> <span style='text-decoration: underline;'>|.....................| 1.035 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.4601</span> | 86.05 | 0.04039 | 0.01014 | 0.4145 | +#> |.....................| 4.597 | 0.6559 | 0.8784 | 1.303 | +#> <span style='text-decoration: underline;'>|.....................| 1.035 |...........|...........|...........|</span> +#> | F| Forward Diff. | 6.603 | 0.03134 | 0.3976 | 0.4307 | +#> |.....................| -5.878 | 1.523 | 0.8347 | -3.098 | +#> <span style='text-decoration: underline;'>|.....................| -1.971 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 30</span>| 752.42045 | 0.9923 | -0.9726 | -1.025 | -0.9478 | +#> |.....................| -0.6717 | -0.9497 | -0.9056 | -0.7410 | +#> <span style='text-decoration: underline;'>|.....................| -0.8056 |...........|...........|...........|</span> +#> | U| 752.42045 | 85.87 | -3.210 | -4.593 | -0.3477 | +#> |.....................| 4.602 | 0.6545 | 0.8777 | 1.310 | +#> <span style='text-decoration: underline;'>|.....................| 1.036 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.42045</span> | 85.87 | 0.04038 | 0.01013 | 0.4139 | +#> |.....................| 4.602 | 0.6545 | 0.8777 | 1.310 | +#> <span style='text-decoration: underline;'>|.....................| 1.036 |...........|...........|...........|</span> +#> | F| Forward Diff. | -7.567 | 0.04074 | 0.3551 | 0.4112 | +#> |.....................| -5.553 | 1.278 | 0.7625 | -2.890 | +#> <span style='text-decoration: underline;'>|.....................| -1.881 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 31</span>| 752.38271 | 0.9943 | -0.9729 | -1.026 | -0.9563 | +#> |.....................| -0.6682 | -0.9523 | -0.9058 | -0.7392 | +#> <span style='text-decoration: underline;'>|.....................| -0.8032 |...........|...........|...........|</span> +#> | U| 752.38271 | 86.04 | -3.210 | -4.594 | -0.3505 | +#> |.....................| 4.610 | 0.6527 | 0.8775 | 1.312 | +#> <span style='text-decoration: underline;'>|.....................| 1.038 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.38271</span> | 86.04 | 0.04037 | 0.01012 | 0.4133 | +#> |.....................| 4.610 | 0.6527 | 0.8775 | 1.312 | +#> <span style='text-decoration: underline;'>|.....................| 1.038 |...........|...........|...........|</span> +#> | F| Forward Diff. | 5.602 | 0.02847 | 0.3641 | 0.4189 | +#> |.....................| -5.001 | 1.344 | 0.7516 | -2.828 | +#> <span style='text-decoration: underline;'>|.....................| -1.805 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 32</span>| 752.35435 | 0.9925 | -0.9730 | -1.028 | -0.9633 | +#> |.....................| -0.6679 | -0.9545 | -0.9069 | -0.7341 | +#> <span style='text-decoration: underline;'>|.....................| -0.7988 |...........|...........|...........|</span> +#> | U| 752.35435 | 85.89 | -3.210 | -4.595 | -0.3529 | +#> |.....................| 4.611 | 0.6511 | 0.8766 | 1.318 | +#> <span style='text-decoration: underline;'>|.....................| 1.043 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.35435</span> | 85.89 | 0.04036 | 0.01010 | 0.4127 | +#> |.....................| 4.611 | 0.6511 | 0.8766 | 1.318 | +#> <span style='text-decoration: underline;'>|.....................| 1.043 |...........|...........|...........|</span> +#> | F| Forward Diff. | -6.571 | 0.03612 | 0.3357 | 0.4086 | +#> |.....................| -4.992 | 1.118 | 0.6605 | -2.632 | +#> <span style='text-decoration: underline;'>|.....................| -1.560 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 33</span>| 752.32772 | 0.9943 | -0.9732 | -1.029 | -0.9711 | +#> |.....................| -0.6669 | -0.9557 | -0.9071 | -0.7282 | +#> <span style='text-decoration: underline;'>|.....................| -0.7989 |...........|...........|...........|</span> +#> | U| 752.32772 | 86.04 | -3.210 | -4.596 | -0.3555 | +#> |.....................| 4.613 | 0.6503 | 0.8764 | 1.325 | +#> <span style='text-decoration: underline;'>|.....................| 1.043 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.32772</span> | 86.04 | 0.04035 | 0.01009 | 0.4121 | +#> |.....................| 4.613 | 0.6503 | 0.8764 | 1.325 | +#> <span style='text-decoration: underline;'>|.....................| 1.043 |...........|...........|...........|</span> +#> | F| Forward Diff. | 5.212 | 0.02538 | 0.3153 | 0.4089 | +#> |.....................| -4.808 | 1.231 | 0.6502 | -2.445 | +#> <span style='text-decoration: underline;'>|.....................| -1.583 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 34</span>| 752.30453 | 0.9927 | -0.9733 | -1.030 | -0.9795 | +#> |.....................| -0.6622 | -0.9567 | -0.9058 | -0.7271 | +#> <span style='text-decoration: underline;'>|.....................| -0.8012 |...........|...........|...........|</span> +#> | U| 752.30453 | 85.90 | -3.210 | -4.598 | -0.3583 | +#> |.....................| 4.623 | 0.6496 | 0.8775 | 1.326 | +#> <span style='text-decoration: underline;'>|.....................| 1.040 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.30453</span> | 85.90 | 0.04035 | 0.01008 | 0.4114 | +#> |.....................| 4.623 | 0.6496 | 0.8775 | 1.326 | +#> <span style='text-decoration: underline;'>|.....................| 1.040 |...........|...........|...........|</span> +#> | F| Forward Diff. | -5.777 | 0.03360 | 0.2795 | 0.3849 | +#> |.....................| -4.177 | 1.041 | 0.7583 | -2.411 | +#> <span style='text-decoration: underline;'>|.....................| -1.694 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 35</span>| 752.28211 | 0.9943 | -0.9735 | -1.030 | -0.9865 | +#> |.....................| -0.6621 | -0.9586 | -0.9093 | -0.7251 | +#> <span style='text-decoration: underline;'>|.....................| -0.7954 |...........|...........|...........|</span> +#> | U| 752.28211 | 86.04 | -3.210 | -4.598 | -0.3606 | +#> |.....................| 4.623 | 0.6483 | 0.8743 | 1.328 | +#> <span style='text-decoration: underline;'>|.....................| 1.046 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.28211</span> | 86.04 | 0.04034 | 0.01008 | 0.4108 | +#> |.....................| 4.623 | 0.6483 | 0.8743 | 1.328 | +#> <span style='text-decoration: underline;'>|.....................| 1.046 |...........|...........|...........|</span> +#> | F| Forward Diff. | 4.685 | 0.02318 | 0.3105 | 0.3984 | +#> |.....................| -4.118 | 1.106 | 0.4577 | -2.335 | +#> <span style='text-decoration: underline;'>|.....................| -1.438 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 36</span>| 752.26507 | 0.9926 | -0.9736 | -1.031 | -0.9930 | +#> |.....................| -0.6630 | -0.9604 | -0.9091 | -0.7199 | +#> <span style='text-decoration: underline;'>|.....................| -0.7902 |...........|...........|...........|</span> +#> | U| 752.26507 | 85.89 | -3.210 | -4.598 | -0.3628 | +#> |.....................| 4.621 | 0.6470 | 0.8745 | 1.334 | +#> <span style='text-decoration: underline;'>|.....................| 1.051 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.26507</span> | 85.89 | 0.04034 | 0.01007 | 0.4103 | +#> |.....................| 4.621 | 0.6470 | 0.8745 | 1.334 | +#> <span style='text-decoration: underline;'>|.....................| 1.051 |...........|...........|...........|</span> +#> | F| Forward Diff. | -6.810 | 0.03096 | 0.2910 | 0.3899 | +#> |.....................| -4.283 | 0.8991 | 0.4756 | -2.130 | +#> <span style='text-decoration: underline;'>|.....................| -1.153 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 37</span>| 752.24597 | 0.9942 | -0.9737 | -1.033 | -1.000 | +#> |.....................| -0.6608 | -0.9614 | -0.9045 | -0.7160 | +#> <span style='text-decoration: underline;'>|.....................| -0.7919 |...........|...........|...........|</span> +#> | U| 752.24597 | 86.03 | -3.211 | -4.600 | -0.3653 | +#> |.....................| 4.626 | 0.6463 | 0.8787 | 1.339 | +#> <span style='text-decoration: underline;'>|.....................| 1.049 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.24597</span> | 86.03 | 0.04033 | 0.01005 | 0.4097 | +#> |.....................| 4.626 | 0.6463 | 0.8787 | 1.339 | +#> <span style='text-decoration: underline;'>|.....................| 1.049 |...........|...........|...........|</span> +#> | F| Forward Diff. | 3.512 | 0.02244 | 0.2659 | 0.3868 | +#> |.....................| -3.943 | 0.9821 | 0.8784 | -2.032 | +#> <span style='text-decoration: underline;'>|.....................| -1.263 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 38</span>| 752.22949 | 0.9926 | -0.9738 | -1.034 | -1.007 | +#> |.....................| -0.6572 | -0.9618 | -0.9098 | -0.7144 | +#> <span style='text-decoration: underline;'>|.....................| -0.7948 |...........|...........|...........|</span> +#> | U| 752.22949 | 85.90 | -3.211 | -4.601 | -0.3676 | +#> |.....................| 4.634 | 0.6461 | 0.8739 | 1.341 | +#> <span style='text-decoration: underline;'>|.....................| 1.047 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.22949</span> | 85.90 | 0.04033 | 0.01004 | 0.4091 | +#> |.....................| 4.634 | 0.6461 | 0.8739 | 1.341 | +#> <span style='text-decoration: underline;'>|.....................| 1.047 |...........|...........|...........|</span> +#> | F| Forward Diff. | -6.652 | 0.02915 | 0.2261 | 0.3631 | +#> |.....................| -3.474 | 0.8493 | 0.4224 | -1.980 | +#> <span style='text-decoration: underline;'>|.....................| -1.394 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 39</span>| 752.21433 | 0.9945 | -0.9739 | -1.034 | -1.016 | +#> |.....................| -0.6569 | -0.9629 | -0.9144 | -0.7124 | +#> <span style='text-decoration: underline;'>|.....................| -0.7922 |...........|...........|...........|</span> +#> | U| 752.21433 | 86.05 | -3.211 | -4.601 | -0.3704 | +#> |.....................| 4.634 | 0.6453 | 0.8697 | 1.343 | +#> <span style='text-decoration: underline;'>|.....................| 1.049 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.21433</span> | 86.05 | 0.04032 | 0.01004 | 0.4085 | +#> |.....................| 4.634 | 0.6453 | 0.8697 | 1.343 | +#> <span style='text-decoration: underline;'>|.....................| 1.049 |...........|...........|...........|</span> +#> | F| Forward Diff. | 5.271 | 0.01812 | 0.2470 | 0.3694 | +#> |.....................| -3.388 | 0.9655 | 0.02976 | -1.920 | +#> <span style='text-decoration: underline;'>|.....................| -1.299 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 40</span>| 752.19821 | 0.9933 | -0.9740 | -1.034 | -1.022 | +#> |.....................| -0.6566 | -0.9648 | -0.9096 | -0.7099 | +#> <span style='text-decoration: underline;'>|.....................| -0.7872 |...........|...........|...........|</span> +#> | U| 752.19821 | 85.95 | -3.211 | -4.602 | -0.3726 | +#> |.....................| 4.635 | 0.6440 | 0.8741 | 1.346 | +#> <span style='text-decoration: underline;'>|.....................| 1.054 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.19821</span> | 85.95 | 0.04032 | 0.01004 | 0.4079 | +#> |.....................| 4.635 | 0.6440 | 0.8741 | 1.346 | +#> <span style='text-decoration: underline;'>|.....................| 1.054 |...........|...........|...........|</span> +#> | F| Forward Diff. | -2.667 | 0.02369 | 0.2481 | 0.3640 | +#> |.....................| -3.371 | 0.7751 | 0.4401 | -1.801 | +#> <span style='text-decoration: underline;'>|.....................| -1.045 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 41</span>| 752.18532 | 0.9951 | -0.9741 | -1.036 | -1.031 | +#> |.....................| -0.6545 | -0.9659 | -0.9070 | -0.7062 | +#> <span style='text-decoration: underline;'>|.....................| -0.7858 |...........|...........|...........|</span> +#> | U| 752.18532 | 86.11 | -3.211 | -4.603 | -0.3754 | +#> |.....................| 4.639 | 0.6432 | 0.8764 | 1.350 | +#> <span style='text-decoration: underline;'>|.....................| 1.055 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.18532</span> | 86.11 | 0.04032 | 0.01002 | 0.4072 | +#> |.....................| 4.639 | 0.6432 | 0.8764 | 1.350 | +#> <span style='text-decoration: underline;'>|.....................| 1.055 |...........|...........|...........|</span> +#> | F| Forward Diff. | 8.833 | 0.01368 | 0.2421 | 0.3674 | +#> |.....................| -3.039 | 0.8770 | 0.6679 | -1.687 | +#> <span style='text-decoration: underline;'>|.....................| -1.010 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 42</span>| 752.16831 | 0.9936 | -0.9742 | -1.037 | -1.039 | +#> |.....................| -0.6539 | -0.9664 | -0.9110 | -0.7027 | +#> <span style='text-decoration: underline;'>|.....................| -0.7873 |...........|...........|...........|</span> +#> | U| 752.16831 | 85.98 | -3.211 | -4.605 | -0.3782 | +#> |.....................| 4.641 | 0.6428 | 0.8728 | 1.354 | +#> <span style='text-decoration: underline;'>|.....................| 1.054 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.16831</span> | 85.98 | 0.04031 | 0.01001 | 0.4066 | +#> |.....................| 4.641 | 0.6428 | 0.8728 | 1.354 | +#> <span style='text-decoration: underline;'>|.....................| 1.054 |...........|...........|...........|</span> +#> | F| Forward Diff. | -0.7512 | 0.02003 | 0.1902 | 0.3449 | +#> |.....................| -2.985 | 0.7407 | 0.3269 | -1.581 | +#> <span style='text-decoration: underline;'>|.....................| -1.064 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 43</span>| 752.14828 | 0.9957 | -0.9743 | -1.038 | -1.040 | +#> |.....................| -0.6457 | -0.9684 | -0.9119 | -0.6984 | +#> <span style='text-decoration: underline;'>|.....................| -0.7843 |...........|...........|...........|</span> +#> | U| 752.14828 | 86.16 | -3.211 | -4.605 | -0.3785 | +#> |.....................| 4.658 | 0.6414 | 0.8720 | 1.359 | +#> <span style='text-decoration: underline;'>|.....................| 1.057 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.14828</span> | 86.16 | 0.04031 | 0.01000 | 0.4065 | +#> |.....................| 4.658 | 0.6414 | 0.8720 | 1.359 | +#> <span style='text-decoration: underline;'>|.....................| 1.057 |...........|...........|...........|</span> +#> | F| Forward Diff. | 12.68 | 0.008742 | 0.2033 | 0.3626 | +#> |.....................| -1.835 | 0.8163 | 0.2532 | -1.452 | +#> <span style='text-decoration: underline;'>|.....................| -0.9466 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 44</span>| 752.12689 | 0.9938 | -0.9744 | -1.038 | -1.049 | +#> |.....................| -0.6468 | -0.9706 | -0.9116 | -0.6946 | +#> <span style='text-decoration: underline;'>|.....................| -0.7819 |...........|...........|...........|</span> +#> | U| 752.12689 | 86.00 | -3.211 | -4.606 | -0.3814 | +#> |.....................| 4.656 | 0.6399 | 0.8723 | 1.363 | +#> <span style='text-decoration: underline;'>|.....................| 1.059 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.12689</span> | 86.00 | 0.04030 | 0.009996 | 0.4058 | +#> |.....................| 4.656 | 0.6399 | 0.8723 | 1.363 | +#> <span style='text-decoration: underline;'>|.....................| 1.059 |...........|...........|...........|</span> +#> | F| Forward Diff. | -0.08747 | 0.01751 | 0.1808 | 0.3434 | +#> |.....................| -2.013 | 0.5634 | 0.2760 | -1.320 | +#> <span style='text-decoration: underline;'>|.....................| -0.7971 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 45</span>| 752.10460 | 0.9941 | -0.9745 | -1.039 | -1.050 | +#> |.....................| -0.6390 | -0.9728 | -0.9127 | -0.6895 | +#> <span style='text-decoration: underline;'>|.....................| -0.7788 |...........|...........|...........|</span> +#> | U| 752.1046 | 86.03 | -3.211 | -4.606 | -0.3818 | +#> |.....................| 4.673 | 0.6383 | 0.8713 | 1.369 | +#> <span style='text-decoration: underline;'>|.....................| 1.062 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.1046</span> | 86.03 | 0.04030 | 0.009989 | 0.4057 | +#> |.....................| 4.673 | 0.6383 | 0.8713 | 1.369 | +#> <span style='text-decoration: underline;'>|.....................| 1.062 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 46</span>| 752.09051 | 0.9947 | -0.9746 | -1.040 | -1.052 | +#> |.....................| -0.6247 | -0.9768 | -0.9147 | -0.6801 | +#> <span style='text-decoration: underline;'>|.....................| -0.7732 |...........|...........|...........|</span> +#> | U| 752.09051 | 86.08 | -3.211 | -4.608 | -0.3827 | +#> |.....................| 4.704 | 0.6355 | 0.8695 | 1.380 | +#> <span style='text-decoration: underline;'>|.....................| 1.067 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.09051</span> | 86.08 | 0.04030 | 0.009976 | 0.4055 | +#> |.....................| 4.704 | 0.6355 | 0.8695 | 1.380 | +#> <span style='text-decoration: underline;'>|.....................| 1.067 |...........|...........|...........|</span> +#> | F| Forward Diff. | 5.771 | 0.01029 | 0.1542 | 0.3620 | +#> |.....................| 0.8997 | 0.2873 | 0.01810 | -0.9019 | +#> <span style='text-decoration: underline;'>|.....................| -0.3639 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 47</span>| 752.06630 | 0.9944 | -0.9751 | -1.045 | -1.068 | +#> |.....................| -0.6300 | -0.9815 | -0.9184 | -0.6573 | +#> <span style='text-decoration: underline;'>|.....................| -0.7726 |...........|...........|...........|</span> +#> | U| 752.0663 | 86.05 | -3.212 | -4.613 | -0.3878 | +#> |.....................| 4.692 | 0.6323 | 0.8661 | 1.407 | +#> <span style='text-decoration: underline;'>|.....................| 1.068 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.0663</span> | 86.05 | 0.04028 | 0.009926 | 0.4043 | +#> |.....................| 4.692 | 0.6323 | 0.8661 | 1.407 | +#> <span style='text-decoration: underline;'>|.....................| 1.068 |...........|...........|...........|</span> +#> | F| Forward Diff. | 3.128 | 0.007908 | 0.004436 | 0.3353 | +#> |.....................| 0.2209 | 0.1645 | -0.3029 | -0.2852 | +#> <span style='text-decoration: underline;'>|.....................| -0.2419 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 48</span>| 752.06241 | 0.9926 | -0.9758 | -1.042 | -1.095 | +#> |.....................| -0.6306 | -0.9841 | -0.9113 | -0.6557 | +#> <span style='text-decoration: underline;'>|.....................| -0.7685 |...........|...........|...........|</span> +#> | U| 752.06241 | 85.89 | -3.213 | -4.609 | -0.3969 | +#> |.....................| 4.691 | 0.6304 | 0.8725 | 1.408 | +#> <span style='text-decoration: underline;'>|.....................| 1.072 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.06241</span> | 85.89 | 0.04025 | 0.009958 | 0.4021 | +#> |.....................| 4.691 | 0.6304 | 0.8725 | 1.408 | +#> <span style='text-decoration: underline;'>|.....................| 1.072 |...........|...........|...........|</span> +#> | F| Forward Diff. | -8.924 | 0.01284 | 0.1020 | 0.2919 | +#> |.....................| 0.1011 | -0.08995 | 0.3194 | -0.2130 | +#> <span style='text-decoration: underline;'>|.....................| -0.05120 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 49</span>| 752.04768 | 0.9941 | -0.9763 | -1.043 | -1.124 | +#> |.....................| -0.6313 | -0.9862 | -0.9116 | -0.6566 | +#> <span style='text-decoration: underline;'>|.....................| -0.7644 |...........|...........|...........|</span> +#> | U| 752.04768 | 86.02 | -3.213 | -4.611 | -0.4065 | +#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | +#> <span style='text-decoration: underline;'>|.....................| 1.076 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.04768</span> | 86.02 | 0.04023 | 0.009946 | 0.3998 | +#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | +#> <span style='text-decoration: underline;'>|.....................| 1.076 |...........|...........|...........|</span> +#> | F| Forward Diff. | 0.04447 | 0.001311 | 0.1345 | 0.2729 | +#> |.....................| 0.05334 | -0.06694 | 0.2984 | -0.1966 | +#> <span style='text-decoration: underline;'>|.....................| 0.06514 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 50</span>| 752.04768 | 0.9941 | -0.9763 | -1.043 | -1.124 | +#> |.....................| -0.6313 | -0.9862 | -0.9116 | -0.6566 | +#> <span style='text-decoration: underline;'>|.....................| -0.7644 |...........|...........|...........|</span> +#> | U| 752.04768 | 86.02 | -3.213 | -4.611 | -0.4065 | +#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | +#> <span style='text-decoration: underline;'>|.....................| 1.076 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 752.04768</span> | 86.02 | 0.04023 | 0.009946 | 0.3998 | +#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | +#> <span style='text-decoration: underline;'>|.....................| 1.076 |...........|...........|...........|</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: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</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='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'>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_A1 |f_parent_qlogis | log_alpha | +#> |.....................| log_beta | sigma | o1 | o2 | +#> <span style='text-decoration: underline;'>|.....................| o3 | o4 | o5 |...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 491.68697 | 1.000 | -1.000 | -0.9113 | -0.8954 | +#> |.....................| -0.8491 | -0.8582 | -0.8760 | -0.8739 | +#> |.....................| -0.8673 | -0.8694 | -0.8683 |...........| +#> | U| 491.68697 | 94.21 | -5.416 | -0.9966 | -0.2046 | +#> |.....................| 2.098 | 1.647 | 0.7612 | 0.8665 | +#> |.....................| 1.192 | 1.089 | 1.144 |...........| +#> | X|<span style='font-weight: bold;'> 491.68697</span> | 94.21 | 0.004447 | 0.2696 | 0.8150 | +#> |.....................| 8.153 | 1.647 | 0.7612 | 0.8665 | +#> <span style='text-decoration: underline;'>|.....................| 1.192 | 1.089 | 1.144 |...........|</span> +#> | G| Gill Diff. | 19.86 | 1.831 | -0.1132 | -0.03447 | +#> |.....................| -0.1365 | -48.08 | 10.28 | 8.952 | +#> <span style='text-decoration: underline;'>|.....................| -12.04 | -8.764 | -10.61 |...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 1105.9428 | 0.6506 | -1.032 | -0.9093 | -0.8948 | +#> |.....................| -0.8467 | -0.01215 | -1.057 | -1.031 | +#> |.....................| -0.6554 | -0.7152 | -0.6817 |...........| +#> | U| 1105.9428 | 61.29 | -5.448 | -0.9946 | -0.2040 | +#> |.....................| 2.101 | 2.344 | 0.6235 | 0.7300 | +#> |.....................| 1.445 | 1.256 | 1.357 |...........| +#> | X|<span style='font-weight: bold;'> 1105.9428</span> | 61.29 | 0.004306 | 0.2700 | 0.8155 | +#> |.....................| 8.173 | 2.344 | 0.6235 | 0.7300 | +#> <span style='text-decoration: underline;'>|.....................| 1.445 | 1.256 | 1.357 |...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 499.02505 | 0.9651 | -1.003 | -0.9111 | -0.8953 | +#> |.....................| -0.8489 | -0.7736 | -0.8941 | -0.8896 | +#> |.....................| -0.8462 | -0.8540 | -0.8497 |...........| +#> | U| 499.02505 | 90.91 | -5.419 | -0.9964 | -0.2045 | +#> |.....................| 2.099 | 1.717 | 0.7475 | 0.8529 | +#> |.....................| 1.217 | 1.105 | 1.165 |...........| +#> | X|<span style='font-weight: bold;'> 499.02505</span> | 90.91 | 0.004433 | 0.2696 | 0.8150 | +#> |.....................| 8.155 | 1.717 | 0.7475 | 0.8529 | +#> <span style='text-decoration: underline;'>|.....................| 1.217 | 1.105 | 1.165 |...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 491.11153 | 0.9924 | -1.001 | -0.9112 | -0.8954 | +#> |.....................| -0.8491 | -0.8397 | -0.8799 | -0.8773 | +#> |.....................| -0.8627 | -0.8661 | -0.8642 |...........| +#> | U| 491.11153 | 93.49 | -5.416 | -0.9966 | -0.2046 | +#> |.....................| 2.098 | 1.663 | 0.7582 | 0.8635 | +#> |.....................| 1.198 | 1.092 | 1.148 |...........| +#> | X|<span style='font-weight: bold;'> 491.11153</span> | 93.49 | 0.004444 | 0.2696 | 0.8150 | +#> |.....................| 8.154 | 1.663 | 0.7582 | 0.8635 | +#> <span style='text-decoration: underline;'>|.....................| 1.198 | 1.092 | 1.148 |...........|</span> +#> | F| Forward Diff. | -141.0 | 1.761 | -0.2309 | -0.1084 | +#> |.....................| -0.3671 | -44.06 | 11.23 | 7.698 | +#> <span style='text-decoration: underline;'>|.....................| -11.77 | -8.480 | -10.17 |...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 489.72110 | 1.001 | -1.001 | -0.9112 | -0.8954 | +#> |.....................| -0.8490 | -0.8217 | -0.8840 | -0.8806 | +#> |.....................| -0.8581 | -0.8627 | -0.8602 |...........| +#> | U| 489.7211 | 94.29 | -5.417 | -0.9965 | -0.2046 | +#> |.....................| 2.099 | 1.678 | 0.7552 | 0.8607 | +#> |.....................| 1.203 | 1.096 | 1.153 |...........| +#> | X|<span style='font-weight: bold;'> 489.7211</span> | 94.29 | 0.004441 | 0.2696 | 0.8150 | +#> |.....................| 8.154 | 1.678 | 0.7552 | 0.8607 | +#> <span style='text-decoration: underline;'>|.....................| 1.203 | 1.096 | 1.153 |...........|</span> +#> | F| Forward Diff. | 37.99 | 1.786 | -0.09663 | -0.03934 | +#> |.....................| -0.1210 | -40.49 | 9.520 | 7.642 | +#> <span style='text-decoration: underline;'>|.....................| -11.65 | -8.313 | -10.04 |...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 488.87741 | 0.9957 | -1.002 | -0.9111 | -0.8953 | +#> |.....................| -0.8490 | -0.8027 | -0.8883 | -0.8842 | +#> |.....................| -0.8530 | -0.8591 | -0.8558 |...........| +#> | U| 488.87741 | 93.80 | -5.418 | -0.9965 | -0.2045 | +#> |.....................| 2.099 | 1.693 | 0.7519 | 0.8576 | +#> |.....................| 1.209 | 1.100 | 1.158 |...........| +#> | X|<span style='font-weight: bold;'> 488.87741</span> | 93.80 | 0.004437 | 0.2696 | 0.8150 | +#> |.....................| 8.155 | 1.693 | 0.7519 | 0.8576 | +#> <span style='text-decoration: underline;'>|.....................| 1.209 | 1.100 | 1.158 |...........|</span> +#> | F| Forward Diff. | -68.52 | 1.732 | -0.1791 | -0.08434 | +#> |.....................| -0.2775 | -36.72 | 9.505 | 7.234 | +#> <span style='text-decoration: underline;'>|.....................| -11.37 | -8.098 | -9.790 |...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 487.98842 | 1.002 | -1.003 | -0.9111 | -0.8953 | +#> |.....................| -0.8489 | -0.7841 | -0.8926 | -0.8878 | +#> |.....................| -0.8478 | -0.8553 | -0.8512 |...........| +#> | U| 487.98842 | 94.37 | -5.418 | -0.9964 | -0.2045 | +#> |.....................| 2.099 | 1.708 | 0.7486 | 0.8545 | +#> |.....................| 1.215 | 1.104 | 1.163 |...........| +#> | X|<span style='font-weight: bold;'> 487.98842</span> | 94.37 | 0.004434 | 0.2697 | 0.8150 | +#> |.....................| 8.156 | 1.708 | 0.7486 | 0.8545 | +#> <span style='text-decoration: underline;'>|.....................| 1.215 | 1.104 | 1.163 |...........|</span> +#> | F| Forward Diff. | 53.83 | 1.743 | -0.07921 | -0.03701 | +#> |.....................| -0.09401 | -33.22 | 8.823 | 7.101 | +#> <span style='text-decoration: underline;'>|.....................| -11.24 | -7.914 | -9.621 |...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 487.18834 | 0.9967 | -1.004 | -0.9110 | -0.8953 | +#> |.....................| -0.8488 | -0.7657 | -0.8973 | -0.8916 | +#> |.....................| -0.8421 | -0.8512 | -0.8463 |...........| +#> | U| 487.18834 | 93.89 | -5.419 | -0.9963 | -0.2045 | +#> |.....................| 2.099 | 1.724 | 0.7451 | 0.8512 | +#> |.....................| 1.222 | 1.108 | 1.169 |...........| +#> | X|<span style='font-weight: bold;'> 487.18834</span> | 93.89 | 0.004430 | 0.2697 | 0.8151 | +#> |.....................| 8.156 | 1.724 | 0.7451 | 0.8512 | +#> <span style='text-decoration: underline;'>|.....................| 1.222 | 1.108 | 1.169 |...........|</span> +#> | F| Forward Diff. | -47.29 | 1.692 | -0.1608 | -0.08286 | +#> |.....................| -0.2512 | -29.89 | 8.493 | 6.629 | +#> <span style='text-decoration: underline;'>|.....................| -10.92 | -7.677 | -9.350 |...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 486.46922 | 1.002 | -1.005 | -0.9109 | -0.8952 | +#> |.....................| -0.8487 | -0.7480 | -0.9022 | -0.8958 | +#> |.....................| -0.8355 | -0.8466 | -0.8406 |...........| +#> | U| 486.46922 | 94.36 | -5.420 | -0.9963 | -0.2045 | +#> |.....................| 2.099 | 1.738 | 0.7413 | 0.8476 | +#> |.....................| 1.230 | 1.113 | 1.175 |...........| +#> | X|<span style='font-weight: bold;'> 486.46922</span> | 94.36 | 0.004425 | 0.2697 | 0.8151 | +#> |.....................| 8.157 | 1.738 | 0.7413 | 0.8476 | +#> <span style='text-decoration: underline;'>|.....................| 1.230 | 1.113 | 1.175 |...........|</span> +#> | F| Forward Diff. | 49.83 | 1.694 | -0.07480 | -0.03429 | +#> |.....................| -0.09436 | -26.68 | 8.123 | 6.503 | +#> <span style='text-decoration: underline;'>|.....................| -10.68 | -7.439 | -9.119 |...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 485.78721 | 0.9968 | -1.006 | -0.9109 | -0.8952 | +#> |.....................| -0.8486 | -0.7319 | -0.9078 | -0.9005 | +#> |.....................| -0.8277 | -0.8412 | -0.8339 |...........| +#> | U| 485.78721 | 93.91 | -5.422 | -0.9962 | -0.2044 | +#> |.....................| 2.099 | 1.752 | 0.7370 | 0.8435 | +#> |.....................| 1.239 | 1.119 | 1.183 |...........| +#> | X|<span style='font-weight: bold;'> 485.78721</span> | 93.91 | 0.004420 | 0.2697 | 0.8151 | +#> |.....................| 8.158 | 1.752 | 0.7370 | 0.8435 | +#> <span style='text-decoration: underline;'>|.....................| 1.239 | 1.119 | 1.183 |...........|</span> +#> | F| Forward Diff. | -42.45 | 1.646 | -0.1526 | -0.07491 | +#> |.....................| -0.2510 | -24.12 | 7.576 | 5.974 | +#> <span style='text-decoration: underline;'>|.....................| -10.35 | -7.128 | -8.768 |...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 485.17009 | 1.001 | -1.008 | -0.9107 | -0.8952 | +#> |.....................| -0.8484 | -0.7183 | -0.9141 | -0.9058 | +#> |.....................| -0.8180 | -0.8347 | -0.8257 |...........| +#> | U| 485.17009 | 94.32 | -5.423 | -0.9961 | -0.2044 | +#> |.....................| 2.099 | 1.763 | 0.7322 | 0.8389 | +#> |.....................| 1.251 | 1.126 | 1.192 |...........| +#> | X|<span style='font-weight: bold;'> 485.17009</span> | 94.32 | 0.004413 | 0.2697 | 0.8152 | +#> |.....................| 8.160 | 1.763 | 0.7322 | 0.8389 | +#> <span style='text-decoration: underline;'>|.....................| 1.251 | 1.126 | 1.192 |...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 484.56759 | 1.002 | -1.010 | -0.9106 | -0.8951 | +#> |.....................| -0.8481 | -0.7038 | -0.9212 | -0.9119 | +#> |.....................| -0.8067 | -0.8272 | -0.8163 |...........| +#> | U| 484.56759 | 94.37 | -5.425 | -0.9959 | -0.2043 | +#> |.....................| 2.099 | 1.775 | 0.7268 | 0.8336 | +#> |.....................| 1.264 | 1.134 | 1.203 |...........| +#> | X|<span style='font-weight: bold;'> 484.56759</span> | 94.37 | 0.004404 | 0.2697 | 0.8152 | +#> |.....................| 8.162 | 1.775 | 0.7268 | 0.8336 | +#> <span style='text-decoration: underline;'>|.....................| 1.264 | 1.134 | 1.203 |...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 483.17982 | 1.003 | -1.015 | -0.9102 | -0.8949 | +#> |.....................| -0.8475 | -0.6634 | -0.9410 | -0.9287 | +#> |.....................| -0.7754 | -0.8064 | -0.7900 |...........| +#> | U| 483.17982 | 94.51 | -5.431 | -0.9956 | -0.2042 | +#> |.....................| 2.100 | 1.808 | 0.7117 | 0.8190 | +#> |.....................| 1.302 | 1.157 | 1.233 |...........| +#> | X|<span style='font-weight: bold;'> 483.17982</span> | 94.51 | 0.004381 | 0.2698 | 0.8153 | +#> |.....................| 8.167 | 1.808 | 0.7117 | 0.8190 | +#> <span style='text-decoration: underline;'>|.....................| 1.302 | 1.157 | 1.233 |...........|</span> +#> | F| Forward Diff. | 68.60 | 1.559 | 0.008498 | -0.01857 | +#> |.....................| -0.01950 | -13.38 | 5.413 | 4.461 | +#> <span style='text-decoration: underline;'>|.....................| -8.084 | -5.202 | -6.751 |...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 482.50435 | 0.9937 | -1.034 | -0.9105 | -0.8944 | +#> |.....................| -0.8462 | -0.6947 | -0.9713 | -0.9553 | +#> |.....................| -0.7043 | -0.7694 | -0.7343 |...........| +#> | U| 482.50435 | 93.61 | -5.449 | -0.9958 | -0.2036 | +#> |.....................| 2.101 | 1.782 | 0.6887 | 0.7959 | +#> |.....................| 1.386 | 1.197 | 1.297 |...........| +#> | X|<span style='font-weight: bold;'> 482.50435</span> | 93.61 | 0.004300 | 0.2698 | 0.8158 | +#> |.....................| 8.177 | 1.782 | 0.6887 | 0.7959 | +#> <span style='text-decoration: underline;'>|.....................| 1.386 | 1.197 | 1.297 |...........|</span> +#> | F| Forward Diff. | -85.62 | 1.442 | -0.1650 | -0.08233 | +#> |.....................| -0.3434 | -17.31 | 3.930 | 3.048 | +#> <span style='text-decoration: underline;'>|.....................| -4.934 | -3.045 | -4.080 |...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 481.97261 | 1.003 | -1.090 | -0.9106 | -0.8929 | +#> |.....................| -0.8403 | -0.7109 | -0.9936 | -0.9798 | +#> |.....................| -0.6305 | -0.7595 | -0.6850 |...........| +#> | U| 481.97261 | 94.53 | -5.505 | -0.9959 | -0.2021 | +#> |.....................| 2.107 | 1.769 | 0.6717 | 0.7747 | +#> |.....................| 1.474 | 1.208 | 1.353 |...........| +#> | X|<span style='font-weight: bold;'> 481.97261</span> | 94.53 | 0.004066 | 0.2697 | 0.8170 | +#> |.....................| 8.226 | 1.769 | 0.6717 | 0.7747 | +#> <span style='text-decoration: underline;'>|.....................| 1.474 | 1.208 | 1.353 |...........|</span> +#> | F| Forward Diff. | 56.89 | 1.274 | 0.1237 | 0.02279 | +#> |.....................| 0.2367 | -19.64 | 1.923 | 2.281 | +#> <span style='text-decoration: underline;'>|.....................| -1.663 | -2.419 | -1.870 |...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 481.06506 | 1.001 | -1.169 | -0.9152 | -0.8919 | +#> |.....................| -0.8407 | -0.6475 | -0.9528 | -0.9773 | +#> |.....................| -0.6368 | -0.7786 | -0.6952 |...........| +#> | U| 481.06506 | 94.29 | -5.585 | -1.000 | -0.2011 | +#> |.....................| 2.107 | 1.821 | 0.7028 | 0.7769 | +#> |.....................| 1.467 | 1.187 | 1.341 |...........| +#> | X|<span style='font-weight: bold;'> 481.06506</span> | 94.29 | 0.003755 | 0.2688 | 0.8179 | +#> |.....................| 8.223 | 1.821 | 0.7028 | 0.7769 | +#> <span style='text-decoration: underline;'>|.....................| 1.467 | 1.187 | 1.341 |...........|</span> +#> | F| Forward Diff. | 24.24 | 0.9898 | -0.1087 | 0.01886 | +#> |.....................| 0.1247 | -10.78 | 3.743 | 2.188 | +#> <span style='text-decoration: underline;'>|.....................| -2.085 | -3.507 | -2.452 |...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 481.22982 | 0.9921 | -1.212 | -0.9099 | -0.8928 | +#> |.....................| -0.8459 | -0.6315 | -1.015 | -0.9814 | +#> |.....................| -0.6906 | -0.7213 | -0.7106 |...........| +#> | U| 481.22982 | 93.46 | -5.628 | -0.9952 | -0.2020 | +#> |.....................| 2.102 | 1.834 | 0.6553 | 0.7733 | +#> |.....................| 1.403 | 1.250 | 1.324 |...........| +#> | X|<span style='font-weight: bold;'> 481.22982</span> | 93.46 | 0.003596 | 0.2699 | 0.8171 | +#> |.....................| 8.180 | 1.834 | 0.6553 | 0.7733 | +#> <span style='text-decoration: underline;'>|.....................| 1.403 | 1.250 | 1.324 |...........|</span> +#> |<span style='font-weight: bold;'> 18</span>| 481.29798 | 0.9919 | -1.186 | -0.9131 | -0.8922 | +#> |.....................| -0.8428 | -0.6388 | -0.9780 | -0.9794 | +#> |.....................| -0.6574 | -0.7554 | -0.7007 |...........| +#> | U| 481.29798 | 93.44 | -5.602 | -0.9984 | -0.2014 | +#> |.....................| 2.105 | 1.828 | 0.6836 | 0.7751 | +#> |.....................| 1.442 | 1.213 | 1.335 |...........| +#> | X|<span style='font-weight: bold;'> 481.29798</span> | 93.44 | 0.003691 | 0.2693 | 0.8176 | +#> |.....................| 8.206 | 1.828 | 0.6836 | 0.7751 | +#> <span style='text-decoration: underline;'>|.....................| 1.442 | 1.213 | 1.335 |...........|</span> +#> |<span style='font-weight: bold;'> 19</span>| 481.41397 | 0.9918 | -1.173 | -0.9147 | -0.8919 | +#> |.....................| -0.8412 | -0.6424 | -0.9596 | -0.9784 | +#> |.....................| -0.6408 | -0.7724 | -0.6957 |...........| +#> | U| 481.41397 | 93.43 | -5.589 | -1.000 | -0.2012 | +#> |.....................| 2.106 | 1.825 | 0.6976 | 0.7759 | +#> |.....................| 1.462 | 1.194 | 1.341 |...........| +#> | X|<span style='font-weight: bold;'> 481.41397</span> | 93.43 | 0.003739 | 0.2689 | 0.8178 | +#> |.....................| 8.219 | 1.825 | 0.6976 | 0.7759 | +#> <span style='text-decoration: underline;'>|.....................| 1.462 | 1.194 | 1.341 |...........|</span> +#> |<span style='font-weight: bold;'> 20</span>| 481.05031 | 0.9977 | -1.169 | -0.9152 | -0.8919 | +#> |.....................| -0.8407 | -0.6461 | -0.9533 | -0.9776 | +#> |.....................| -0.6366 | -0.7782 | -0.6949 |...........| +#> | U| 481.05031 | 93.99 | -5.585 | -1.000 | -0.2011 | +#> |.....................| 2.107 | 1.822 | 0.7024 | 0.7766 | +#> |.....................| 1.467 | 1.188 | 1.342 |...........| +#> | X|<span style='font-weight: bold;'> 481.05031</span> | 93.99 | 0.003754 | 0.2688 | 0.8179 | +#> |.....................| 8.223 | 1.822 | 0.7024 | 0.7766 | +#> <span style='text-decoration: underline;'>|.....................| 1.467 | 1.188 | 1.342 |...........|</span> +#> | F| Forward Diff. | -27.42 | 0.9768 | -0.2107 | -0.01109 | +#> |.....................| -0.02839 | -10.63 | 3.585 | 2.076 | +#> <span style='text-decoration: underline;'>|.....................| -2.082 | -3.487 | -2.432 |...........|</span> +#> |<span style='font-weight: bold;'> 21</span>| 481.00693 | 0.9997 | -1.170 | -0.9150 | -0.8919 | +#> |.....................| -0.8408 | -0.6450 | -0.9548 | -0.9778 | +#> |.....................| -0.6377 | -0.7765 | -0.6951 |...........| +#> | U| 481.00693 | 94.18 | -5.586 | -1.000 | -0.2011 | +#> |.....................| 2.107 | 1.823 | 0.7012 | 0.7764 | +#> |.....................| 1.466 | 1.190 | 1.342 |...........| +#> | X|<span style='font-weight: bold;'> 481.00693</span> | 94.18 | 0.003750 | 0.2689 | 0.8178 | +#> |.....................| 8.222 | 1.823 | 0.7012 | 0.7764 | +#> <span style='text-decoration: underline;'>|.....................| 1.466 | 1.190 | 1.342 |...........|</span> +#> | F| Forward Diff. | 5.549 | 0.9801 | -0.1366 | 0.007724 | +#> |.....................| 0.06864 | -10.47 | 3.736 | 2.095 | +#> <span style='text-decoration: underline;'>|.....................| -2.145 | -3.386 | -2.439 |...........|</span> +#> |<span style='font-weight: bold;'> 22</span>| 480.97727 | 0.9982 | -1.171 | -0.9150 | -0.8919 | +#> |.....................| -0.8408 | -0.6422 | -0.9558 | -0.9784 | +#> |.....................| -0.6371 | -0.7756 | -0.6944 |...........| +#> | U| 480.97727 | 94.04 | -5.586 | -1.000 | -0.2011 | +#> |.....................| 2.107 | 1.825 | 0.7005 | 0.7760 | +#> |.....................| 1.466 | 1.191 | 1.342 |...........| +#> | X|<span style='font-weight: bold;'> 480.97727</span> | 94.04 | 0.003749 | 0.2689 | 0.8178 | +#> |.....................| 8.222 | 1.825 | 0.7005 | 0.7760 | +#> <span style='text-decoration: underline;'>|.....................| 1.466 | 1.191 | 1.342 |...........|</span> +#> | F| Forward Diff. | -18.22 | 0.9728 | -0.1820 | -0.005388 | +#> |.....................| -0.004679 | -10.15 | 3.348 | 1.956 | +#> <span style='text-decoration: underline;'>|.....................| -2.141 | -3.348 | -2.415 |...........|</span> +#> |<span style='font-weight: bold;'> 23</span>| 480.94781 | 0.9999 | -1.172 | -0.9148 | -0.8919 | +#> |.....................| -0.8410 | -0.6410 | -0.9575 | -0.9785 | +#> |.....................| -0.6383 | -0.7738 | -0.6946 |...........| +#> | U| 480.94781 | 94.20 | -5.587 | -1.000 | -0.2011 | +#> |.....................| 2.107 | 1.826 | 0.6992 | 0.7758 | +#> |.....................| 1.465 | 1.193 | 1.342 |...........| +#> | X|<span style='font-weight: bold;'> 480.94781</span> | 94.20 | 0.003745 | 0.2689 | 0.8178 | +#> |.....................| 8.220 | 1.826 | 0.6992 | 0.7758 | +#> <span style='text-decoration: underline;'>|.....................| 1.465 | 1.193 | 1.342 |...........|</span> +#> | F| Forward Diff. | 8.568 | 0.9740 | -0.1199 | 0.009837 | +#> |.....................| 0.07469 | -9.926 | 3.371 | 0.7973 | +#> <span style='text-decoration: underline;'>|.....................| -2.181 | -3.230 | -2.408 |...........|</span> +#> |<span style='font-weight: bold;'> 24</span>| 480.92664 | 0.9984 | -1.173 | -0.9147 | -0.8919 | +#> |.....................| -0.8411 | -0.6390 | -0.9589 | -0.9778 | +#> |.....................| -0.6386 | -0.7721 | -0.6942 |...........| +#> | U| 480.92664 | 94.06 | -5.588 | -1.000 | -0.2011 | +#> |.....................| 2.107 | 1.828 | 0.6981 | 0.7765 | +#> |.....................| 1.465 | 1.195 | 1.343 |...........| +#> | X|<span style='font-weight: bold;'> 480.92664</span> | 94.06 | 0.003741 | 0.2689 | 0.8178 | +#> |.....................| 8.219 | 1.828 | 0.6981 | 0.7765 | +#> <span style='text-decoration: underline;'>|.....................| 1.465 | 1.195 | 1.343 |...........|</span> +#> | F| Forward Diff. | -15.24 | 0.9644 | -0.1632 | -0.002739 | +#> |.....................| -0.008738 | -9.656 | 3.177 | 0.7945 | +#> <span style='text-decoration: underline;'>|.....................| -2.140 | -3.159 | -2.407 |...........|</span> +#> |<span style='font-weight: bold;'> 25</span>| 480.90633 | 0.9999 | -1.174 | -0.9146 | -0.8920 | +#> |.....................| -0.8412 | -0.6376 | -0.9602 | -0.9760 | +#> |.....................| -0.6390 | -0.7705 | -0.6939 |...........| +#> | U| 480.90633 | 94.20 | -5.589 | -0.9999 | -0.2012 | +#> |.....................| 2.106 | 1.829 | 0.6971 | 0.7780 | +#> |.....................| 1.464 | 1.196 | 1.343 |...........| +#> | X|<span style='font-weight: bold;'> 480.90633</span> | 94.20 | 0.003737 | 0.2690 | 0.8178 | +#> |.....................| 8.219 | 1.829 | 0.6971 | 0.7780 | +#> <span style='text-decoration: underline;'>|.....................| 1.464 | 1.196 | 1.343 |...........|</span> +#> | F| Forward Diff. | 8.878 | 0.9654 | -0.1149 | 0.008298 | +#> |.....................| 0.06381 | -9.456 | 3.199 | 2.165 | +#> <span style='text-decoration: underline;'>|.....................| -2.158 | -3.035 | -2.359 |...........|</span> +#> |<span style='font-weight: bold;'> 26</span>| 480.88677 | 0.9984 | -1.175 | -0.9145 | -0.8920 | +#> |.....................| -0.8413 | -0.6358 | -0.9617 | -0.9757 | +#> |.....................| -0.6395 | -0.7687 | -0.6936 |...........| +#> | U| 480.88677 | 94.05 | -5.591 | -0.9998 | -0.2012 | +#> |.....................| 2.106 | 1.831 | 0.6960 | 0.7783 | +#> |.....................| 1.464 | 1.198 | 1.343 |...........| +#> | X|<span style='font-weight: bold;'> 480.88677</span> | 94.05 | 0.003733 | 0.2690 | 0.8178 | +#> |.....................| 8.218 | 1.831 | 0.6960 | 0.7783 | +#> <span style='text-decoration: underline;'>|.....................| 1.464 | 1.198 | 1.343 |...........|</span> +#> | F| Forward Diff. | -15.55 | 0.9550 | -0.1566 | -0.004027 | +#> |.....................| -0.01529 | -9.334 | 3.082 | 0.8457 | +#> <span style='text-decoration: underline;'>|.....................| -2.216 | -2.967 | -2.371 |...........|</span> +#> |<span style='font-weight: bold;'> 27</span>| 480.86430 | 0.9998 | -1.177 | -0.9143 | -0.8920 | +#> |.....................| -0.8414 | -0.6346 | -0.9633 | -0.9749 | +#> |.....................| -0.6404 | -0.7668 | -0.6935 |...........| +#> | U| 480.8643 | 94.19 | -5.592 | -0.9996 | -0.2012 | +#> |.....................| 2.106 | 1.832 | 0.6948 | 0.7790 | +#> |.....................| 1.463 | 1.200 | 1.343 |...........| +#> | X|<span style='font-weight: bold;'> 480.8643</span> | 94.19 | 0.003727 | 0.2690 | 0.8177 | +#> |.....................| 8.217 | 1.832 | 0.6948 | 0.7790 | +#> <span style='text-decoration: underline;'>|.....................| 1.463 | 1.200 | 1.343 |...........|</span> +#> | F| Forward Diff. | 6.756 | 0.9537 | -0.1079 | 0.006011 | +#> |.....................| 0.04748 | -9.023 | 3.021 | 2.222 | +#> <span style='text-decoration: underline;'>|.....................| -2.227 | -2.836 | -2.339 |...........|</span> +#> |<span style='font-weight: bold;'> 28</span>| 480.84403 | 0.9982 | -1.178 | -0.9142 | -0.8920 | +#> |.....................| -0.8415 | -0.6324 | -0.9646 | -0.9751 | +#> |.....................| -0.6405 | -0.7653 | -0.6931 |...........| +#> | U| 480.84403 | 94.04 | -5.593 | -0.9995 | -0.2012 | +#> |.....................| 2.106 | 1.833 | 0.6938 | 0.7788 | +#> |.....................| 1.462 | 1.202 | 1.344 |...........| +#> | X|<span style='font-weight: bold;'> 480.84403</span> | 94.04 | 0.003723 | 0.2690 | 0.8177 | +#> |.....................| 8.216 | 1.833 | 0.6938 | 0.7788 | +#> <span style='text-decoration: underline;'>|.....................| 1.462 | 1.202 | 1.344 |...........|</span> +#> | F| Forward Diff. | -17.74 | 0.9443 | -0.1486 | -0.005686 | +#> |.....................| -0.02964 | -8.905 | 2.905 | 2.091 | +#> <span style='text-decoration: underline;'>|.....................| -2.264 | -2.753 | -2.319 |...........|</span> +#> |<span style='font-weight: bold;'> 29</span>| 480.81486 | 0.9998 | -1.179 | -0.9140 | -0.8921 | +#> |.....................| -0.8417 | -0.6315 | -0.9657 | -0.9770 | +#> |.....................| -0.6415 | -0.7640 | -0.6932 |...........| +#> | U| 480.81486 | 94.18 | -5.595 | -0.9993 | -0.2013 | +#> |.....................| 2.106 | 1.834 | 0.6930 | 0.7772 | +#> |.....................| 1.461 | 1.203 | 1.344 |...........| +#> | X|<span style='font-weight: bold;'> 480.81486</span> | 94.18 | 0.003718 | 0.2691 | 0.8177 | +#> |.....................| 8.215 | 1.834 | 0.6930 | 0.7772 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.203 | 1.344 |...........|</span> +#> | F| Forward Diff. | 6.172 | 0.9439 | -0.09077 | 0.005496 | +#> |.....................| 0.04002 | -8.557 | 3.060 | 0.8688 | +#> <span style='text-decoration: underline;'>|.....................| -2.237 | -2.681 | -2.329 |...........|</span> +#> |<span style='font-weight: bold;'> 30</span>| 480.79675 | 0.9982 | -1.180 | -0.9139 | -0.8921 | +#> |.....................| -0.8418 | -0.6292 | -0.9672 | -0.9770 | +#> |.....................| -0.6415 | -0.7628 | -0.6927 |...........| +#> | U| 480.79675 | 94.04 | -5.596 | -0.9992 | -0.2013 | +#> |.....................| 2.106 | 1.836 | 0.6918 | 0.7772 | +#> |.....................| 1.461 | 1.205 | 1.344 |...........| +#> | X|<span style='font-weight: bold;'> 480.79675</span> | 94.04 | 0.003714 | 0.2691 | 0.8177 | +#> |.....................| 8.214 | 1.836 | 0.6918 | 0.7772 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.205 | 1.344 |...........|</span> +#> | F| Forward Diff. | -18.05 | 0.9344 | -0.1333 | -0.006636 | +#> |.....................| -0.03697 | -8.406 | 2.763 | 0.7695 | +#> <span style='text-decoration: underline;'>|.....................| -2.291 | -2.623 | -2.307 |...........|</span> +#> |<span style='font-weight: bold;'> 31</span>| 480.77804 | 0.9997 | -1.182 | -0.9138 | -0.8921 | +#> |.....................| -0.8419 | -0.6281 | -0.9686 | -0.9750 | +#> |.....................| -0.6417 | -0.7615 | -0.6923 |...........| +#> | U| 480.77804 | 94.18 | -5.597 | -0.9991 | -0.2013 | +#> |.....................| 2.106 | 1.837 | 0.6907 | 0.7789 | +#> |.....................| 1.461 | 1.206 | 1.345 |...........| +#> | X|<span style='font-weight: bold;'> 480.77804</span> | 94.18 | 0.003708 | 0.2691 | 0.8176 | +#> |.....................| 8.213 | 1.837 | 0.6907 | 0.7789 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.206 | 1.345 |...........|</span> +#> | F| Forward Diff. | 5.466 | 0.9331 | -0.08875 | 0.003744 | +#> |.....................| 0.02543 | -8.171 | 2.670 | 2.155 | +#> <span style='text-decoration: underline;'>|.....................| -2.279 | -2.534 | -2.278 |...........|</span> +#> |<span style='font-weight: bold;'> 32</span>| 480.75892 | 0.9982 | -1.183 | -0.9137 | -0.8921 | +#> |.....................| -0.8419 | -0.6258 | -0.9698 | -0.9756 | +#> |.....................| -0.6414 | -0.7603 | -0.6917 |...........| +#> | U| 480.75892 | 94.03 | -5.598 | -0.9991 | -0.2014 | +#> |.....................| 2.106 | 1.839 | 0.6899 | 0.7784 | +#> |.....................| 1.461 | 1.207 | 1.346 |...........| +#> | X|<span style='font-weight: bold;'> 480.75892</span> | 94.03 | 0.003704 | 0.2691 | 0.8176 | +#> |.....................| 8.212 | 1.839 | 0.6899 | 0.7784 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.207 | 1.346 |...........|</span> +#> | F| Forward Diff. | -18.29 | 0.9240 | -0.1279 | -0.008301 | +#> |.....................| -0.04619 | -7.961 | 2.584 | 0.8229 | +#> <span style='text-decoration: underline;'>|.....................| -2.311 | -2.476 | -2.253 |...........|</span> +#> |<span style='font-weight: bold;'> 33</span>| 480.73432 | 0.9997 | -1.185 | -0.9136 | -0.8922 | +#> |.....................| -0.8421 | -0.6250 | -0.9708 | -0.9758 | +#> |.....................| -0.6420 | -0.7587 | -0.6914 |...........| +#> | U| 480.73432 | 94.18 | -5.601 | -0.9989 | -0.2014 | +#> |.....................| 2.105 | 1.840 | 0.6891 | 0.7782 | +#> |.....................| 1.461 | 1.209 | 1.346 |...........| +#> | X|<span style='font-weight: bold;'> 480.73432</span> | 94.18 | 0.003695 | 0.2692 | 0.8176 | +#> |.....................| 8.211 | 1.840 | 0.6891 | 0.7782 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.209 | 1.346 |...........|</span> +#> | F| Forward Diff. | 5.056 | 0.9202 | -0.07575 | 0.002374 | +#> |.....................| 0.02179 | -7.789 | 2.502 | 2.101 | +#> <span style='text-decoration: underline;'>|.....................| -2.273 | -2.370 | -2.217 |...........|</span> +#> |<span style='font-weight: bold;'> 34</span>| 480.71449 | 0.9983 | -1.187 | -0.9135 | -0.8922 | +#> |.....................| -0.8422 | -0.6227 | -0.9719 | -0.9765 | +#> |.....................| -0.6416 | -0.7575 | -0.6908 |...........| +#> | U| 480.71449 | 94.05 | -5.602 | -0.9988 | -0.2014 | +#> |.....................| 2.105 | 1.841 | 0.6883 | 0.7776 | +#> |.....................| 1.461 | 1.210 | 1.347 |...........| +#> | X|<span style='font-weight: bold;'> 480.71449</span> | 94.05 | 0.003690 | 0.2692 | 0.8175 | +#> |.....................| 8.210 | 1.841 | 0.6883 | 0.7776 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.210 | 1.347 |...........|</span> +#> | F| Forward Diff. | -16.10 | 0.9104 | -0.1099 | -0.008208 | +#> |.....................| -0.04557 | -7.571 | 2.606 | 1.992 | +#> <span style='text-decoration: underline;'>|.....................| -2.295 | -2.312 | -2.196 |...........|</span> +#> |<span style='font-weight: bold;'> 35</span>| 480.68777 | 0.9997 | -1.189 | -0.9134 | -0.8923 | +#> |.....................| -0.8423 | -0.6220 | -0.9726 | -0.9789 | +#> |.....................| -0.6421 | -0.7569 | -0.6908 |...........| +#> | U| 480.68777 | 94.18 | -5.604 | -0.9987 | -0.2015 | +#> |.....................| 2.105 | 1.842 | 0.6877 | 0.7755 | +#> |.....................| 1.461 | 1.211 | 1.347 |...........| +#> | X|<span style='font-weight: bold;'> 480.68777</span> | 94.18 | 0.003683 | 0.2692 | 0.8175 | +#> |.....................| 8.209 | 1.842 | 0.6877 | 0.7755 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.211 | 1.347 |...........|</span> +#> | F| Forward Diff. | 4.858 | 0.9091 | -0.06076 | 0.001972 | +#> |.....................| 0.01464 | -7.318 | 2.391 | 0.7174 | +#> <span style='text-decoration: underline;'>|.....................| -2.245 | -2.255 | -2.188 |...........|</span> +#> |<span style='font-weight: bold;'> 36</span>| 480.67297 | 0.9982 | -1.190 | -0.9134 | -0.8923 | +#> |.....................| -0.8424 | -0.6196 | -0.9738 | -0.9789 | +#> |.....................| -0.6415 | -0.7559 | -0.6900 |...........| +#> | U| 480.67297 | 94.03 | -5.605 | -0.9987 | -0.2015 | +#> |.....................| 2.105 | 1.844 | 0.6868 | 0.7755 | +#> |.....................| 1.461 | 1.212 | 1.348 |...........| +#> | X|<span style='font-weight: bold;'> 480.67297</span> | 94.03 | 0.003678 | 0.2692 | 0.8175 | +#> |.....................| 8.209 | 1.844 | 0.6868 | 0.7755 | +#> <span style='text-decoration: underline;'>|.....................| 1.461 | 1.212 | 1.348 |...........|</span> +#> | F| Forward Diff. | -18.29 | 0.8994 | -0.1037 | -0.01039 | +#> |.....................| -0.05604 | -7.086 | 2.324 | 0.6431 | +#> <span style='text-decoration: underline;'>|.....................| -2.272 | -2.229 | -2.170 |...........|</span> +#> |<span style='font-weight: bold;'> 37</span>| 480.65610 | 0.9996 | -1.192 | -0.9134 | -0.8923 | +#> |.....................| -0.8424 | -0.6187 | -0.9745 | -0.9768 | +#> |.....................| -0.6410 | -0.7549 | -0.6892 |...........| +#> | U| 480.6561 | 94.17 | -5.607 | -0.9987 | -0.2015 | +#> |.....................| 2.105 | 1.845 | 0.6862 | 0.7773 | +#> |.....................| 1.462 | 1.213 | 1.348 |...........| +#> | X|<span style='font-weight: bold;'> 480.6561</span> | 94.17 | 0.003671 | 0.2692 | 0.8175 | +#> |.....................| 8.208 | 1.845 | 0.6862 | 0.7773 | +#> <span style='text-decoration: underline;'>|.....................| 1.462 | 1.213 | 1.348 |...........|</span> +#> | F| Forward Diff. | 3.523 | 0.8967 | -0.06519 |-0.0005238 | +#> |.....................| 0.007306 | -6.938 | 2.250 | 0.8205 | +#> <span style='text-decoration: underline;'>|.....................| -2.209 | -2.143 | -2.109 |...........|</span> +#> |<span style='font-weight: bold;'> 38</span>| 480.63930 | 0.9982 | -1.192 | -0.9133 | -0.8923 | +#> |.....................| -0.8425 | -0.6159 | -0.9754 | -0.9772 | +#> |.....................| -0.6401 | -0.7540 | -0.6884 |...........| +#> | U| 480.6393 | 94.04 | -5.608 | -0.9987 | -0.2015 | +#> |.....................| 2.105 | 1.847 | 0.6856 | 0.7770 | +#> |.....................| 1.463 | 1.214 | 1.349 |...........| +#> | X|<span style='font-weight: bold;'> 480.6393</span> | 94.04 | 0.003670 | 0.2692 | 0.8175 | +#> |.....................| 8.208 | 1.847 | 0.6856 | 0.7770 | +#> <span style='text-decoration: underline;'>|.....................| 1.463 | 1.214 | 1.349 |...........|</span> +#> | F| Forward Diff. | -17.45 | 0.8903 | -0.1044 | -0.01155 | +#> |.....................| -0.05881 | -6.641 | 2.195 | 1.966 | +#> <span style='text-decoration: underline;'>|.....................| -2.207 | -2.119 | -2.090 |...........|</span> +#> |<span style='font-weight: bold;'> 39</span>| 480.61554 | 0.9996 | -1.195 | -0.9133 | -0.8924 | +#> |.....................| -0.8426 | -0.6153 | -0.9757 | -0.9778 | +#> |.....................| -0.6400 | -0.7531 | -0.6877 |...........| +#> | U| 480.61554 | 94.16 | -5.611 | -0.9986 | -0.2016 | +#> |.....................| 2.105 | 1.848 | 0.6853 | 0.7765 | +#> |.....................| 1.463 | 1.215 | 1.350 |...........| +#> | X|<span style='font-weight: bold;'> 480.61554</span> | 94.16 | 0.003659 | 0.2692 | 0.8174 | +#> |.....................| 8.207 | 1.848 | 0.6853 | 0.7765 | +#> <span style='text-decoration: underline;'>|.....................| 1.463 | 1.215 | 1.350 |...........|</span> +#> | F| Forward Diff. | 2.395 | 0.8850 | -0.05988 | -0.001937 | +#> |.....................| 0.0008548 | -6.531 | 2.145 | 0.7341 | +#> <span style='text-decoration: underline;'>|.....................| -2.178 | -2.045 | -2.040 |...........|</span> +#> |<span style='font-weight: bold;'> 40</span>| 480.59501 | 0.9985 | -1.195 | -0.9132 | -0.8924 | +#> |.....................| -0.8426 | -0.6124 | -0.9766 | -0.9781 | +#> |.....................| -0.6390 | -0.7522 | -0.6868 |...........| +#> | U| 480.59501 | 94.06 | -5.611 | -0.9986 | -0.2016 | +#> |.....................| 2.105 | 1.850 | 0.6846 | 0.7762 | +#> |.....................| 1.464 | 1.216 | 1.351 |...........| +#> | X|<span style='font-weight: bold;'> 480.59501</span> | 94.06 | 0.003658 | 0.2692 | 0.8174 | +#> |.....................| 8.207 | 1.850 | 0.6846 | 0.7762 | +#> <span style='text-decoration: underline;'>|.....................| 1.464 | 1.216 | 1.351 |...........|</span> +#> | F| Forward Diff. | -13.20 | 0.8797 | -0.08878 | -0.01245 | +#> |.....................| -0.05202 | -6.149 | 2.097 | 1.936 | +#> <span style='text-decoration: underline;'>|.....................| -2.128 | -2.007 | -2.021 |...........|</span> +#> |<span style='font-weight: bold;'> 41</span>| 480.57374 | 0.9995 | -1.198 | -0.9132 | -0.8924 | +#> |.....................| -0.8426 | -0.6117 | -0.9768 | -0.9794 | +#> |.....................| -0.6387 | -0.7515 | -0.6862 |...........| +#> | U| 480.57374 | 94.16 | -5.614 | -0.9986 | -0.2016 | +#> |.....................| 2.105 | 1.851 | 0.6845 | 0.7751 | +#> |.....................| 1.464 | 1.217 | 1.352 |...........| +#> | X|<span style='font-weight: bold;'> 480.57374</span> | 94.16 | 0.003647 | 0.2692 | 0.8174 | +#> |.....................| 8.207 | 1.851 | 0.6845 | 0.7751 | +#> <span style='text-decoration: underline;'>|.....................| 1.464 | 1.217 | 1.352 |...........|</span> +#> |<span style='font-weight: bold;'> 42</span>| 480.55656 | 0.9993 | -1.203 | -0.9133 | -0.8924 | +#> |.....................| -0.8427 | -0.6115 | -0.9767 | -0.9815 | +#> |.....................| -0.6386 | -0.7506 | -0.6853 |...........| +#> | U| 480.55656 | 94.14 | -5.619 | -0.9986 | -0.2016 | +#> |.....................| 2.105 | 1.851 | 0.6846 | 0.7733 | +#> |.....................| 1.465 | 1.218 | 1.353 |...........| +#> | X|<span style='font-weight: bold;'> 480.55656</span> | 94.14 | 0.003629 | 0.2692 | 0.8174 | +#> |.....................| 8.206 | 1.851 | 0.6846 | 0.7733 | +#> <span style='text-decoration: underline;'>|.....................| 1.465 | 1.218 | 1.353 |...........|</span> +#> |<span style='font-weight: bold;'> 43</span>| 480.48642 | 0.9984 | -1.228 | -0.9134 | -0.8925 | +#> |.....................| -0.8432 | -0.6102 | -0.9761 | -0.9914 | +#> |.....................| -0.6380 | -0.7463 | -0.6812 |...........| +#> | U| 480.48642 | 94.05 | -5.643 | -0.9987 | -0.2017 | +#> |.....................| 2.104 | 1.852 | 0.6850 | 0.7647 | +#> |.....................| 1.465 | 1.223 | 1.357 |...........| +#> | X|<span style='font-weight: bold;'> 480.48642</span> | 94.05 | 0.003541 | 0.2692 | 0.8174 | +#> |.....................| 8.202 | 1.852 | 0.6850 | 0.7647 | +#> <span style='text-decoration: underline;'>|.....................| 1.465 | 1.223 | 1.357 |...........|</span> +#> |<span style='font-weight: bold;'> 44</span>| 480.43193 | 0.9946 | -1.325 | -0.9138 | -0.8928 | +#> |.....................| -0.8452 | -0.6054 | -0.9741 | -1.031 | +#> |.....................| -0.6354 | -0.7292 | -0.6649 |...........| +#> | U| 480.43193 | 93.70 | -5.741 | -0.9991 | -0.2020 | +#> |.....................| 2.102 | 1.856 | 0.6866 | 0.7303 | +#> |.....................| 1.469 | 1.241 | 1.376 |...........| +#> | X|<span style='font-weight: bold;'> 480.43193</span> | 93.70 | 0.003212 | 0.2691 | 0.8171 | +#> |.....................| 8.185 | 1.856 | 0.6866 | 0.7303 | +#> <span style='text-decoration: underline;'>|.....................| 1.469 | 1.241 | 1.376 |...........|</span> +#> | F| Forward Diff. | -73.68 | 0.5532 | -0.05170 | -0.03792 | +#> |.....................| -0.2632 | -4.949 | 2.751 | -2.063 | +#> <span style='text-decoration: underline;'>|.....................| -2.027 | -0.5538 | -1.006 |...........|</span> +#> |<span style='font-weight: bold;'> 45</span>| 480.12037 | 0.9986 | -1.465 | -0.9157 | -0.8935 | +#> |.....................| -0.8478 | -0.6011 | -0.9922 | -1.022 | +#> |.....................| -0.6184 | -0.7143 | -0.6451 |...........| +#> | U| 480.12037 | 94.07 | -5.880 | -1.001 | -0.2027 | +#> |.....................| 2.100 | 1.859 | 0.6728 | 0.7378 | +#> |.....................| 1.489 | 1.257 | 1.399 |...........| +#> | X|<span style='font-weight: bold;'> 480.12037</span> | 94.07 | 0.002795 | 0.2687 | 0.8166 | +#> |.....................| 8.164 | 1.859 | 0.6728 | 0.7378 | +#> <span style='text-decoration: underline;'>|.....................| 1.489 | 1.257 | 1.399 |...........|</span> +#> | F| Forward Diff. | -14.31 | 0.1919 | -0.006458 | -0.005637 | +#> |.....................| -0.1500 | -5.088 | 0.6605 | -0.1467 | +#> <span style='text-decoration: underline;'>|.....................| -1.672 | 0.02074 | -0.4009 |...........|</span> +#> |<span style='font-weight: bold;'> 46</span>| 480.21684 | 0.9998 | -1.532 | -0.9143 | -0.8951 | +#> |.....................| -0.8360 | -0.5884 | -0.9862 | -1.032 | +#> |.....................| -0.5071 | -0.7680 | -0.6684 |...........| +#> | U| 480.21684 | 94.19 | -5.947 | -0.9996 | -0.2043 | +#> |.....................| 2.112 | 1.870 | 0.6773 | 0.7298 | +#> |.....................| 1.621 | 1.199 | 1.372 |...........| +#> | X|<span style='font-weight: bold;'> 480.21684</span> | 94.19 | 0.002613 | 0.2690 | 0.8152 | +#> |.....................| 8.261 | 1.870 | 0.6773 | 0.7298 | +#> <span style='text-decoration: underline;'>|.....................| 1.621 | 1.199 | 1.372 |...........|</span> +#> |<span style='font-weight: bold;'> 47</span>| 480.06028 | 1.000 | -1.489 | -0.9152 | -0.8941 | +#> |.....................| -0.8435 | -0.5961 | -0.9901 | -1.026 | +#> |.....................| -0.5774 | -0.7340 | -0.6536 |...........| +#> | U| 480.06028 | 94.21 | -5.905 | -1.000 | -0.2033 | +#> |.....................| 2.104 | 1.863 | 0.6744 | 0.7349 | +#> |.....................| 1.538 | 1.236 | 1.389 |...........| +#> | X|<span style='font-weight: bold;'> 480.06028</span> | 94.21 | 0.002726 | 0.2688 | 0.8161 | +#> |.....................| 8.200 | 1.863 | 0.6744 | 0.7349 | +#> <span style='text-decoration: underline;'>|.....................| 1.538 | 1.236 | 1.389 |...........|</span> +#> | F| Forward Diff. | 6.437 | 0.1507 | 0.07551 | -0.008836 | +#> |.....................| 0.08632 | -3.858 | 0.8547 | 0.1963 | +#> <span style='text-decoration: underline;'>|.....................| 0.4591 | -0.8475 | -0.5830 |...........|</span> +#> |<span style='font-weight: bold;'> 48</span>| 480.03665 | 0.9987 | -1.532 | -0.9229 | -0.8934 | +#> |.....................| -0.8415 | -0.5884 | -1.015 | -1.029 | +#> |.....................| -0.5816 | -0.7442 | -0.6445 |...........| +#> | U| 480.03665 | 94.09 | -5.948 | -1.008 | -0.2026 | +#> |.....................| 2.106 | 1.870 | 0.6552 | 0.7323 | +#> |.....................| 1.533 | 1.225 | 1.399 |...........| +#> | X|<span style='font-weight: bold;'> 480.03665</span> | 94.09 | 0.002612 | 0.2673 | 0.8166 | +#> |.....................| 8.216 | 1.870 | 0.6552 | 0.7323 | +#> <span style='text-decoration: underline;'>|.....................| 1.533 | 1.225 | 1.399 |...........|</span> +#> | F| Forward Diff. | -11.33 | 0.04720 | -0.3576 | -0.009993 | +#> |.....................| 0.09366 | -3.049 | -0.8552 | 2.379 | +#> <span style='text-decoration: underline;'>|.....................| 0.07272 | -1.673 | -0.4189 |...........|</span> +#> |<span style='font-weight: bold;'> 49</span>| 480.00388 | 0.9997 | -1.574 | -0.9191 | -0.8927 | +#> |.....................| -0.8426 | -0.5789 | -1.009 | -1.024 | +#> |.....................| -0.5828 | -0.7165 | -0.6339 |...........| +#> | U| 480.00388 | 94.18 | -5.990 | -1.004 | -0.2019 | +#> |.....................| 2.105 | 1.878 | 0.6600 | 0.7361 | +#> |.....................| 1.531 | 1.255 | 1.412 |...........| +#> | X|<span style='font-weight: bold;'> 480.00388</span> | 94.18 | 0.002504 | 0.2681 | 0.8172 | +#> |.....................| 8.207 | 1.878 | 0.6600 | 0.7361 | +#> <span style='text-decoration: underline;'>|.....................| 1.531 | 1.255 | 1.412 |...........|</span> +#> | F| Forward Diff. | 1.604 | -0.07853 | -0.1199 | 0.02191 | +#> |.....................| 0.1056 | -1.650 | -0.4080 | 0.6580 | +#> <span style='text-decoration: underline;'>|.....................| 0.2834 | 0.2201 | 0.3460 |...........|</span> +#> |<span style='font-weight: bold;'> 50</span>| 480.03472 | 1.000 | -1.551 | -0.8873 | -0.8972 | +#> |.....................| -0.8660 | -0.5703 | -1.019 | -1.030 | +#> |.....................| -0.5914 | -0.7201 | -0.6545 |...........| +#> | U| 480.03472 | 94.21 | -5.967 | -0.9727 | -0.2064 | +#> |.....................| 2.082 | 1.885 | 0.6528 | 0.7314 | +#> |.....................| 1.521 | 1.251 | 1.388 |...........| +#> | X|<span style='font-weight: bold;'> 480.03472</span> | 94.21 | 0.002563 | 0.2743 | 0.8135 | +#> |.....................| 8.017 | 1.885 | 0.6528 | 0.7314 | +#> <span style='text-decoration: underline;'>|.....................| 1.521 | 1.251 | 1.388 |...........|</span> +#> |<span style='font-weight: bold;'> 51</span>| 480.00362 | 0.9987 | -1.569 | -0.9113 | -0.8938 | +#> |.....................| -0.8484 | -0.5757 | -1.011 | -1.026 | +#> |.....................| -0.5851 | -0.7175 | -0.6392 |...........| +#> | U| 480.00362 | 94.09 | -5.984 | -0.9966 | -0.2030 | +#> |.....................| 2.099 | 1.880 | 0.6585 | 0.7346 | +#> |.....................| 1.528 | 1.254 | 1.406 |...........| +#> | X|<span style='font-weight: bold;'> 480.00362</span> | 94.09 | 0.002519 | 0.2696 | 0.8163 | +#> |.....................| 8.160 | 1.880 | 0.6585 | 0.7346 | +#> <span style='text-decoration: underline;'>|.....................| 1.528 | 1.254 | 1.406 |...........|</span> +#> | F| Forward Diff. | -11.27 | -0.06004 | 0.2734 | -0.003181 | +#> |.....................| -0.1459 | -1.804 | -0.6958 | 0.2356 | +#> <span style='text-decoration: underline;'>|.....................| -0.08489 | -0.1057 | -0.1437 |...........|</span> +#> |<span style='font-weight: bold;'> 52</span>| 479.99564 | 1.000 | -1.563 | -0.9133 | -0.8943 | +#> |.....................| -0.8490 | -0.5744 | -1.010 | -1.027 | +#> |.....................| -0.5870 | -0.7192 | -0.6381 |...........| +#> | U| 479.99564 | 94.21 | -5.979 | -0.9986 | -0.2035 | +#> |.....................| 2.099 | 1.881 | 0.6592 | 0.7342 | +#> |.....................| 1.526 | 1.252 | 1.407 |...........| +#> | X|<span style='font-weight: bold;'> 479.99564</span> | 94.21 | 0.002532 | 0.2692 | 0.8159 | +#> |.....................| 8.155 | 1.881 | 0.6592 | 0.7342 | +#> <span style='text-decoration: underline;'>|.....................| 1.526 | 1.252 | 1.407 |...........|</span> +#> | F| Forward Diff. | 5.442 | -0.04353 | 0.2015 | -0.005586 | +#> |.....................| -0.1078 | -1.130 | -0.4765 | -0.6210 | +#> <span style='text-decoration: underline;'>|.....................| 0.09560 | 0.04932 | 0.1423 |...........|</span> +#> |<span style='font-weight: bold;'> 53</span>| 479.99256 | 0.9995 | -1.560 | -0.9178 | -0.8945 | +#> |.....................| -0.8473 | -0.5732 | -1.008 | -1.026 | +#> |.....................| -0.5881 | -0.7196 | -0.6366 |...........| +#> | U| 479.99256 | 94.16 | -5.975 | -1.003 | -0.2037 | +#> |.....................| 2.100 | 1.882 | 0.6609 | 0.7344 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99256</span> | 94.16 | 0.002541 | 0.2683 | 0.8157 | +#> |.....................| 8.169 | 1.882 | 0.6609 | 0.7344 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> | F| Forward Diff. | -1.663 | -0.03616 | -0.04918 | -0.01811 | +#> |.....................| -0.07323 | -1.616 | -0.5475 | -0.9126 | +#> <span style='text-decoration: underline;'>|.....................| -0.2713 | -0.2260 | -0.04317 |...........|</span> +#> |<span style='font-weight: bold;'> 54</span>| 479.99337 | 0.9995 | -1.558 | -0.9178 | -0.8940 | +#> |.....................| -0.8453 | -0.5718 | -1.004 | -1.025 | +#> |.....................| -0.5887 | -0.7198 | -0.6325 |...........| +#> | U| 479.99337 | 94.16 | -5.974 | -1.003 | -0.2032 | +#> |.....................| 2.102 | 1.883 | 0.6641 | 0.7358 | +#> |.....................| 1.524 | 1.251 | 1.413 |...........| +#> | X|<span style='font-weight: bold;'> 479.99337</span> | 94.16 | 0.002545 | 0.2683 | 0.8161 | +#> |.....................| 8.185 | 1.883 | 0.6641 | 0.7358 | +#> <span style='text-decoration: underline;'>|.....................| 1.524 | 1.251 | 1.413 |...........|</span> +#> |<span style='font-weight: bold;'> 55</span>| 479.99257 | 0.9996 | -1.559 | -0.9178 | -0.8942 | +#> |.....................| -0.8464 | -0.5725 | -1.006 | -1.026 | +#> |.....................| -0.5884 | -0.7197 | -0.6348 |...........| +#> | U| 479.99257 | 94.17 | -5.975 | -1.003 | -0.2035 | +#> |.....................| 2.101 | 1.883 | 0.6623 | 0.7351 | +#> |.....................| 1.525 | 1.252 | 1.411 |...........| +#> | X|<span style='font-weight: bold;'> 479.99257</span> | 94.17 | 0.002543 | 0.2683 | 0.8159 | +#> |.....................| 8.175 | 1.883 | 0.6623 | 0.7351 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.411 |...........|</span> +#> |<span style='font-weight: bold;'> 56</span>| 479.99255 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99255 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99255</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> | C| Central Diff. | 1.014 | -0.03924 | -0.07311 | -0.03520 | +#> |.....................| -0.07193 | -1.047 | -0.3482 | -0.6653 | +#> <span style='text-decoration: underline;'>|.....................| -0.001386 | 0.002313 | -0.01832 |...........|</span> +#> |<span style='font-weight: bold;'> 57</span>| 479.99382 | 0.9993 | -1.559 | -0.9177 | -0.8943 | +#> |.....................| -0.8469 | -0.5723 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99382 | 94.14 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6617 | 0.7350 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99382</span> | 94.14 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6617 | 0.7350 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 58</span>| 479.99260 | 0.9996 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5726 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.9926 | 94.17 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.9926</span> | 94.17 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 59</span>| 479.99255 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99255 | 94.17 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99255</span> | 94.17 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 60</span>| 479.99254 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99254 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99254</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> | C| Central Diff. | 0.7083 | -0.03937 | -0.07377 | -0.03537 | +#> |.....................| -0.07427 | -1.038 | -0.3482 | -0.6698 | +#> <span style='text-decoration: underline;'>|.....................| -0.009774 | 0.01032 | -0.01719 |...........|</span> +#> |<span style='font-weight: bold;'> 61</span>| 479.99255 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99255 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99255</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 62</span>| 479.99264 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99264 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99264</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 63</span>| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99259</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 64</span>| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99259</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 65</span>| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99259</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 66</span>| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99259</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 67</span>| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99259</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 68</span>| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99259</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 69</span>| 479.99258 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99258 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99258</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 70</span>| 479.99258 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99258 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99258</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</span> +#> |<span style='font-weight: bold;'> 71</span>| 479.99258 | 0.9997 | -1.559 | -0.9178 | -0.8944 | +#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | +#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| +#> | U| 479.99258 | 94.18 | -5.975 | -1.003 | -0.2036 | +#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | +#> |.....................| 1.525 | 1.252 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 479.99258</span> | 94.18 | 0.002542 | 0.2683 | 0.8158 | +#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | +#> <span style='text-decoration: underline;'>|.....................| 1.525 | 1.252 | 1.409 |...........|</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: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</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: gradient problems with initial estimate and covariance; see $scaleInfo</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'>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_A1 |f_parent_qlogis | log_k1 | +#> |.....................| log_k2 | g_qlogis | sigma | o1 | +#> |.....................| o2 | o3 | o4 | o5 | +#> <span style='text-decoration: underline;'>|.....................| o6 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 514.27068 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 514.27068 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 514.27068</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | G| Gill Diff. | 26.19 | 1.724 | -0.1273 | 0.01210 | +#> |.....................| -0.2599 | 0.04964 | -46.10 | 17.02 | +#> |.....................| 9.682 | -11.00 | -4.182 | 3.869 | +#> <span style='text-decoration: underline;'>|.....................| -10.57 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 1072.3430 | 0.5548 | -1.029 | -0.9091 | -0.9298 | +#> |.....................| -0.9733 | -0.8898 | -0.07504 | -1.166 | +#> |.....................| -1.039 | -0.6809 | -0.8005 | -0.9394 | +#> <span style='text-decoration: underline;'>|.....................| -0.6887 |...........|...........|...........|</span> +#> | U| 1072.343 | 52.05 | -5.403 | -0.9690 | -1.880 | +#> |.....................| -4.266 | 0.1355 | 2.292 | 0.5199 | +#> |.....................| 0.7209 | 1.403 | 1.065 | 0.8339 | +#> <span style='text-decoration: underline;'>|.....................| 1.368 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 1072.343</span> | 52.05 | 0.004504 | 0.2751 | 0.1526 | +#> |.....................| 0.01403 | 0.5338 | 2.292 | 0.5199 | +#> |.....................| 0.7209 | 1.403 | 1.065 | 0.8339 | +#> <span style='text-decoration: underline;'>|.....................| 1.368 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 539.25377 | 0.9555 | -1.003 | -0.9110 | -0.9296 | +#> |.....................| -0.9773 | -0.8890 | -0.7801 | -0.9058 | +#> |.....................| -0.8907 | -0.8491 | -0.8645 | -0.8802 | +#> <span style='text-decoration: underline;'>|.....................| -0.8503 |...........|...........|...........|</span> +#> | U| 539.25377 | 89.63 | -5.376 | -0.9709 | -1.880 | +#> |.....................| -4.270 | 0.1356 | 1.712 | 0.7103 | +#> |.....................| 0.8487 | 1.204 | 1.001 | 0.8867 | +#> <span style='text-decoration: underline;'>|.....................| 1.181 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 539.25377</span> | 89.63 | 0.004625 | 0.2747 | 0.1526 | +#> |.....................| 0.01398 | 0.5339 | 1.712 | 0.7103 | +#> |.....................| 0.8487 | 1.204 | 1.001 | 0.8867 | +#> <span style='text-decoration: underline;'>|.....................| 1.181 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 527.20532 | 0.9955 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9777 | -0.8889 | -0.8506 | -0.8798 | +#> |.....................| -0.8759 | -0.8659 | -0.8709 | -0.8743 | +#> <span style='text-decoration: underline;'>|.....................| -0.8665 |...........|...........|...........|</span> +#> | U| 527.20532 | 93.39 | -5.374 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.654 | 0.7293 | +#> |.....................| 0.8615 | 1.184 | 0.9947 | 0.8920 | +#> <span style='text-decoration: underline;'>|.....................| 1.162 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.20532</span> | 93.39 | 0.004637 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.654 | 0.7293 | +#> |.....................| 0.8615 | 1.184 | 0.9947 | 0.8920 | +#> <span style='text-decoration: underline;'>|.....................| 1.162 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 527.55150 | 0.9996 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8576 | -0.8772 | +#> |.....................| -0.8744 | -0.8676 | -0.8715 | -0.8737 | +#> <span style='text-decoration: underline;'>|.....................| -0.8681 |...........|...........|...........|</span> +#> | U| 527.5515 | 93.77 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.648 | 0.7312 | +#> |.....................| 0.8628 | 1.182 | 0.9941 | 0.8925 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.5515</span> | 93.77 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.648 | 0.7312 | +#> |.....................| 0.8628 | 1.182 | 0.9941 | 0.8925 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 527.60332 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8743 | -0.8678 | -0.8716 | -0.8737 | +#> <span style='text-decoration: underline;'>|.....................| -0.8682 |...........|...........|...........|</span> +#> | U| 527.60332 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60332</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 527.60868 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60868 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60868</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 527.60932 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60932 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60932</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 527.60939 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60939 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60939</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 527.60940 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.6094 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.6094</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 527.60940 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.6094 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.6094</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 527.60940 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.6094 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.6094</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60941</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60941</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60941</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60941</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | +#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | +#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | +#> <span style='text-decoration: underline;'>|.....................| -0.8683 |...........|...........|...........|</span> +#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | +#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.60941</span> | 93.81 | 0.004638 | 0.2747 | 0.1526 | +#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | +#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 |...........|...........|...........|</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='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: 1.288 0.09 1.379</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> -#> <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[5]+THETA[5];</span> -#> <span class='message'>rx_expr_12~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_14~t*rx_expr_12;</span> -#> <span class='message'>rx_expr_15~1+rx_expr_14;</span> -#> <span class='message'>rx_expr_17~rx_expr_7-(rx_expr_8);</span> -#> <span class='message'>rx_expr_19~exp(rx_expr_17);</span> -#> <span class='message'>d/dt(parent)=-rx_expr_19*parent/(rx_expr_15);</span> -#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> -#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> -#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_19*parent*f_parent_to_A1/(rx_expr_15);</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_13~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_13+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_13+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_16~rx_expr_10*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_r_=(rx_expr_0)*Rx_pow_di(THETA[7],2)+(rx_expr_2)*(rx_expr_1)*Rx_pow_di(THETA[6],2);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=THETA[2];</span> -#> <span class='message'>f_parent_qlogis=THETA[3];</span> -#> <span class='message'>log_alpha=THETA[4];</span> -#> <span class='message'>log_beta=THETA[5];</span> -#> <span class='message'>sigma_parent=THETA[6];</span> -#> <span class='message'>sigma_A1=THETA[7];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=ETA[2];</span> -#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> -#> <span class='message'>eta.log_alpha=ETA[4];</span> -#> <span class='message'>eta.log_beta=ETA[5];</span> -#> <span class='message'>parent_0_model=rx_expr_6;</span> -#> <span class='message'>k_A1=rx_expr_11;</span> -#> <span class='message'>alpha=exp(rx_expr_7);</span> -#> <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: 6.666 0.38 7.044</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: 1.39 0.093 1.483</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> -#> <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_17~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_19~1+rx_expr_17;</span> -#> <span class='message'>rx_expr_24~1/(rx_expr_19);</span> -#> <span class='message'>rx_expr_26~(rx_expr_24);</span> -#> <span class='message'>rx_expr_27~1-rx_expr_26;</span> -#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</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(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</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_18~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_18+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_18+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_22~rx_expr_11*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_r_=(rx_expr_0)*Rx_pow_di(THETA[8],2)+(rx_expr_2)*(rx_expr_1)*Rx_pow_di(THETA[7],2);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=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_parent=THETA[7];</span> -#> <span class='message'>sigma_A1=THETA[8];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=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_A1=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_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: 14.67 0.529 15.2</span></div><div class='input'> +</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: 93.6104 -5.6552 -0.1308 2.1755 -1.1174 2.9315 1.6064 0.6616 0.5897 0.4753 9.7765 10.2253 +#> 2: 93.8838 -5.6936 -0.1062 2.2361 -1.0529 2.7849 1.5260 0.6285 0.5602 0.4515 7.9206 5.2721 +#> 3: 93.9304 -5.7260 -0.0940 2.2480 -1.0317 2.6457 1.4889 0.5971 0.5322 0.4290 7.5051 3.6573 +#> 4: 93.6107 -5.7914 -0.0929 2.2382 -1.0171 2.5134 2.0027 0.5676 0.5056 0.4075 7.3763 3.1438 +#> 5: 93.7262 -5.7517 -0.0926 2.2365 -1.0306 2.3877 1.9026 0.5679 0.4803 0.3871 7.2914 3.0275 +#> 6: 93.7261 -5.7719 -0.0823 2.2625 -1.0391 2.2683 2.1168 0.5638 0.4563 0.3678 7.0857 2.8196 +#> 7: 93.5991 -5.8553 -0.0917 2.2659 -1.0146 2.1549 2.3708 0.5618 0.4335 0.3494 6.9413 2.7447 +#> 8: 93.4288 -5.8969 -0.0885 2.2757 -1.0253 2.1183 2.4324 0.5615 0.4118 0.3319 7.2269 2.6781 +#> 9: 93.4049 -6.1188 -0.0863 2.2841 -1.0154 2.0124 3.0090 0.5633 0.3912 0.3153 7.2084 2.7464 +#> 10: 93.4773 -6.1940 -0.0816 2.2893 -1.0174 1.9958 3.6308 0.5540 0.3716 0.2996 7.2414 2.8980 +#> 11: 93.5334 -6.1739 -0.0772 2.2901 -1.0479 2.2841 3.4492 0.5567 0.3531 0.2846 7.0567 2.8159 +#> 12: 93.5824 -6.3716 -0.0875 2.2706 -1.0452 2.1699 4.3087 0.5505 0.3354 0.2704 7.2970 2.3790 +#> 13: 93.8528 -6.3302 -0.0846 2.2564 -1.0302 2.0614 4.6014 0.5475 0.3186 0.2568 7.3901 2.1942 +#> 14: 94.0343 -6.1408 -0.0887 2.2666 -1.0280 1.9995 4.3714 0.5202 0.3027 0.2440 7.1696 2.0730 +#> 15: 94.1712 -6.3900 -0.0759 2.2825 -1.0112 1.8995 5.0913 0.5358 0.2876 0.2318 7.2155 2.0259 +#> 16: 93.9481 -6.1284 -0.0798 2.2707 -1.0264 1.8046 4.8368 0.5501 0.2732 0.2202 7.2731 2.0912 +#> 17: 93.7828 -6.2736 -0.0852 2.2870 -1.0249 1.7143 4.5949 0.5408 0.2595 0.2092 7.0213 2.0417 +#> 18: 93.8758 -6.3616 -0.0851 2.2713 -1.0157 1.8699 4.9132 0.5349 0.2465 0.1987 7.0613 1.8601 +#> 19: 93.7565 -6.5413 -0.0866 2.2695 -1.0166 2.5251 5.9754 0.5312 0.2547 0.1888 7.2555 1.7947 +#> 20: 93.7233 -6.3942 -0.0970 2.2620 -1.0195 2.3989 5.6766 0.5484 0.2576 0.1794 7.0292 1.8687 +#> 21: 93.8298 -6.2619 -0.0974 2.2570 -1.0118 2.2789 5.3928 0.5497 0.2545 0.1704 6.7138 1.8157 +#> 22: 93.9520 -6.1633 -0.0874 2.2777 -1.0274 2.1650 5.1232 0.5437 0.2641 0.1622 6.8254 1.8443 +#> 23: 93.8442 -6.3255 -0.0855 2.2568 -1.0151 2.1243 4.9615 0.5334 0.2885 0.1556 6.8049 1.8073 +#> 24: 93.9659 -6.5470 -0.0855 2.2572 -1.0178 2.0788 6.2156 0.5425 0.2834 0.1583 6.9598 1.8686 +#> 25: 94.3004 -6.4881 -0.0920 2.2371 -1.0187 3.2507 5.9048 0.5367 0.2872 0.1609 6.8709 1.8839 +#> 26: 94.1750 -6.4437 -0.0964 2.2337 -1.0301 3.1136 5.6096 0.5307 0.2820 0.1611 6.5948 1.8742 +#> 27: 94.6007 -6.3072 -0.0750 2.2936 -1.0343 3.9844 5.3291 0.5042 0.2679 0.1695 6.7524 1.8335 +#> 28: 94.4915 -6.1389 -0.0826 2.2730 -1.0223 3.7852 5.0626 0.4998 0.2590 0.1812 6.4646 1.8937 +#> 29: 94.1900 -6.1516 -0.0836 2.2680 -1.0287 3.7861 4.8095 0.4976 0.2612 0.1875 6.4674 1.8998 +#> 30: 94.6632 -6.0574 -0.0773 2.2637 -1.0280 3.5968 4.5690 0.4948 0.2525 0.2040 6.5945 1.9022 +#> 31: 94.3460 -6.1684 -0.0761 2.2677 -1.0276 3.4170 4.3406 0.4901 0.2690 0.2038 6.9918 1.8446 +#> 32: 94.4385 -5.9347 -0.0751 2.2893 -1.0146 3.3283 4.1235 0.4882 0.2576 0.2002 6.7622 1.7754 +#> 33: 94.7021 -5.9329 -0.0787 2.2987 -1.0108 3.3485 3.9174 0.4859 0.2640 0.1941 6.9648 1.8014 +#> 34: 94.4058 -6.0311 -0.0692 2.2980 -1.0125 3.1811 3.7215 0.4994 0.2676 0.1936 6.9791 1.7561 +#> 35: 94.4503 -6.0470 -0.0692 2.2950 -1.0100 3.5600 3.7611 0.4994 0.2637 0.1928 6.8010 1.7890 +#> 36: 94.3400 -6.0339 -0.0792 2.2960 -1.0204 3.3820 3.5731 0.4822 0.2638 0.1887 6.6462 1.6763 +#> 37: 94.1497 -6.0221 -0.0879 2.2653 -1.0073 3.2129 3.3944 0.4979 0.2506 0.1793 6.4853 1.7911 +#> 38: 94.1574 -5.8638 -0.0884 2.2752 -1.0156 3.0523 3.2247 0.4992 0.2435 0.1772 6.4329 1.7707 +#> 39: 94.1680 -5.9558 -0.0948 2.2535 -1.0205 2.8997 3.0635 0.5065 0.2448 0.1819 6.4462 1.8100 +#> 40: 94.0516 -6.0814 -0.0881 2.2531 -1.0356 2.7547 3.4976 0.4949 0.2515 0.1827 6.4734 1.8133 +#> 41: 94.1522 -6.1880 -0.0849 2.2618 -1.0230 2.6170 4.1610 0.5129 0.2389 0.1797 6.4165 1.7782 +#> 42: 94.2178 -6.1829 -0.0854 2.2791 -1.0325 2.8092 4.1174 0.5052 0.2288 0.1853 6.4332 1.7883 +#> 43: 93.9083 -6.1600 -0.0831 2.2860 -1.0350 2.9631 3.9116 0.4914 0.2310 0.1826 6.4865 1.8449 +#> 44: 93.9636 -6.1494 -0.0824 2.2903 -1.0150 2.8149 3.7221 0.4921 0.2214 0.1805 6.4818 1.9385 +#> 45: 93.9937 -6.2329 -0.0895 2.2832 -1.0157 4.2815 4.5622 0.5075 0.2250 0.1796 6.4098 1.8355 +#> 46: 93.8001 -6.1784 -0.0944 2.2664 -1.0212 4.0674 4.3341 0.5023 0.2274 0.1795 6.5539 1.7875 +#> 47: 93.8997 -6.3400 -0.0945 2.2627 -1.0183 3.8641 4.9860 0.5017 0.2312 0.1834 6.5497 1.7838 +#> 48: 93.7861 -6.3496 -0.0944 2.2713 -1.0255 3.6709 5.3403 0.5025 0.2197 0.1839 6.1766 1.9080 +#> 49: 93.7128 -6.3914 -0.0944 2.2752 -1.0137 3.4873 5.6007 0.5051 0.2198 0.1788 6.3050 1.8320 +#> 50: 94.1645 -6.3056 -0.0945 2.2755 -1.0062 3.3130 5.3207 0.4998 0.2176 0.1781 6.4998 1.8516 +#> 51: 93.9897 -6.1556 -0.1026 2.2633 -1.0097 3.1473 5.0547 0.4853 0.2439 0.1796 6.3184 1.7981 +#> 52: 93.7604 -6.2264 -0.1068 2.2485 -0.9936 2.9899 4.8209 0.4887 0.2542 0.1793 6.5076 1.7916 +#> 53: 93.8821 -6.5447 -0.1049 2.2546 -1.0020 2.8404 6.5603 0.4701 0.2556 0.1789 6.5735 1.7763 +#> 54: 93.8865 -6.4028 -0.1081 2.2507 -1.0162 2.6984 6.2323 0.4724 0.2576 0.1846 6.3607 1.8295 +#> 55: 94.0120 -6.5455 -0.0986 2.2728 -1.0119 2.5635 6.3983 0.4550 0.2686 0.1773 6.6815 1.7869 +#> 56: 94.1921 -6.6581 -0.0953 2.2713 -1.0151 2.4353 8.2169 0.4478 0.2675 0.1763 6.6257 1.7873 +#> 57: 93.8812 -6.4499 -0.1081 2.2447 -1.0182 2.3136 7.8060 0.4683 0.2562 0.1804 6.2421 1.8455 +#> 58: 93.9830 -6.5112 -0.1092 2.2436 -1.0136 2.1979 7.4157 0.4695 0.2569 0.1762 6.3196 1.8224 +#> 59: 93.8537 -6.6528 -0.1105 2.2390 -1.0089 2.0880 9.0039 0.4689 0.2534 0.1692 6.3735 1.8049 +#> 60: 93.7399 -6.4780 -0.1212 2.2263 -0.9979 1.9836 8.5537 0.4565 0.2445 0.1696 6.4748 1.8439 +#> 61: 93.8180 -6.4608 -0.1243 2.2275 -1.0039 1.8844 8.1260 0.4630 0.2414 0.1693 6.3936 1.7653 +#> 62: 93.5774 -6.3127 -0.1298 2.2250 -1.0022 1.7902 7.7197 0.4711 0.2452 0.1708 6.5708 1.8014 +#> 63: 93.5731 -6.2060 -0.1327 2.2213 -1.0031 1.7007 7.3337 0.4685 0.2426 0.1712 6.4933 1.8318 +#> 64: 93.3587 -6.2299 -0.1316 2.2290 -1.0004 1.6302 6.9671 0.4694 0.2460 0.1710 6.2584 1.8361 +#> 65: 93.2982 -6.1900 -0.1354 2.2341 -0.9963 1.5487 6.6187 0.4685 0.2482 0.1750 6.0950 1.8341 +#> 66: 93.4532 -6.2107 -0.1251 2.2254 -0.9786 1.4713 6.2878 0.4822 0.2489 0.1701 6.3732 1.7951 +#> 67: 93.5878 -6.1823 -0.1208 2.2455 -0.9766 1.3977 5.9734 0.4860 0.2407 0.1668 6.4456 1.8371 +#> 68: 93.5819 -5.9209 -0.1200 2.2599 -0.9792 1.3278 5.6747 0.4793 0.2412 0.1686 6.5728 1.8144 +#> 69: 93.4002 -6.1142 -0.1242 2.2542 -0.9878 1.4433 5.3910 0.4730 0.2511 0.1830 6.3888 1.7900 +#> 70: 93.2631 -6.1875 -0.1271 2.2639 -0.9844 1.5244 5.1214 0.4711 0.2444 0.1770 6.5093 1.7117 +#> 71: 93.2629 -6.2944 -0.1275 2.2418 -0.9805 1.4481 4.8654 0.4612 0.2522 0.1748 6.4659 1.8500 +#> 72: 93.0324 -6.2727 -0.1332 2.2421 -0.9766 1.3757 5.1467 0.4519 0.2524 0.1673 6.3452 1.8054 +#> 73: 93.0174 -6.4402 -0.1391 2.2320 -0.9795 1.3069 6.1963 0.4480 0.2563 0.1637 6.3915 1.8506 +#> 74: 93.0073 -6.4286 -0.1450 2.2241 -0.9962 1.2416 6.0011 0.4510 0.2461 0.1682 6.6924 1.8302 +#> 75: 93.2607 -6.5056 -0.1379 2.2233 -0.9926 1.1795 6.0508 0.4573 0.2540 0.1669 6.4813 1.7896 +#> 76: 93.2937 -6.1637 -0.1404 2.2228 -0.9970 1.1205 5.7483 0.4588 0.2529 0.1656 6.3781 1.7976 +#> 77: 93.2223 -6.1702 -0.1381 2.2200 -0.9858 1.4369 5.4609 0.4633 0.2585 0.1697 6.3510 1.8749 +#> 78: 93.3189 -6.1924 -0.1355 2.2238 -0.9944 1.3651 5.1878 0.4608 0.2631 0.1612 6.1888 1.7669 +#> 79: 93.2417 -6.6345 -0.1335 2.2340 -0.9865 1.2968 7.3486 0.4570 0.2564 0.1532 6.0902 1.7505 +#> 80: 93.3476 -6.3069 -0.1305 2.2319 -0.9880 1.6281 6.9812 0.4649 0.2525 0.1514 6.0659 1.7582 +#> 81: 93.4798 -6.3145 -0.1253 2.2468 -0.9989 1.9108 6.6321 0.4447 0.2583 0.1579 6.0843 1.7959 +#> 82: 93.2745 -6.2461 -0.1184 2.2529 -0.9937 1.8153 6.3005 0.4439 0.2602 0.1691 6.2826 1.7896 +#> 83: 93.4628 -6.3953 -0.1189 2.2640 -0.9880 1.7245 6.1094 0.4430 0.2612 0.1709 6.4474 1.6820 +#> 84: 93.3664 -6.2885 -0.1105 2.2675 -0.9875 1.6383 6.1170 0.4498 0.2689 0.1719 6.4847 1.6731 +#> 85: 93.5090 -6.3029 -0.1095 2.2709 -0.9898 1.6666 6.1406 0.4365 0.2693 0.1710 6.2452 1.6594 +#> 86: 93.5097 -6.2256 -0.1106 2.2701 -0.9928 1.5833 6.2468 0.4365 0.2749 0.1632 6.2007 1.7178 +#> 87: 93.5165 -6.3038 -0.1046 2.2731 -0.9877 1.5041 5.9345 0.4398 0.2667 0.1603 6.3928 1.7003 +#> 88: 93.3766 -6.2723 -0.1071 2.2771 -0.9881 1.4289 5.6378 0.4241 0.2538 0.1598 6.1043 1.6772 +#> 89: 93.4448 -6.0430 -0.1102 2.2781 -0.9725 1.3575 5.3559 0.4187 0.2915 0.1518 6.0153 1.7593 +#> 90: 93.2843 -6.1065 -0.1089 2.2866 -0.9705 1.5362 5.0881 0.4203 0.2844 0.1656 5.9235 1.6631 +#> 91: 93.4159 -6.0210 -0.1095 2.2879 -0.9798 2.1371 4.8337 0.4245 0.2857 0.1573 5.9182 1.7482 +#> 92: 93.3198 -6.2526 -0.1075 2.2919 -0.9791 2.0303 4.7352 0.4159 0.2918 0.1590 6.0853 1.6755 +#> 93: 93.3269 -6.1838 -0.1173 2.2809 -0.9999 1.9287 4.4985 0.4211 0.2893 0.1684 6.1189 1.6734 +#> 94: 93.2077 -6.1086 -0.1148 2.2890 -0.9918 2.1061 4.2736 0.4230 0.2802 0.1662 5.9328 1.7116 +#> 95: 93.0207 -6.1510 -0.1170 2.2665 -0.9791 2.1360 4.0630 0.4199 0.2937 0.1734 6.1415 1.6737 +#> 96: 93.2134 -6.1614 -0.1152 2.2861 -0.9711 2.5372 4.1579 0.4211 0.2790 0.1647 6.1575 1.6338 +#> 97: 93.1425 -6.2333 -0.1140 2.2912 -0.9665 2.4103 4.4551 0.4136 0.2835 0.1645 6.0790 1.6652 +#> 98: 92.9412 -6.2651 -0.1167 2.2847 -0.9738 2.2898 4.7233 0.4095 0.2882 0.1836 5.9305 1.6158 +#> 99: 92.9087 -6.1870 -0.1177 2.2833 -0.9744 2.1753 4.4872 0.4142 0.2913 0.1876 5.9838 1.7003 +#> 100: 92.7788 -6.2113 -0.1146 2.2928 -0.9939 2.0665 4.4195 0.4109 0.2945 0.1866 6.0195 1.7275 +#> 101: 92.8783 -6.0718 -0.1080 2.2959 -0.9968 1.9632 4.1985 0.4142 0.2966 0.1778 6.2542 1.6844 +#> 102: 93.0451 -6.3706 -0.1086 2.2894 -0.9974 1.8650 5.2121 0.4135 0.3030 0.1769 6.2204 1.6281 +#> 103: 93.2901 -6.4069 -0.1066 2.2943 -0.9896 1.7718 5.7453 0.4152 0.2879 0.1818 6.0239 1.7299 +#> 104: 93.3437 -6.3694 -0.1063 2.2769 -0.9914 1.6832 5.8903 0.4210 0.2884 0.1855 6.1116 1.7415 +#> 105: 93.4609 -6.2767 -0.1060 2.2751 -1.0157 1.5990 5.5958 0.4214 0.2865 0.1841 6.1287 1.7322 +#> 106: 93.5833 -6.2340 -0.1006 2.2879 -1.0084 1.8669 5.3160 0.4272 0.2982 0.1829 6.0211 1.6726 +#> 107: 93.7800 -6.1505 -0.0948 2.2685 -1.0219 1.7735 5.0502 0.4325 0.2841 0.1753 5.8556 1.7636 +#> 108: 93.8532 -6.3744 -0.0938 2.2650 -1.0210 2.0297 5.7080 0.4307 0.2836 0.1701 6.0669 1.6804 +#> 109: 93.8994 -6.3544 -0.0829 2.2862 -1.0287 1.9282 5.4226 0.4184 0.3113 0.1789 6.2343 1.6667 +#> 110: 94.0150 -6.5609 -0.0905 2.2821 -1.0088 2.1118 6.8121 0.4276 0.3275 0.1845 6.1640 1.6706 +#> 111: 93.7887 -6.0185 -0.0925 2.2831 -1.0097 2.0062 6.4715 0.4209 0.3255 0.1852 6.2823 1.6301 +#> 112: 93.9709 -6.0918 -0.0934 2.2857 -1.0067 2.2032 6.1479 0.4207 0.3285 0.1817 6.1718 1.6494 +#> 113: 93.8761 -6.3434 -0.0955 2.2919 -1.0223 2.5209 5.8405 0.4259 0.3293 0.1842 6.0377 1.6431 +#> 114: 93.6959 -6.2312 -0.0934 2.2782 -1.0154 2.3949 5.5485 0.4237 0.3460 0.1814 6.2225 1.6229 +#> 115: 93.5487 -6.0915 -0.0971 2.2836 -1.0083 2.2751 5.2711 0.4199 0.3557 0.1783 6.5929 1.6479 +#> 116: 93.5953 -6.1479 -0.1013 2.2760 -1.0018 2.1614 5.0075 0.4163 0.3399 0.1794 6.1822 1.6222 +#> 117: 93.3508 -6.1730 -0.1076 2.2632 -0.9953 2.0533 4.7571 0.4057 0.3303 0.1803 6.3444 1.7106 +#> 118: 93.4462 -5.9724 -0.1177 2.2557 -0.9963 2.0318 4.5193 0.3956 0.3349 0.1920 6.0439 1.7146 +#> 119: 93.5841 -6.0400 -0.1151 2.2480 -1.0035 1.9956 4.2933 0.3968 0.3448 0.1929 6.0754 1.6750 +#> 120: 93.4891 -6.0937 -0.1175 2.2499 -1.0006 1.8958 4.0786 0.3927 0.3392 0.1927 6.1654 1.6495 +#> 121: 93.4611 -6.1371 -0.1217 2.2538 -1.0067 1.8011 3.8747 0.3864 0.3549 0.1851 5.9558 1.6940 +#> 122: 93.4636 -6.1015 -0.1243 2.2564 -1.0002 1.7414 3.6810 0.3840 0.3557 0.1860 6.0583 1.6629 +#> 123: 93.2988 -5.9318 -0.1243 2.2601 -0.9989 2.2063 3.4969 0.3840 0.3543 0.1833 5.9686 1.5966 +#> 124: 93.4200 -5.9847 -0.1231 2.2594 -0.9991 2.0959 3.3221 0.3846 0.3544 0.1787 6.1292 1.5957 +#> 125: 93.3727 -6.1217 -0.1239 2.2584 -1.0082 1.9911 3.6395 0.3838 0.3577 0.1782 6.2794 1.6262 +#> 126: 93.4956 -6.0529 -0.1244 2.2482 -1.0096 1.8916 3.4576 0.3847 0.3505 0.1753 6.1181 1.6347 +#> 127: 93.6265 -5.9360 -0.1298 2.2342 -1.0075 1.7970 3.2847 0.3887 0.3367 0.1691 6.2315 1.7051 +#> 128: 93.4446 -6.0523 -0.1337 2.2453 -1.0079 1.7072 3.1205 0.3840 0.3302 0.1759 6.2082 1.6705 +#> 129: 93.4470 -6.0065 -0.1321 2.2321 -1.0015 1.6636 2.9644 0.3853 0.3303 0.1671 6.1479 1.6733 +#> 130: 93.3205 -5.9628 -0.1290 2.2252 -0.9954 2.0336 2.9210 0.3879 0.3284 0.1634 6.0582 1.6372 +#> 131: 93.3836 -5.8919 -0.1358 2.2375 -0.9930 2.1392 2.7749 0.3801 0.3202 0.1644 5.9972 1.6837 +#> 132: 93.1041 -5.9265 -0.1203 2.2552 -0.9929 2.0323 2.8741 0.3831 0.3353 0.1755 6.0648 1.5934 +#> 133: 93.1617 -6.0668 -0.1175 2.2538 -0.9963 1.9306 3.6825 0.3846 0.3187 0.1790 6.0732 1.5684 +#> 134: 93.1503 -6.1208 -0.1232 2.2644 -0.9851 2.3429 3.8026 0.3788 0.3296 0.1737 5.8807 1.5722 +#> 135: 92.8629 -5.9726 -0.1197 2.2650 -0.9761 2.2257 3.6124 0.3802 0.3407 0.1765 5.8408 1.5446 +#> 136: 93.1460 -6.0654 -0.1227 2.2661 -0.9736 2.1144 3.4583 0.3770 0.3434 0.1700 5.7690 1.5561 +#> 137: 93.1243 -6.2350 -0.1274 2.2472 -0.9811 2.0087 4.3526 0.3733 0.3670 0.1615 5.9377 1.5224 +#> 138: 93.1203 -6.1704 -0.1283 2.2472 -0.9891 1.9083 4.1557 0.3788 0.3671 0.1641 5.8765 1.5525 +#> 139: 93.2841 -6.0586 -0.1366 2.2404 -0.9894 1.8129 4.3184 0.3718 0.3693 0.1630 6.1854 1.6388 +#> 140: 93.4239 -6.2398 -0.1382 2.2459 -0.9713 1.7241 4.5903 0.3713 0.3627 0.1548 6.0737 1.5826 +#> 141: 93.4149 -6.1972 -0.1388 2.2605 -0.9686 2.2179 4.5557 0.3701 0.3675 0.1486 6.0793 1.5603 +#> 142: 93.4404 -5.8955 -0.1203 2.2682 -0.9706 2.1070 4.3279 0.3830 0.3719 0.1581 5.9534 1.6189 +#> 143: 93.3108 -5.8069 -0.1142 2.2835 -0.9672 2.0194 4.1115 0.3787 0.3924 0.1592 5.9410 1.5521 +#> 144: 93.3953 -5.7456 -0.1154 2.2891 -0.9553 2.2741 3.9059 0.3787 0.3849 0.1633 6.0163 1.5640 +#> 145: 93.3322 -5.8301 -0.1100 2.2926 -0.9595 2.1604 3.7106 0.3687 0.3754 0.1657 5.8968 1.5844 +#> 146: 93.0844 -5.8926 -0.1084 2.2870 -0.9605 2.0524 3.5251 0.3649 0.3713 0.1646 6.1960 1.5691 +#> 147: 93.2106 -6.0084 -0.1074 2.2931 -0.9654 1.9498 3.5341 0.3646 0.3669 0.1641 6.0548 1.5230 +#> 148: 93.2005 -6.1989 -0.1065 2.2924 -0.9740 1.8523 4.4855 0.3631 0.3660 0.1759 5.9600 1.5194 +#> 149: 93.0788 -6.2470 -0.1108 2.2861 -0.9836 2.1348 4.7630 0.3597 0.3815 0.1815 5.9584 1.5227 +#> 150: 93.2241 -6.2660 -0.1126 2.2847 -0.9912 2.1149 5.0574 0.3656 0.3788 0.1781 5.7213 1.5379 +#> 151: 93.0046 -6.5379 -0.1164 2.2757 -0.9845 2.0092 6.8660 0.3719 0.3827 0.1807 5.7612 1.5697 +#> 152: 93.2222 -6.4637 -0.1154 2.2737 -0.9950 1.6744 6.2289 0.3670 0.3881 0.1638 5.8514 1.5920 +#> 153: 93.1619 -6.3230 -0.1224 2.2638 -0.9924 1.7907 5.5429 0.3842 0.3946 0.1720 5.7562 1.5493 +#> 154: 93.0402 -6.4004 -0.1205 2.2633 -0.9868 1.7620 6.2494 0.3860 0.3891 0.1737 5.7577 1.5109 +#> 155: 93.1692 -6.4353 -0.1203 2.2696 -0.9761 1.8710 6.4519 0.3949 0.3962 0.1721 5.8348 1.4949 +#> 156: 93.2709 -6.2672 -0.1203 2.2663 -0.9708 2.1172 5.1692 0.3949 0.4187 0.1637 6.1251 1.5012 +#> 157: 93.1264 -6.1931 -0.1208 2.2728 -0.9669 1.9985 4.7739 0.3938 0.4031 0.1696 6.1014 1.5627 +#> 158: 93.1263 -6.1951 -0.1237 2.2826 -0.9729 1.7675 4.6131 0.3928 0.3904 0.1659 6.1582 1.5647 +#> 159: 92.9780 -6.2831 -0.1242 2.2726 -0.9770 1.8348 5.4674 0.3938 0.3887 0.1631 6.0622 1.5787 +#> 160: 93.1289 -6.4397 -0.1263 2.2651 -0.9675 2.4637 6.0560 0.3919 0.4017 0.1626 5.9486 1.5859 +#> 161: 93.2629 -6.3336 -0.1294 2.2670 -0.9666 2.9602 5.4966 0.3872 0.3988 0.1667 5.9034 1.5421 +#> 162: 93.1652 -6.3800 -0.1342 2.2518 -0.9754 2.8800 5.6206 0.3908 0.4158 0.1627 5.9332 1.5306 +#> 163: 93.2886 -6.4115 -0.1437 2.2330 -0.9685 1.9997 6.2760 0.4015 0.4076 0.1623 5.7905 1.5398 +#> 164: 93.4631 -6.7246 -0.1396 2.2358 -0.9854 1.8885 7.8014 0.3952 0.4028 0.1573 5.7052 1.5695 +#> 165: 93.4757 -6.8408 -0.1404 2.2346 -0.9825 2.4877 9.3632 0.3948 0.4019 0.1615 5.8406 1.5902 +#> 166: 93.9075 -6.7707 -0.1428 2.2331 -0.9848 1.9761 8.9292 0.3939 0.3909 0.1610 5.7600 1.5966 +#> 167: 93.8895 -7.1938 -0.1363 2.2449 -0.9870 2.0894 11.4058 0.3850 0.3899 0.1627 5.8501 1.5748 +#> 168: 93.5849 -6.8478 -0.1294 2.2466 -0.9888 2.3573 9.4037 0.3935 0.3808 0.1645 6.0206 1.6591 +#> 169: 93.4931 -6.4550 -0.1173 2.2727 -0.9990 2.1948 6.5738 0.3844 0.4029 0.1699 6.0990 1.6123 +#> 170: 93.7188 -6.4015 -0.1173 2.2715 -0.9981 1.8800 6.1745 0.3844 0.4001 0.1635 6.1990 1.5745 +#> 171: 93.5938 -6.4389 -0.1119 2.2663 -0.9893 2.5731 6.5397 0.3858 0.4044 0.1554 6.1636 1.5631 +#> 172: 93.4515 -6.2049 -0.1050 2.2937 -0.9701 2.6134 4.6813 0.3687 0.4017 0.1715 6.3875 1.5006 +#> 173: 93.2254 -6.2074 -0.1041 2.3111 -0.9661 2.5799 4.6939 0.3669 0.4016 0.1738 6.5633 1.5229 +#> 174: 93.4116 -6.1198 -0.1050 2.3075 -0.9711 3.0196 4.3080 0.3720 0.3988 0.1778 6.4856 1.5214 +#> 175: 93.4952 -6.0439 -0.1050 2.3008 -0.9714 3.1172 3.7728 0.3720 0.3979 0.1749 6.1918 1.4985 +#> 176: 93.6186 -6.0891 -0.1061 2.3033 -0.9794 2.1081 3.8909 0.3705 0.4029 0.1796 6.1064 1.4657 +#> 177: 93.6432 -5.9977 -0.1031 2.2953 -0.9950 1.9411 3.4156 0.3694 0.3970 0.1843 6.0473 1.4918 +#> 178: 93.5736 -6.0079 -0.0996 2.2986 -0.9809 1.7778 3.5107 0.3696 0.3909 0.1840 6.1243 1.4937 +#> 179: 93.6407 -6.0246 -0.0977 2.3042 -0.9770 2.0631 3.8144 0.3718 0.3885 0.1798 6.1851 1.5212 +#> 180: 93.6336 -5.8865 -0.0969 2.3217 -0.9871 2.2566 3.1377 0.3721 0.3715 0.1784 6.0747 1.5546 +#> 181: 93.5075 -5.8632 -0.0965 2.3140 -0.9764 2.5812 2.9771 0.3715 0.3728 0.1876 5.9833 1.5356 +#> 182: 93.4464 -5.8627 -0.0930 2.3211 -0.9713 2.5956 2.8054 0.3836 0.3759 0.1861 6.1293 1.6259 +#> 183: 93.2737 -5.8238 -0.0977 2.3127 -0.9642 2.8739 2.6277 0.3846 0.3743 0.1868 6.0451 1.6493 +#> 184: 93.2191 -5.9175 -0.0993 2.3107 -0.9592 2.3088 3.0689 0.3829 0.3515 0.1711 6.1487 1.6666 +#> 185: 93.3626 -5.8872 -0.1070 2.3112 -0.9413 2.2812 3.2719 0.3712 0.3555 0.1783 6.1295 1.6288 +#> 186: 93.1585 -5.8532 -0.1053 2.3140 -0.9665 2.7906 2.8415 0.3734 0.3531 0.1680 6.0294 1.6104 +#> 187: 93.3041 -5.6798 -0.0957 2.3158 -0.9608 3.1056 2.0850 0.3813 0.3484 0.1728 6.1191 1.5813 +#> 188: 93.2466 -5.6791 -0.0954 2.3172 -0.9446 3.8296 2.1956 0.3816 0.3439 0.1757 5.9670 1.5445 +#> 189: 93.3532 -5.6883 -0.0859 2.3335 -0.9594 2.8968 2.3125 0.3691 0.3512 0.1812 5.9467 1.6101 +#> 190: 93.5064 -5.6288 -0.0726 2.3548 -0.9562 2.8233 2.1930 0.3334 0.3700 0.1759 6.4036 1.5877 +#> 191: 93.4145 -5.6906 -0.0726 2.3467 -0.9624 2.8818 2.3581 0.3334 0.3771 0.1712 6.2046 1.4952 +#> 192: 93.2060 -5.7479 -0.0716 2.3433 -0.9618 2.5221 2.6613 0.3324 0.3909 0.1552 6.1651 1.4971 +#> 193: 93.2904 -5.7634 -0.0811 2.3327 -0.9585 2.6968 2.6324 0.3339 0.3856 0.1632 6.5621 1.5258 +#> 194: 93.5271 -5.7859 -0.0874 2.3419 -0.9580 2.8361 2.8424 0.3286 0.3784 0.1636 6.3714 1.5386 +#> 195: 93.3944 -5.9358 -0.0838 2.3407 -0.9718 3.4161 3.2427 0.3315 0.3787 0.1678 6.3722 1.5181 +#> 196: 93.2341 -5.9078 -0.0701 2.3492 -0.9816 3.1580 3.0586 0.3285 0.3666 0.1681 6.4633 1.5382 +#> 197: 93.2967 -6.0131 -0.0745 2.3426 -0.9991 3.7978 3.6459 0.3353 0.3491 0.1796 6.2264 1.5310 +#> 198: 93.2628 -5.7991 -0.0730 2.3434 -0.9819 2.3896 2.6695 0.3371 0.3431 0.1762 6.3141 1.5254 +#> 199: 93.2765 -5.9078 -0.0782 2.3553 -0.9864 2.2760 3.3883 0.3420 0.3459 0.1866 6.0192 1.4982 +#> 200: 93.0447 -5.9148 -0.0769 2.3543 -0.9759 2.1516 2.9675 0.3455 0.3476 0.1870 5.9079 1.4688 +#> 201: 93.1655 -5.8951 -0.0763 2.3493 -0.9707 1.8254 2.9481 0.3448 0.3526 0.1831 6.0676 1.5097 +#> 202: 93.1082 -5.8916 -0.0768 2.3499 -0.9673 1.8503 2.9562 0.3447 0.3574 0.1821 6.1282 1.5026 +#> 203: 93.0728 -5.9316 -0.0774 2.3506 -0.9650 2.0210 3.2306 0.3441 0.3563 0.1827 6.1253 1.4974 +#> 204: 93.0846 -5.9347 -0.0773 2.3494 -0.9648 2.1463 3.2567 0.3453 0.3563 0.1824 6.1301 1.4911 +#> 205: 93.0929 -5.9439 -0.0781 2.3491 -0.9659 2.2204 3.3165 0.3453 0.3572 0.1823 6.1098 1.4941 +#> 206: 93.1795 -5.9401 -0.0795 2.3481 -0.9681 2.2588 3.2940 0.3470 0.3568 0.1829 6.1132 1.4996 +#> 207: 93.2303 -5.9158 -0.0805 2.3467 -0.9703 2.3439 3.1823 0.3484 0.3571 0.1845 6.1021 1.5059 +#> 208: 93.2161 -5.8969 -0.0825 2.3440 -0.9700 2.3306 3.0999 0.3496 0.3563 0.1848 6.0998 1.5177 +#> 209: 93.2077 -5.8842 -0.0848 2.3413 -0.9681 2.3580 3.0406 0.3499 0.3553 0.1841 6.0829 1.5199 +#> 210: 93.1951 -5.8661 -0.0867 2.3383 -0.9656 2.4170 2.9578 0.3501 0.3543 0.1833 6.0562 1.5261 +#> 211: 93.1870 -5.8543 -0.0892 2.3347 -0.9645 2.4650 2.9307 0.3502 0.3548 0.1831 6.0286 1.5289 +#> 212: 93.2077 -5.8506 -0.0915 2.3316 -0.9626 2.4909 2.9544 0.3504 0.3555 0.1835 6.0079 1.5300 +#> 213: 93.2104 -5.8492 -0.0938 2.3283 -0.9612 2.4695 2.9635 0.3503 0.3548 0.1841 5.9859 1.5341 +#> 214: 93.2059 -5.8537 -0.0959 2.3255 -0.9615 2.4264 3.0084 0.3499 0.3540 0.1835 5.9698 1.5370 +#> 215: 93.2051 -5.8569 -0.0977 2.3227 -0.9608 2.4277 3.0541 0.3495 0.3534 0.1830 5.9586 1.5374 +#> 216: 93.1879 -5.8596 -0.0993 2.3199 -0.9600 2.4347 3.0802 0.3493 0.3534 0.1828 5.9465 1.5380 +#> 217: 93.1834 -5.8621 -0.1008 2.3173 -0.9594 2.4479 3.0998 0.3491 0.3535 0.1827 5.9369 1.5402 +#> 218: 93.1796 -5.8657 -0.1021 2.3152 -0.9593 2.4234 3.1238 0.3492 0.3534 0.1835 5.9184 1.5441 +#> 219: 93.1680 -5.8721 -0.1032 2.3132 -0.9588 2.4640 3.1464 0.3494 0.3531 0.1839 5.8929 1.5493 +#> 220: 93.1579 -5.8839 -0.1044 2.3118 -0.9586 2.5707 3.1909 0.3495 0.3531 0.1847 5.8754 1.5496 +#> 221: 93.1557 -5.8882 -0.1058 2.3100 -0.9583 2.6662 3.2052 0.3492 0.3533 0.1854 5.8662 1.5518 +#> 222: 93.1624 -5.8832 -0.1074 2.3075 -0.9578 2.7993 3.1736 0.3490 0.3542 0.1861 5.8489 1.5546 +#> 223: 93.1699 -5.8771 -0.1086 2.3052 -0.9583 2.9085 3.1456 0.3488 0.3558 0.1871 5.8436 1.5610 +#> 224: 93.1870 -5.8751 -0.1097 2.3037 -0.9583 2.9988 3.1279 0.3487 0.3570 0.1878 5.8390 1.5628 +#> 225: 93.2094 -5.8719 -0.1110 2.3012 -0.9583 3.0581 3.1018 0.3485 0.3574 0.1885 5.8214 1.5656 +#> 226: 93.2352 -5.8683 -0.1122 2.2988 -0.9587 3.1297 3.0761 0.3482 0.3584 0.1895 5.8105 1.5680 +#> 227: 93.2611 -5.8653 -0.1132 2.2964 -0.9589 3.1563 3.0610 0.3476 0.3594 0.1904 5.8038 1.5701 +#> 228: 93.2741 -5.8593 -0.1140 2.2943 -0.9591 3.1641 3.0356 0.3470 0.3603 0.1911 5.7984 1.5730 +#> 229: 93.2899 -5.8593 -0.1151 2.2919 -0.9595 3.1626 3.0313 0.3466 0.3613 0.1918 5.7999 1.5745 +#> 230: 93.3048 -5.8650 -0.1164 2.2899 -0.9593 3.1743 3.0542 0.3460 0.3624 0.1921 5.7990 1.5753 +#> 231: 93.3159 -5.8638 -0.1177 2.2875 -0.9592 3.1930 3.0524 0.3454 0.3631 0.1924 5.7956 1.5748 +#> 232: 93.3209 -5.8611 -0.1189 2.2852 -0.9590 3.1872 3.0420 0.3450 0.3639 0.1926 5.7921 1.5755 +#> 233: 93.3196 -5.8556 -0.1200 2.2833 -0.9589 3.1861 3.0209 0.3445 0.3644 0.1926 5.7852 1.5779 +#> 234: 93.3245 -5.8530 -0.1210 2.2813 -0.9591 3.1890 3.0115 0.3441 0.3651 0.1922 5.7781 1.5786 +#> 235: 93.3219 -5.8522 -0.1218 2.2800 -0.9593 3.1573 3.0042 0.3437 0.3659 0.1917 5.7813 1.5797 +#> 236: 93.3155 -5.8524 -0.1227 2.2789 -0.9595 3.1542 3.0035 0.3433 0.3669 0.1913 5.7834 1.5800 +#> 237: 93.3060 -5.8556 -0.1235 2.2779 -0.9599 3.1308 3.0158 0.3430 0.3678 0.1910 5.7833 1.5809 +#> 238: 93.3111 -5.8563 -0.1242 2.2772 -0.9602 3.1194 3.0099 0.3427 0.3683 0.1907 5.7842 1.5809 +#> 239: 93.3177 -5.8580 -0.1248 2.2764 -0.9605 3.0944 3.0130 0.3423 0.3686 0.1904 5.7840 1.5815 +#> 240: 93.3222 -5.8606 -0.1255 2.2754 -0.9608 3.0739 3.0140 0.3420 0.3686 0.1902 5.7843 1.5825 +#> 241: 93.3289 -5.8627 -0.1262 2.2740 -0.9611 3.0848 3.0167 0.3417 0.3688 0.1900 5.7836 1.5840 +#> 242: 93.3366 -5.8627 -0.1270 2.2727 -0.9612 3.1273 3.0103 0.3415 0.3691 0.1898 5.7855 1.5850 +#> 243: 93.3441 -5.8646 -0.1277 2.2714 -0.9614 3.1530 3.0218 0.3414 0.3692 0.1896 5.7829 1.5856 +#> 244: 93.3499 -5.8645 -0.1285 2.2700 -0.9618 3.1705 3.0265 0.3412 0.3694 0.1894 5.7778 1.5874 +#> 245: 93.3619 -5.8673 -0.1294 2.2686 -0.9622 3.1863 3.0397 0.3412 0.3694 0.1892 5.7752 1.5889 +#> 246: 93.3745 -5.8698 -0.1301 2.2671 -0.9627 3.2105 3.0484 0.3412 0.3693 0.1890 5.7716 1.5905 +#> 247: 93.3838 -5.8757 -0.1307 2.2659 -0.9632 3.2158 3.0715 0.3412 0.3693 0.1889 5.7688 1.5922 +#> 248: 93.3914 -5.8799 -0.1314 2.2650 -0.9640 3.2268 3.0851 0.3413 0.3690 0.1889 5.7648 1.5934 +#> 249: 93.3983 -5.8844 -0.1319 2.2640 -0.9648 3.2471 3.0990 0.3415 0.3691 0.1889 5.7641 1.5944 +#> 250: 93.4032 -5.8898 -0.1324 2.2629 -0.9655 3.2828 3.1197 0.3414 0.3694 0.1887 5.7623 1.5965 +#> 251: 93.4053 -5.8939 -0.1329 2.2621 -0.9657 3.3074 3.1303 0.3414 0.3698 0.1887 5.7611 1.5978 +#> 252: 93.4095 -5.8950 -0.1334 2.2613 -0.9658 3.3479 3.1281 0.3414 0.3701 0.1887 5.7578 1.5986 +#> 253: 93.4132 -5.8956 -0.1340 2.2606 -0.9660 3.3486 3.1283 0.3413 0.3703 0.1887 5.7559 1.5999 +#> 254: 93.4201 -5.8966 -0.1345 2.2597 -0.9660 3.3502 3.1298 0.3413 0.3706 0.1888 5.7593 1.5997 +#> 255: 93.4235 -5.8953 -0.1349 2.2590 -0.9656 3.3332 3.1220 0.3412 0.3706 0.1887 5.7571 1.6012 +#> 256: 93.4231 -5.8926 -0.1353 2.2585 -0.9651 3.3255 3.1104 0.3411 0.3706 0.1886 5.7569 1.6018 +#> 257: 93.4247 -5.8874 -0.1356 2.2582 -0.9646 3.3164 3.0917 0.3410 0.3705 0.1885 5.7585 1.6030 +#> 258: 93.4198 -5.8857 -0.1359 2.2580 -0.9641 3.3086 3.0828 0.3409 0.3702 0.1885 5.7608 1.6026 +#> 259: 93.4125 -5.8833 -0.1362 2.2576 -0.9638 3.2926 3.0726 0.3408 0.3701 0.1885 5.7651 1.6023 +#> 260: 93.4073 -5.8847 -0.1365 2.2572 -0.9640 3.2737 3.0759 0.3406 0.3703 0.1885 5.7687 1.6030 +#> 261: 93.4049 -5.8885 -0.1368 2.2571 -0.9642 3.2510 3.0904 0.3402 0.3702 0.1882 5.7742 1.6028 +#> 262: 93.4036 -5.8931 -0.1371 2.2566 -0.9645 3.2279 3.1104 0.3397 0.3699 0.1880 5.7766 1.6033 +#> 263: 93.4026 -5.8964 -0.1375 2.2562 -0.9647 3.2024 3.1313 0.3395 0.3696 0.1877 5.7786 1.6029 +#> 264: 93.3990 -5.9003 -0.1377 2.2559 -0.9649 3.1808 3.1545 0.3393 0.3694 0.1874 5.7778 1.6022 +#> 265: 93.4005 -5.9013 -0.1380 2.2555 -0.9650 3.1664 3.1680 0.3390 0.3693 0.1871 5.7765 1.6021 +#> 266: 93.4005 -5.9011 -0.1382 2.2552 -0.9653 3.1530 3.1708 0.3387 0.3692 0.1869 5.7763 1.6020 +#> 267: 93.4006 -5.9035 -0.1384 2.2549 -0.9654 3.1384 3.1902 0.3384 0.3690 0.1866 5.7768 1.6014 +#> 268: 93.3972 -5.9086 -0.1385 2.2547 -0.9653 3.1224 3.2331 0.3380 0.3688 0.1863 5.7778 1.6008 +#> 269: 93.3936 -5.9113 -0.1386 2.2547 -0.9654 3.0959 3.2552 0.3377 0.3688 0.1861 5.7782 1.6001 +#> 270: 93.3867 -5.9139 -0.1387 2.2547 -0.9653 3.0853 3.2756 0.3372 0.3687 0.1859 5.7787 1.5989 +#> 271: 93.3836 -5.9154 -0.1389 2.2545 -0.9654 3.0824 3.2889 0.3367 0.3686 0.1858 5.7761 1.5980 +#> 272: 93.3812 -5.9160 -0.1390 2.2543 -0.9653 3.0741 3.2919 0.3362 0.3686 0.1857 5.7729 1.5977 +#> 273: 93.3767 -5.9174 -0.1390 2.2542 -0.9652 3.0663 3.2992 0.3358 0.3687 0.1856 5.7699 1.5970 +#> 274: 93.3696 -5.9171 -0.1391 2.2543 -0.9652 3.0604 3.2940 0.3355 0.3687 0.1855 5.7688 1.5958 +#> 275: 93.3658 -5.9177 -0.1393 2.2544 -0.9651 3.0605 3.2961 0.3353 0.3687 0.1853 5.7675 1.5952 +#> 276: 93.3621 -5.9185 -0.1395 2.2543 -0.9649 3.0508 3.2992 0.3351 0.3686 0.1852 5.7672 1.5940 +#> 277: 93.3602 -5.9206 -0.1397 2.2542 -0.9649 3.0453 3.3087 0.3349 0.3685 0.1851 5.7679 1.5935 +#> 278: 93.3565 -5.9213 -0.1400 2.2539 -0.9648 3.0366 3.3117 0.3347 0.3683 0.1852 5.7695 1.5931 +#> 279: 93.3548 -5.9222 -0.1403 2.2535 -0.9647 3.0284 3.3179 0.3345 0.3682 0.1854 5.7703 1.5928 +#> 280: 93.3544 -5.9215 -0.1407 2.2528 -0.9647 3.0193 3.3141 0.3344 0.3683 0.1854 5.7714 1.5927 +#> 281: 93.3533 -5.9205 -0.1410 2.2522 -0.9647 3.0130 3.3090 0.3341 0.3685 0.1855 5.7706 1.5927 +#> 282: 93.3564 -5.9189 -0.1414 2.2514 -0.9648 3.0025 3.3019 0.3339 0.3686 0.1856 5.7682 1.5930 +#> 283: 93.3571 -5.9164 -0.1417 2.2508 -0.9646 2.9990 3.2926 0.3337 0.3686 0.1858 5.7642 1.5943 +#> 284: 93.3576 -5.9154 -0.1421 2.2501 -0.9644 2.9976 3.2895 0.3336 0.3686 0.1860 5.7625 1.5942 +#> 285: 93.3584 -5.9142 -0.1425 2.2496 -0.9644 2.9906 3.2835 0.3334 0.3684 0.1861 5.7591 1.5939 +#> 286: 93.3609 -5.9137 -0.1429 2.2491 -0.9642 2.9852 3.2817 0.3332 0.3682 0.1863 5.7572 1.5939 +#> 287: 93.3641 -5.9131 -0.1433 2.2485 -0.9641 2.9732 3.2785 0.3331 0.3680 0.1863 5.7547 1.5944 +#> 288: 93.3671 -5.9128 -0.1436 2.2480 -0.9641 2.9673 3.2767 0.3330 0.3679 0.1864 5.7540 1.5939 +#> 289: 93.3676 -5.9125 -0.1440 2.2474 -0.9639 2.9663 3.2765 0.3329 0.3678 0.1865 5.7536 1.5939 +#> 290: 93.3659 -5.9126 -0.1443 2.2469 -0.9637 2.9570 3.2776 0.3328 0.3678 0.1866 5.7523 1.5941 +#> 291: 93.3620 -5.9109 -0.1447 2.2466 -0.9634 2.9472 3.2713 0.3327 0.3676 0.1866 5.7527 1.5943 +#> 292: 93.3601 -5.9096 -0.1450 2.2462 -0.9632 2.9359 3.2664 0.3326 0.3675 0.1866 5.7517 1.5944 +#> 293: 93.3582 -5.9077 -0.1453 2.2457 -0.9629 2.9295 3.2586 0.3326 0.3675 0.1866 5.7514 1.5945 +#> 294: 93.3583 -5.9054 -0.1456 2.2454 -0.9626 2.9203 3.2478 0.3326 0.3676 0.1867 5.7508 1.5942 +#> 295: 93.3577 -5.9037 -0.1459 2.2449 -0.9624 2.9216 3.2406 0.3325 0.3678 0.1867 5.7493 1.5934 +#> 296: 93.3570 -5.9016 -0.1462 2.2445 -0.9623 2.9304 3.2334 0.3323 0.3680 0.1868 5.7502 1.5933 +#> 297: 93.3538 -5.8988 -0.1462 2.2441 -0.9621 2.9429 3.2217 0.3321 0.3681 0.1870 5.7539 1.5939 +#> 298: 93.3525 -5.8966 -0.1463 2.2438 -0.9620 2.9662 3.2118 0.3319 0.3683 0.1870 5.7555 1.5942 +#> 299: 93.3526 -5.8957 -0.1465 2.2437 -0.9619 2.9812 3.2056 0.3318 0.3685 0.1870 5.7582 1.5938 +#> 300: 93.3504 -5.8953 -0.1467 2.2436 -0.9616 2.9982 3.2029 0.3316 0.3688 0.1873 5.7609 1.5937 +#> 301: 93.3469 -5.8941 -0.1469 2.2434 -0.9612 3.0124 3.1993 0.3315 0.3690 0.1875 5.7641 1.5933 +#> 302: 93.3442 -5.8944 -0.1472 2.2434 -0.9609 3.0353 3.2015 0.3313 0.3692 0.1876 5.7660 1.5937 +#> 303: 93.3428 -5.8970 -0.1474 2.2432 -0.9607 3.0454 3.2160 0.3312 0.3692 0.1876 5.7654 1.5938 +#> 304: 93.3407 -5.9012 -0.1475 2.2430 -0.9607 3.0626 3.2409 0.3310 0.3693 0.1877 5.7649 1.5932 +#> 305: 93.3395 -5.9051 -0.1476 2.2429 -0.9607 3.0756 3.2632 0.3308 0.3693 0.1879 5.7650 1.5924 +#> 306: 93.3398 -5.9099 -0.1478 2.2429 -0.9607 3.0881 3.2952 0.3306 0.3694 0.1880 5.7655 1.5920 +#> 307: 93.3406 -5.9128 -0.1479 2.2427 -0.9608 3.0995 3.3163 0.3305 0.3695 0.1880 5.7666 1.5921 +#> 308: 93.3418 -5.9165 -0.1480 2.2426 -0.9610 3.1060 3.3420 0.3303 0.3696 0.1881 5.7674 1.5914 +#> 309: 93.3437 -5.9205 -0.1481 2.2424 -0.9610 3.1185 3.3703 0.3301 0.3697 0.1882 5.7665 1.5908 +#> 310: 93.3442 -5.9236 -0.1482 2.2422 -0.9612 3.1270 3.3902 0.3299 0.3698 0.1882 5.7650 1.5904 +#> 311: 93.3482 -5.9268 -0.1482 2.2421 -0.9614 3.1333 3.4086 0.3296 0.3698 0.1882 5.7636 1.5900 +#> 312: 93.3529 -5.9286 -0.1482 2.2420 -0.9615 3.1348 3.4186 0.3294 0.3699 0.1882 5.7622 1.5895 +#> 313: 93.3573 -5.9290 -0.1481 2.2419 -0.9617 3.1332 3.4199 0.3291 0.3699 0.1882 5.7621 1.5891 +#> 314: 93.3630 -5.9293 -0.1482 2.2418 -0.9619 3.1398 3.4211 0.3289 0.3700 0.1883 5.7594 1.5888 +#> 315: 93.3669 -5.9284 -0.1483 2.2416 -0.9622 3.1464 3.4155 0.3286 0.3702 0.1885 5.7586 1.5889 +#> 316: 93.3724 -5.9279 -0.1485 2.2412 -0.9624 3.1426 3.4124 0.3283 0.3704 0.1887 5.7581 1.5887 +#> 317: 93.3763 -5.9281 -0.1487 2.2409 -0.9626 3.1335 3.4108 0.3281 0.3706 0.1888 5.7573 1.5880 +#> 318: 93.3786 -5.9275 -0.1488 2.2405 -0.9627 3.1262 3.4057 0.3279 0.3709 0.1888 5.7579 1.5876 +#> 319: 93.3821 -5.9275 -0.1490 2.2402 -0.9628 3.1273 3.4032 0.3276 0.3711 0.1889 5.7570 1.5870 +#> 320: 93.3856 -5.9272 -0.1491 2.2401 -0.9629 3.1337 3.3989 0.3273 0.3715 0.1888 5.7563 1.5861 +#> 321: 93.3902 -5.9263 -0.1492 2.2399 -0.9631 3.1388 3.3931 0.3269 0.3718 0.1887 5.7555 1.5852 +#> 322: 93.3951 -5.9251 -0.1493 2.2397 -0.9631 3.1415 3.3856 0.3266 0.3721 0.1886 5.7552 1.5846 +#> 323: 93.3988 -5.9251 -0.1493 2.2395 -0.9632 3.1377 3.3824 0.3262 0.3724 0.1885 5.7556 1.5841 +#> 324: 93.4030 -5.9236 -0.1494 2.2394 -0.9633 3.1355 3.3738 0.3259 0.3727 0.1885 5.7562 1.5837 +#> 325: 93.4047 -5.9219 -0.1495 2.2393 -0.9633 3.1415 3.3647 0.3256 0.3731 0.1884 5.7553 1.5831 +#> 326: 93.4077 -5.9204 -0.1495 2.2391 -0.9634 3.1489 3.3564 0.3254 0.3735 0.1884 5.7562 1.5829 +#> 327: 93.4121 -5.9185 -0.1496 2.2390 -0.9635 3.1503 3.3472 0.3250 0.3739 0.1884 5.7562 1.5825 +#> 328: 93.4157 -5.9182 -0.1496 2.2389 -0.9636 3.1564 3.3432 0.3246 0.3743 0.1884 5.7559 1.5823 +#> 329: 93.4181 -5.9169 -0.1496 2.2388 -0.9638 3.1666 3.3361 0.3243 0.3746 0.1884 5.7544 1.5822 +#> 330: 93.4206 -5.9171 -0.1497 2.2386 -0.9640 3.1726 3.3349 0.3239 0.3748 0.1885 5.7538 1.5824 +#> 331: 93.4214 -5.9172 -0.1497 2.2385 -0.9642 3.1764 3.3332 0.3236 0.3750 0.1886 5.7540 1.5824 +#> 332: 93.4226 -5.9171 -0.1497 2.2385 -0.9645 3.1787 3.3303 0.3232 0.3752 0.1887 5.7539 1.5826 +#> 333: 93.4242 -5.9168 -0.1497 2.2384 -0.9645 3.1757 3.3287 0.3229 0.3755 0.1886 5.7545 1.5823 +#> 334: 93.4273 -5.9167 -0.1497 2.2383 -0.9645 3.1832 3.3290 0.3226 0.3758 0.1887 5.7540 1.5818 +#> 335: 93.4306 -5.9170 -0.1498 2.2384 -0.9644 3.1910 3.3318 0.3223 0.3760 0.1887 5.7548 1.5814 +#> 336: 93.4315 -5.9177 -0.1498 2.2384 -0.9644 3.1999 3.3355 0.3219 0.3762 0.1887 5.7558 1.5811 +#> 337: 93.4332 -5.9181 -0.1499 2.2384 -0.9643 3.2145 3.3360 0.3216 0.3764 0.1887 5.7581 1.5805 +#> 338: 93.4352 -5.9169 -0.1498 2.2384 -0.9643 3.2221 3.3307 0.3213 0.3767 0.1887 5.7592 1.5802 +#> 339: 93.4385 -5.9152 -0.1498 2.2384 -0.9643 3.2356 3.3242 0.3210 0.3770 0.1887 5.7605 1.5797 +#> 340: 93.4417 -5.9130 -0.1498 2.2384 -0.9643 3.2506 3.3167 0.3207 0.3773 0.1888 5.7599 1.5794 +#> 341: 93.4452 -5.9102 -0.1497 2.2382 -0.9641 3.2568 3.3064 0.3205 0.3772 0.1888 5.7590 1.5799 +#> 342: 93.4487 -5.9077 -0.1497 2.2381 -0.9641 3.2628 3.2970 0.3203 0.3772 0.1889 5.7587 1.5802 +#> 343: 93.4519 -5.9055 -0.1497 2.2380 -0.9642 3.2685 3.2892 0.3201 0.3772 0.1889 5.7585 1.5810 +#> 344: 93.4556 -5.9048 -0.1497 2.2379 -0.9643 3.2690 3.2847 0.3200 0.3771 0.1891 5.7573 1.5812 +#> 345: 93.4588 -5.9041 -0.1498 2.2377 -0.9645 3.2704 3.2807 0.3199 0.3771 0.1893 5.7567 1.5811 +#> 346: 93.4605 -5.9033 -0.1498 2.2376 -0.9647 3.2655 3.2747 0.3198 0.3770 0.1893 5.7557 1.5808 +#> 347: 93.4638 -5.9027 -0.1498 2.2375 -0.9648 3.2725 3.2701 0.3198 0.3768 0.1894 5.7532 1.5808 +#> 348: 93.4643 -5.9028 -0.1498 2.2373 -0.9649 3.2764 3.2676 0.3197 0.3768 0.1893 5.7523 1.5807 +#> 349: 93.4664 -5.9023 -0.1497 2.2372 -0.9650 3.2806 3.2638 0.3197 0.3767 0.1893 5.7527 1.5815 +#> 350: 93.4700 -5.9014 -0.1497 2.2370 -0.9651 3.2817 3.2585 0.3196 0.3767 0.1892 5.7534 1.5817 +#> 351: 93.4724 -5.9001 -0.1497 2.2369 -0.9652 3.2825 3.2522 0.3196 0.3768 0.1892 5.7541 1.5818 +#> 352: 93.4744 -5.8986 -0.1497 2.2369 -0.9653 3.2875 3.2460 0.3195 0.3768 0.1891 5.7546 1.5819 +#> 353: 93.4738 -5.8975 -0.1496 2.2369 -0.9653 3.2891 3.2407 0.3195 0.3769 0.1889 5.7560 1.5822 +#> 354: 93.4733 -5.8960 -0.1496 2.2369 -0.9652 3.2856 3.2333 0.3194 0.3768 0.1889 5.7579 1.5824 +#> 355: 93.4731 -5.8944 -0.1496 2.2370 -0.9652 3.2893 3.2259 0.3194 0.3767 0.1888 5.7599 1.5826 +#> 356: 93.4724 -5.8933 -0.1495 2.2373 -0.9652 3.2924 3.2197 0.3194 0.3767 0.1888 5.7608 1.5832 +#> 357: 93.4723 -5.8929 -0.1493 2.2376 -0.9654 3.2907 3.2164 0.3194 0.3767 0.1887 5.7605 1.5833 +#> 358: 93.4723 -5.8923 -0.1491 2.2378 -0.9654 3.2875 3.2120 0.3194 0.3766 0.1886 5.7608 1.5837 +#> 359: 93.4705 -5.8931 -0.1490 2.2379 -0.9656 3.2875 3.2121 0.3194 0.3764 0.1886 5.7606 1.5843 +#> 360: 93.4699 -5.8938 -0.1488 2.2382 -0.9658 3.2837 3.2133 0.3195 0.3763 0.1886 5.7606 1.5848 +#> 361: 93.4693 -5.8951 -0.1487 2.2383 -0.9659 3.2822 3.2164 0.3195 0.3763 0.1886 5.7600 1.5852 +#> 362: 93.4691 -5.8963 -0.1486 2.2385 -0.9660 3.2770 3.2196 0.3195 0.3763 0.1884 5.7618 1.5856 +#> 363: 93.4681 -5.8970 -0.1485 2.2387 -0.9660 3.2706 3.2208 0.3195 0.3762 0.1883 5.7639 1.5857 +#> 364: 93.4674 -5.8970 -0.1484 2.2389 -0.9660 3.2593 3.2189 0.3195 0.3760 0.1881 5.7659 1.5855 +#> 365: 93.4680 -5.8968 -0.1482 2.2391 -0.9659 3.2513 3.2174 0.3196 0.3758 0.1881 5.7686 1.5857 +#> 366: 93.4672 -5.8962 -0.1480 2.2393 -0.9658 3.2493 3.2161 0.3196 0.3755 0.1880 5.7714 1.5861 +#> 367: 93.4656 -5.8953 -0.1479 2.2396 -0.9657 3.2462 3.2121 0.3195 0.3753 0.1881 5.7721 1.5862 +#> 368: 93.4645 -5.8946 -0.1478 2.2398 -0.9657 3.2469 3.2083 0.3194 0.3750 0.1882 5.7724 1.5860 +#> 369: 93.4638 -5.8946 -0.1476 2.2401 -0.9657 3.2544 3.2068 0.3194 0.3749 0.1882 5.7713 1.5856 +#> 370: 93.4639 -5.8946 -0.1475 2.2404 -0.9657 3.2547 3.2066 0.3194 0.3748 0.1882 5.7719 1.5853 +#> 371: 93.4646 -5.8959 -0.1474 2.2407 -0.9657 3.2584 3.2129 0.3194 0.3746 0.1883 5.7725 1.5847 +#> 372: 93.4648 -5.8964 -0.1473 2.2409 -0.9658 3.2649 3.2172 0.3193 0.3745 0.1883 5.7730 1.5843 +#> 373: 93.4658 -5.8958 -0.1471 2.2411 -0.9659 3.2744 3.2135 0.3193 0.3743 0.1884 5.7730 1.5843 +#> 374: 93.4678 -5.8953 -0.1470 2.2412 -0.9662 3.2855 3.2100 0.3192 0.3742 0.1885 5.7727 1.5847 +#> 375: 93.4697 -5.8955 -0.1470 2.2413 -0.9663 3.2917 3.2087 0.3190 0.3742 0.1885 5.7733 1.5845 +#> 376: 93.4707 -5.8960 -0.1469 2.2414 -0.9664 3.2997 3.2095 0.3189 0.3741 0.1885 5.7726 1.5841 +#> 377: 93.4712 -5.8965 -0.1468 2.2415 -0.9665 3.3016 3.2100 0.3188 0.3741 0.1885 5.7724 1.5836 +#> 378: 93.4706 -5.8971 -0.1468 2.2416 -0.9665 3.2958 3.2113 0.3187 0.3741 0.1884 5.7733 1.5829 +#> 379: 93.4699 -5.8983 -0.1467 2.2418 -0.9666 3.2940 3.2174 0.3186 0.3741 0.1883 5.7732 1.5827 +#> 380: 93.4709 -5.8993 -0.1467 2.2418 -0.9667 3.2907 3.2225 0.3185 0.3739 0.1882 5.7726 1.5826 +#> 381: 93.4730 -5.9009 -0.1467 2.2418 -0.9667 3.2861 3.2325 0.3185 0.3737 0.1881 5.7709 1.5825 +#> 382: 93.4746 -5.9018 -0.1467 2.2418 -0.9667 3.2841 3.2407 0.3184 0.3734 0.1880 5.7692 1.5822 +#> 383: 93.4744 -5.9033 -0.1468 2.2418 -0.9667 3.2847 3.2537 0.3184 0.3732 0.1878 5.7672 1.5819 +#> 384: 93.4747 -5.9049 -0.1468 2.2418 -0.9667 3.2854 3.2640 0.3184 0.3729 0.1878 5.7657 1.5816 +#> 385: 93.4751 -5.9062 -0.1468 2.2418 -0.9666 3.2917 3.2702 0.3184 0.3727 0.1877 5.7642 1.5813 +#> 386: 93.4756 -5.9074 -0.1468 2.2418 -0.9666 3.2971 3.2753 0.3185 0.3725 0.1876 5.7625 1.5810 +#> 387: 93.4761 -5.9084 -0.1469 2.2417 -0.9666 3.2988 3.2789 0.3185 0.3723 0.1875 5.7613 1.5804 +#> 388: 93.4777 -5.9092 -0.1469 2.2417 -0.9666 3.3055 3.2811 0.3185 0.3721 0.1875 5.7599 1.5803 +#> 389: 93.4805 -5.9092 -0.1468 2.2417 -0.9667 3.3138 3.2802 0.3185 0.3719 0.1874 5.7588 1.5803 +#> 390: 93.4828 -5.9089 -0.1468 2.2417 -0.9667 3.3164 3.2782 0.3186 0.3718 0.1873 5.7576 1.5806 +#> 391: 93.4854 -5.9094 -0.1467 2.2416 -0.9668 3.3265 3.2800 0.3186 0.3716 0.1873 5.7556 1.5804 +#> 392: 93.4877 -5.9103 -0.1467 2.2416 -0.9669 3.3327 3.2836 0.3187 0.3715 0.1873 5.7535 1.5803 +#> 393: 93.4899 -5.9110 -0.1467 2.2416 -0.9669 3.3419 3.2876 0.3187 0.3715 0.1873 5.7517 1.5803 +#> 394: 93.4925 -5.9117 -0.1467 2.2416 -0.9669 3.3494 3.2903 0.3187 0.3714 0.1873 5.7508 1.5801 +#> 395: 93.4945 -5.9121 -0.1467 2.2416 -0.9670 3.3536 3.2912 0.3187 0.3714 0.1873 5.7497 1.5796 +#> 396: 93.4951 -5.9124 -0.1467 2.2416 -0.9670 3.3590 3.2918 0.3187 0.3715 0.1873 5.7476 1.5793 +#> 397: 93.4955 -5.9123 -0.1467 2.2416 -0.9669 3.3626 3.2904 0.3186 0.3715 0.1873 5.7456 1.5788 +#> 398: 93.4971 -5.9120 -0.1467 2.2416 -0.9669 3.3735 3.2887 0.3186 0.3716 0.1873 5.7433 1.5786 +#> 399: 93.4995 -5.9116 -0.1467 2.2415 -0.9669 3.3854 3.2866 0.3186 0.3716 0.1873 5.7422 1.5785 +#> 400: 93.5007 -5.9116 -0.1466 2.2415 -0.9669 3.3923 3.2856 0.3186 0.3717 0.1873 5.7416 1.5786 +#> 401: 93.5028 -5.9109 -0.1467 2.2415 -0.9669 3.4020 3.2820 0.3186 0.3718 0.1873 5.7412 1.5787 +#> 402: 93.5042 -5.9099 -0.1467 2.2414 -0.9669 3.4114 3.2781 0.3186 0.3719 0.1874 5.7406 1.5788 +#> 403: 93.5054 -5.9090 -0.1467 2.2413 -0.9670 3.4179 3.2735 0.3186 0.3720 0.1874 5.7401 1.5785 +#> 404: 93.5071 -5.9093 -0.1468 2.2412 -0.9670 3.4190 3.2726 0.3186 0.3720 0.1875 5.7392 1.5779 +#> 405: 93.5087 -5.9087 -0.1468 2.2411 -0.9671 3.4186 3.2689 0.3186 0.3721 0.1876 5.7386 1.5776 +#> 406: 93.5091 -5.9087 -0.1469 2.2411 -0.9671 3.4228 3.2688 0.3186 0.3721 0.1876 5.7377 1.5774 +#> 407: 93.5094 -5.9091 -0.1470 2.2411 -0.9672 3.4285 3.2698 0.3186 0.3720 0.1877 5.7368 1.5770 +#> 408: 93.5108 -5.9081 -0.1470 2.2410 -0.9672 3.4378 3.2648 0.3187 0.3719 0.1877 5.7358 1.5766 +#> 409: 93.5113 -5.9082 -0.1470 2.2410 -0.9672 3.4444 3.2643 0.3187 0.3719 0.1878 5.7357 1.5763 +#> 410: 93.5102 -5.9099 -0.1470 2.2410 -0.9672 3.4502 3.2731 0.3188 0.3719 0.1878 5.7359 1.5756 +#> 411: 93.5097 -5.9109 -0.1469 2.2410 -0.9673 3.4534 3.2793 0.3188 0.3718 0.1878 5.7348 1.5753 +#> 412: 93.5102 -5.9114 -0.1469 2.2410 -0.9673 3.4522 3.2836 0.3189 0.3717 0.1878 5.7330 1.5753 +#> 413: 93.5110 -5.9120 -0.1469 2.2410 -0.9675 3.4534 3.2885 0.3189 0.3716 0.1878 5.7320 1.5756 +#> 414: 93.5126 -5.9130 -0.1469 2.2410 -0.9675 3.4550 3.2943 0.3190 0.3716 0.1878 5.7314 1.5753 +#> 415: 93.5144 -5.9140 -0.1469 2.2409 -0.9676 3.4574 3.3003 0.3190 0.3715 0.1878 5.7304 1.5751 +#> 416: 93.5147 -5.9149 -0.1469 2.2409 -0.9676 3.4632 3.3059 0.3191 0.3714 0.1878 5.7292 1.5750 +#> 417: 93.5132 -5.9156 -0.1468 2.2410 -0.9677 3.4675 3.3090 0.3192 0.3713 0.1878 5.7292 1.5747 +#> 418: 93.5131 -5.9165 -0.1468 2.2410 -0.9678 3.4680 3.3130 0.3192 0.3712 0.1878 5.7296 1.5747 +#> 419: 93.5142 -5.9166 -0.1467 2.2411 -0.9678 3.4663 3.3143 0.3193 0.3712 0.1879 5.7302 1.5744 +#> 420: 93.5150 -5.9164 -0.1466 2.2412 -0.9679 3.4626 3.3130 0.3193 0.3712 0.1879 5.7303 1.5744 +#> 421: 93.5162 -5.9169 -0.1465 2.2413 -0.9681 3.4596 3.3158 0.3194 0.3713 0.1880 5.7315 1.5743 +#> 422: 93.5173 -5.9172 -0.1465 2.2414 -0.9682 3.4567 3.3165 0.3194 0.3714 0.1881 5.7332 1.5740 +#> 423: 93.5174 -5.9178 -0.1464 2.2415 -0.9684 3.4550 3.3185 0.3194 0.3715 0.1882 5.7348 1.5741 +#> 424: 93.5174 -5.9189 -0.1464 2.2417 -0.9685 3.4531 3.3225 0.3193 0.3716 0.1882 5.7360 1.5737 +#> 425: 93.5171 -5.9184 -0.1463 2.2418 -0.9685 3.4508 3.3186 0.3192 0.3718 0.1882 5.7372 1.5738 +#> 426: 93.5167 -5.9177 -0.1462 2.2419 -0.9686 3.4566 3.3143 0.3192 0.3720 0.1882 5.7385 1.5735 +#> 427: 93.5185 -5.9174 -0.1462 2.2420 -0.9687 3.4561 3.3114 0.3191 0.3721 0.1881 5.7389 1.5734 +#> 428: 93.5192 -5.9177 -0.1461 2.2421 -0.9688 3.4574 3.3112 0.3191 0.3722 0.1880 5.7398 1.5731 +#> 429: 93.5184 -5.9179 -0.1460 2.2421 -0.9689 3.4558 3.3102 0.3190 0.3723 0.1879 5.7405 1.5729 +#> 430: 93.5170 -5.9187 -0.1460 2.2421 -0.9690 3.4575 3.3132 0.3190 0.3724 0.1879 5.7404 1.5727 +#> 431: 93.5156 -5.9192 -0.1460 2.2422 -0.9691 3.4556 3.3150 0.3190 0.3724 0.1879 5.7405 1.5726 +#> 432: 93.5148 -5.9203 -0.1459 2.2422 -0.9692 3.4557 3.3201 0.3190 0.3725 0.1878 5.7409 1.5727 +#> 433: 93.5134 -5.9215 -0.1459 2.2422 -0.9692 3.4569 3.3263 0.3190 0.3726 0.1878 5.7415 1.5731 +#> 434: 93.5128 -5.9222 -0.1459 2.2423 -0.9691 3.4623 3.3304 0.3190 0.3726 0.1877 5.7422 1.5728 +#> 435: 93.5116 -5.9231 -0.1459 2.2424 -0.9691 3.4672 3.3376 0.3191 0.3727 0.1877 5.7424 1.5726 +#> 436: 93.5111 -5.9228 -0.1459 2.2425 -0.9692 3.4658 3.3352 0.3190 0.3727 0.1876 5.7429 1.5725 +#> 437: 93.5100 -5.9227 -0.1459 2.2425 -0.9692 3.4651 3.3328 0.3190 0.3727 0.1876 5.7430 1.5725 +#> 438: 93.5071 -5.9230 -0.1459 2.2425 -0.9692 3.4614 3.3329 0.3190 0.3728 0.1876 5.7437 1.5725 +#> 439: 93.5035 -5.9225 -0.1459 2.2426 -0.9691 3.4555 3.3298 0.3190 0.3728 0.1875 5.7449 1.5725 +#> 440: 93.5006 -5.9222 -0.1459 2.2426 -0.9690 3.4503 3.3286 0.3190 0.3728 0.1874 5.7461 1.5723 +#> 441: 93.4988 -5.9220 -0.1459 2.2427 -0.9689 3.4445 3.3272 0.3190 0.3728 0.1874 5.7466 1.5721 +#> 442: 93.4971 -5.9216 -0.1459 2.2428 -0.9688 3.4392 3.3265 0.3190 0.3728 0.1874 5.7475 1.5721 +#> 443: 93.4957 -5.9214 -0.1458 2.2429 -0.9688 3.4338 3.3256 0.3190 0.3729 0.1874 5.7487 1.5723 +#> 444: 93.4949 -5.9210 -0.1458 2.2430 -0.9688 3.4288 3.3236 0.3189 0.3729 0.1874 5.7502 1.5721 +#> 445: 93.4932 -5.9210 -0.1458 2.2430 -0.9687 3.4283 3.3237 0.3189 0.3731 0.1874 5.7516 1.5719 +#> 446: 93.4922 -5.9205 -0.1458 2.2430 -0.9687 3.4253 3.3215 0.3188 0.3733 0.1873 5.7524 1.5717 +#> 447: 93.4917 -5.9205 -0.1458 2.2430 -0.9686 3.4257 3.3213 0.3187 0.3736 0.1873 5.7528 1.5715 +#> 448: 93.4924 -5.9205 -0.1458 2.2430 -0.9685 3.4296 3.3209 0.3186 0.3737 0.1872 5.7532 1.5717 +#> 449: 93.4920 -5.9203 -0.1459 2.2430 -0.9684 3.4302 3.3194 0.3185 0.3739 0.1872 5.7542 1.5717 +#> 450: 93.4915 -5.9207 -0.1459 2.2430 -0.9684 3.4314 3.3217 0.3184 0.3741 0.1871 5.7551 1.5715 +#> 451: 93.4915 -5.9214 -0.1459 2.2430 -0.9684 3.4371 3.3253 0.3183 0.3743 0.1871 5.7562 1.5717 +#> 452: 93.4926 -5.9212 -0.1458 2.2430 -0.9683 3.4417 3.3242 0.3182 0.3745 0.1870 5.7567 1.5717 +#> 453: 93.4935 -5.9211 -0.1459 2.2430 -0.9683 3.4413 3.3232 0.3182 0.3746 0.1870 5.7574 1.5714 +#> 454: 93.4941 -5.9209 -0.1459 2.2429 -0.9683 3.4406 3.3222 0.3182 0.3748 0.1870 5.7580 1.5713 +#> 455: 93.4947 -5.9212 -0.1459 2.2429 -0.9684 3.4450 3.3232 0.3181 0.3750 0.1870 5.7580 1.5710 +#> 456: 93.4950 -5.9214 -0.1459 2.2429 -0.9684 3.4481 3.3236 0.3181 0.3751 0.1870 5.7585 1.5708 +#> 457: 93.4961 -5.9220 -0.1459 2.2429 -0.9685 3.4516 3.3266 0.3180 0.3752 0.1869 5.7590 1.5707 +#> 458: 93.4965 -5.9218 -0.1459 2.2428 -0.9685 3.4553 3.3257 0.3179 0.3753 0.1869 5.7589 1.5707 +#> 459: 93.4959 -5.9212 -0.1459 2.2428 -0.9685 3.4572 3.3229 0.3178 0.3754 0.1868 5.7596 1.5705 +#> 460: 93.4960 -5.9209 -0.1459 2.2428 -0.9685 3.4573 3.3209 0.3178 0.3755 0.1868 5.7598 1.5704 +#> 461: 93.4944 -5.9211 -0.1459 2.2428 -0.9685 3.4592 3.3202 0.3177 0.3757 0.1868 5.7609 1.5701 +#> 462: 93.4941 -5.9214 -0.1459 2.2428 -0.9686 3.4630 3.3206 0.3176 0.3759 0.1868 5.7617 1.5700 +#> 463: 93.4932 -5.9215 -0.1459 2.2429 -0.9686 3.4708 3.3197 0.3175 0.3761 0.1868 5.7622 1.5699 +#> 464: 93.4933 -5.9209 -0.1459 2.2429 -0.9685 3.4759 3.3162 0.3175 0.3762 0.1869 5.7628 1.5696 +#> 465: 93.4928 -5.9204 -0.1459 2.2428 -0.9685 3.4794 3.3133 0.3174 0.3764 0.1870 5.7642 1.5693 +#> 466: 93.4934 -5.9197 -0.1460 2.2428 -0.9685 3.4838 3.3105 0.3173 0.3766 0.1870 5.7659 1.5693 +#> 467: 93.4931 -5.9197 -0.1460 2.2428 -0.9685 3.4866 3.3094 0.3172 0.3768 0.1871 5.7667 1.5691 +#> 468: 93.4933 -5.9198 -0.1460 2.2428 -0.9685 3.4916 3.3099 0.3172 0.3769 0.1871 5.7672 1.5690 +#> 469: 93.4936 -5.9200 -0.1461 2.2427 -0.9685 3.4929 3.3119 0.3171 0.3771 0.1871 5.7681 1.5689 +#> 470: 93.4938 -5.9200 -0.1461 2.2427 -0.9685 3.4931 3.3111 0.3171 0.3773 0.1871 5.7685 1.5687 +#> 471: 93.4943 -5.9198 -0.1461 2.2427 -0.9685 3.4932 3.3097 0.3170 0.3776 0.1871 5.7681 1.5686 +#> 472: 93.4931 -5.9197 -0.1461 2.2427 -0.9684 3.4923 3.3092 0.3170 0.3778 0.1870 5.7683 1.5686 +#> 473: 93.4928 -5.9193 -0.1461 2.2426 -0.9684 3.4918 3.3068 0.3169 0.3781 0.1870 5.7690 1.5685 +#> 474: 93.4920 -5.9193 -0.1462 2.2426 -0.9683 3.4878 3.3075 0.3169 0.3781 0.1870 5.7687 1.5688 +#> 475: 93.4909 -5.9191 -0.1463 2.2425 -0.9683 3.4868 3.3069 0.3169 0.3782 0.1869 5.7681 1.5692 +#> 476: 93.4887 -5.9190 -0.1464 2.2424 -0.9682 3.4881 3.3072 0.3169 0.3783 0.1869 5.7673 1.5694 +#> 477: 93.4875 -5.9185 -0.1465 2.2423 -0.9681 3.4847 3.3059 0.3169 0.3784 0.1868 5.7667 1.5696 +#> 478: 93.4867 -5.9182 -0.1466 2.2421 -0.9681 3.4804 3.3056 0.3170 0.3784 0.1867 5.7661 1.5700 +#> 479: 93.4865 -5.9178 -0.1468 2.2419 -0.9681 3.4768 3.3043 0.3171 0.3784 0.1867 5.7657 1.5702 +#> 480: 93.4863 -5.9181 -0.1469 2.2417 -0.9680 3.4733 3.3057 0.3172 0.3784 0.1866 5.7656 1.5702 +#> 481: 93.4865 -5.9182 -0.1470 2.2415 -0.9680 3.4694 3.3069 0.3173 0.3784 0.1866 5.7648 1.5705 +#> 482: 93.4871 -5.9187 -0.1472 2.2412 -0.9681 3.4667 3.3089 0.3173 0.3784 0.1865 5.7631 1.5709 +#> 483: 93.4860 -5.9192 -0.1473 2.2410 -0.9681 3.4668 3.3107 0.3174 0.3785 0.1865 5.7624 1.5709 +#> 484: 93.4858 -5.9193 -0.1474 2.2408 -0.9681 3.4681 3.3111 0.3174 0.3786 0.1864 5.7615 1.5713 +#> 485: 93.4858 -5.9195 -0.1476 2.2406 -0.9681 3.4643 3.3110 0.3174 0.3787 0.1864 5.7612 1.5717 +#> 486: 93.4853 -5.9198 -0.1477 2.2404 -0.9682 3.4665 3.3115 0.3174 0.3788 0.1864 5.7612 1.5717 +#> 487: 93.4856 -5.9201 -0.1478 2.2402 -0.9682 3.4687 3.3143 0.3173 0.3790 0.1864 5.7612 1.5719 +#> 488: 93.4858 -5.9209 -0.1479 2.2401 -0.9683 3.4688 3.3186 0.3173 0.3792 0.1864 5.7626 1.5722 +#> 489: 93.4870 -5.9211 -0.1480 2.2399 -0.9684 3.4681 3.3198 0.3174 0.3794 0.1863 5.7640 1.5725 +#> 490: 93.4881 -5.9213 -0.1481 2.2398 -0.9684 3.4694 3.3211 0.3174 0.3797 0.1864 5.7650 1.5728 +#> 491: 93.4892 -5.9210 -0.1482 2.2395 -0.9685 3.4716 3.3193 0.3173 0.3799 0.1864 5.7650 1.5732 +#> 492: 93.4907 -5.9211 -0.1483 2.2393 -0.9686 3.4754 3.3179 0.3173 0.3801 0.1865 5.7648 1.5736 +#> 493: 93.4928 -5.9215 -0.1484 2.2390 -0.9686 3.4858 3.3185 0.3173 0.3803 0.1865 5.7640 1.5738 +#> 494: 93.4937 -5.9217 -0.1485 2.2388 -0.9687 3.4940 3.3182 0.3172 0.3805 0.1865 5.7639 1.5740 +#> 495: 93.4945 -5.9213 -0.1485 2.2386 -0.9688 3.4998 3.3151 0.3172 0.3808 0.1866 5.7638 1.5742 +#> 496: 93.4953 -5.9208 -0.1486 2.2384 -0.9688 3.5036 3.3123 0.3172 0.3810 0.1867 5.7635 1.5745 +#> 497: 93.4969 -5.9205 -0.1487 2.2382 -0.9689 3.5064 3.3109 0.3172 0.3813 0.1868 5.7637 1.5747 +#> 498: 93.4980 -5.9205 -0.1488 2.2379 -0.9690 3.5057 3.3104 0.3171 0.3815 0.1868 5.7639 1.5752 +#> 499: 93.4999 -5.9205 -0.1488 2.2377 -0.9691 3.5095 3.3102 0.3171 0.3817 0.1869 5.7639 1.5756 +#> 500: 93.5013 -5.9210 -0.1489 2.2376 -0.9691 3.5093 3.3135 0.3171 0.3818 0.1869 5.7644 1.5758</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='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'>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_A1 |f_parent_qlogis | log_alpha | +#> |.....................| log_beta |sigma_parent | sigma_A1 | o1 | +#> |.....................| o2 | o3 | o4 | o5 | +#> |<span style='font-weight: bold;'> 1</span>| 470.09130 | 1.000 | -1.000 | -0.9119 | -0.8960 | +#> |.....................| -0.8494 | -0.8528 | -0.8683 | -0.8768 | +#> |.....................| -0.8744 | -0.8681 | -0.8700 | -0.8694 | +#> | U| 470.0913 | 94.11 | -5.371 | -0.9909 | -0.1965 | +#> |.....................| 2.121 | 1.952 | 1.178 | 0.7545 | +#> |.....................| 0.8769 | 1.189 | 1.095 | 1.127 | +#> | X|<span style='font-weight: bold;'> 470.0913</span> | 94.11 | 0.004648 | 0.2707 | 0.8216 | +#> |.....................| 8.339 | 1.952 | 1.178 | 0.7545 | +#> |.....................| 0.8769 | 1.189 | 1.095 | 1.127 | +#> | G| Gill Diff. | 72.01 | 2.213 | -0.2476 | -0.3163 | +#> |.....................| -0.8532 | -32.82 | -13.44 | 9.552 | +#> |.....................| 11.72 | -12.16 | -9.599 | -9.049 | +#> |<span style='font-weight: bold;'> 2</span>| 5180.4321 | 0.1393 | -1.026 | -0.9090 | -0.8922 | +#> |.....................| -0.8392 | -0.4605 | -0.7077 | -0.9910 | +#> |.....................| -1.014 | -0.7228 | -0.7553 | -0.7612 | +#> | U| 5180.4321 | 13.11 | -5.398 | -0.9880 | -0.1927 | +#> |.....................| 2.131 | 2.334 | 1.272 | 0.6684 | +#> |.....................| 0.7541 | 1.362 | 1.220 | 1.248 | +#> | X|<span style='font-weight: bold;'> 5180.4321</span> | 13.11 | 0.004526 | 0.2713 | 0.8247 | +#> |.....................| 8.424 | 2.334 | 1.272 | 0.6684 | +#> |.....................| 0.7541 | 1.362 | 1.220 | 1.248 | +#> |<span style='font-weight: bold;'> 3</span>| 529.93288 | 0.9139 | -1.003 | -0.9116 | -0.8956 | +#> |.....................| -0.8484 | -0.8135 | -0.8523 | -0.8883 | +#> |.....................| -0.8884 | -0.8536 | -0.8585 | -0.8585 | +#> | U| 529.93288 | 86.01 | -5.374 | -0.9906 | -0.1961 | +#> |.....................| 2.122 | 1.990 | 1.187 | 0.7459 | +#> |.....................| 0.8647 | 1.206 | 1.107 | 1.139 | +#> | X|<span style='font-weight: bold;'> 529.93288</span> | 86.01 | 0.004635 | 0.2708 | 0.8219 | +#> |.....................| 8.347 | 1.990 | 1.187 | 0.7459 | +#> |.....................| 0.8647 | 1.206 | 1.107 | 1.139 | +#> |<span style='font-weight: bold;'> 4</span>| 469.96296 | 0.9914 | -1.000 | -0.9119 | -0.8959 | +#> |.....................| -0.8493 | -0.8489 | -0.8667 | -0.8780 | +#> |.....................| -0.8758 | -0.8667 | -0.8689 | -0.8683 | +#> | U| 469.96296 | 93.30 | -5.372 | -0.9909 | -0.1965 | +#> |.....................| 2.121 | 1.955 | 1.179 | 0.7536 | +#> |.....................| 0.8757 | 1.191 | 1.096 | 1.128 | +#> | X|<span style='font-weight: bold;'> 469.96296</span> | 93.30 | 0.004646 | 0.2707 | 0.8216 | +#> |.....................| 8.339 | 1.955 | 1.179 | 0.7536 | +#> |.....................| 0.8757 | 1.191 | 1.096 | 1.128 | +#> | F| Forward Diff. | -91.63 | 2.121 | -0.4143 | -0.3985 | +#> |.....................| -1.124 | -34.23 | -12.87 | 9.567 | +#> |.....................| 8.592 | -11.79 | -9.469 | -8.518 | +#> |<span style='font-weight: bold;'> 5</span>| 469.41305 | 0.9973 | -1.001 | -0.9118 | -0.8959 | +#> |.....................| -0.8491 | -0.8424 | -0.8642 | -0.8798 | +#> |.....................| -0.8776 | -0.8644 | -0.8670 | -0.8666 | +#> | U| 469.41305 | 93.85 | -5.372 | -0.9908 | -0.1964 | +#> |.....................| 2.121 | 1.962 | 1.180 | 0.7523 | +#> |.....................| 0.8741 | 1.193 | 1.098 | 1.130 | +#> | X|<span style='font-weight: bold;'> 469.41305</span> | 93.85 | 0.004644 | 0.2707 | 0.8217 | +#> |.....................| 8.341 | 1.962 | 1.180 | 0.7523 | +#> |.....................| 0.8741 | 1.193 | 1.098 | 1.130 | +#> | F| Forward Diff. | 19.88 | 2.163 | -0.2989 | -0.3449 | +#> |.....................| -0.9473 | -32.84 | -13.22 | 8.952 | +#> |.....................| 11.37 | -11.75 | -9.421 | -8.530 | +#> |<span style='font-weight: bold;'> 6</span>| 469.13124 | 0.9930 | -1.001 | -0.9118 | -0.8958 | +#> |.....................| -0.8489 | -0.8354 | -0.8614 | -0.8817 | +#> |.....................| -0.8801 | -0.8619 | -0.8650 | -0.8648 | +#> | U| 469.13124 | 93.45 | -5.373 | -0.9908 | -0.1963 | +#> |.....................| 2.121 | 1.969 | 1.182 | 0.7508 | +#> |.....................| 0.8719 | 1.196 | 1.100 | 1.132 | +#> | X|<span style='font-weight: bold;'> 469.13124</span> | 93.45 | 0.004642 | 0.2708 | 0.8218 | +#> |.....................| 8.343 | 1.969 | 1.182 | 0.7508 | +#> |.....................| 0.8719 | 1.196 | 1.100 | 1.132 | +#> | F| Forward Diff. | -60.06 | 2.108 | -0.3845 | -0.3876 | +#> |.....................| -1.088 | -32.82 | -12.89 | 8.720 | +#> |.....................| 9.663 | -11.60 | -9.301 | -8.348 | +#> |<span style='font-weight: bold;'> 7</span>| 468.71336 | 0.9979 | -1.002 | -0.9117 | -0.8957 | +#> |.....................| -0.8487 | -0.8285 | -0.8586 | -0.8835 | +#> |.....................| -0.8823 | -0.8594 | -0.8631 | -0.8630 | +#> | U| 468.71336 | 93.91 | -5.373 | -0.9907 | -0.1962 | +#> |.....................| 2.122 | 1.975 | 1.183 | 0.7495 | +#> |.....................| 0.8700 | 1.199 | 1.102 | 1.134 | +#> | X|<span style='font-weight: bold;'> 468.71336</span> | 93.91 | 0.004640 | 0.2708 | 0.8218 | +#> |.....................| 8.345 | 1.975 | 1.183 | 0.7495 | +#> |.....................| 0.8700 | 1.199 | 1.102 | 1.134 | +#> | F| Forward Diff. | 31.80 | 2.131 | -0.3007 | -0.3556 | +#> |.....................| -0.9543 | -30.66 | -12.35 | 8.979 | +#> |.....................| 9.681 | -11.54 | -9.231 | -8.330 | +#> |<span style='font-weight: bold;'> 8</span>| 468.42878 | 0.9931 | -1.002 | -0.9116 | -0.8956 | +#> |.....................| -0.8484 | -0.8217 | -0.8559 | -0.8855 | +#> |.....................| -0.8845 | -0.8568 | -0.8610 | -0.8612 | +#> | U| 468.42878 | 93.46 | -5.373 | -0.9906 | -0.1962 | +#> |.....................| 2.122 | 1.982 | 1.185 | 0.7480 | +#> |.....................| 0.8681 | 1.202 | 1.105 | 1.136 | +#> | X|<span style='font-weight: bold;'> 468.42878</span> | 93.46 | 0.004638 | 0.2708 | 0.8219 | +#> |.....................| 8.346 | 1.982 | 1.185 | 0.7480 | +#> |.....................| 0.8681 | 1.202 | 1.105 | 1.136 | +#> | F| Forward Diff. | -55.97 | 2.081 | -0.3855 | -0.3928 | +#> |.....................| -1.100 | -30.89 | -12.11 | 8.596 | +#> |.....................| 9.353 | -11.36 | -9.087 | -8.137 | +#> |<span style='font-weight: bold;'> 9</span>| 468.02528 | 0.9977 | -1.003 | -0.9115 | -0.8955 | +#> |.....................| -0.8482 | -0.8148 | -0.8531 | -0.8875 | +#> |.....................| -0.8866 | -0.8542 | -0.8589 | -0.8593 | +#> | U| 468.02528 | 93.90 | -5.374 | -0.9905 | -0.1961 | +#> |.....................| 2.122 | 1.989 | 1.187 | 0.7465 | +#> |.....................| 0.8662 | 1.206 | 1.107 | 1.138 | +#> | X|<span style='font-weight: bold;'> 468.02528</span> | 93.90 | 0.004636 | 0.2708 | 0.8220 | +#> |.....................| 8.348 | 1.989 | 1.187 | 0.7465 | +#> |.....................| 0.8662 | 1.206 | 1.107 | 1.138 | +#> | F| Forward Diff. | 28.40 | 2.101 | -0.3066 | -0.3612 | +#> |.....................| -0.9721 | -29.21 | -11.91 | 8.561 | +#> |.....................| 9.360 | -11.31 | -9.026 | -8.108 | +#> |<span style='font-weight: bold;'> 10</span>| 467.76129 | 0.9930 | -1.003 | -0.9115 | -0.8954 | +#> |.....................| -0.8479 | -0.8081 | -0.8503 | -0.8895 | +#> |.....................| -0.8888 | -0.8515 | -0.8567 | -0.8574 | +#> | U| 467.76129 | 93.46 | -5.374 | -0.9905 | -0.1960 | +#> |.....................| 2.122 | 1.995 | 1.188 | 0.7450 | +#> |.....................| 0.8643 | 1.209 | 1.109 | 1.140 | +#> | X|<span style='font-weight: bold;'> 467.76129</span> | 93.46 | 0.004633 | 0.2708 | 0.8220 | +#> |.....................| 8.351 | 1.995 | 1.188 | 0.7450 | +#> |.....................| 0.8643 | 1.209 | 1.109 | 1.140 | +#> | F| Forward Diff. | -56.33 | 2.052 | -0.3905 | -0.3944 | +#> |.....................| -1.108 | -29.62 | -11.80 | 8.124 | +#> |.....................| 9.000 | -11.14 | -8.878 | -7.912 | +#> |<span style='font-weight: bold;'> 11</span>| 467.36507 | 0.9976 | -1.004 | -0.9114 | -0.8953 | +#> |.....................| -0.8477 | -0.8013 | -0.8475 | -0.8914 | +#> |.....................| -0.8910 | -0.8487 | -0.8545 | -0.8554 | +#> | U| 467.36507 | 93.88 | -5.375 | -0.9904 | -0.1959 | +#> |.....................| 2.123 | 2.002 | 1.190 | 0.7435 | +#> |.....................| 0.8624 | 1.212 | 1.112 | 1.142 | +#> | X|<span style='font-weight: bold;'> 467.36507</span> | 93.88 | 0.004631 | 0.2708 | 0.8221 | +#> |.....................| 8.353 | 2.002 | 1.190 | 0.7435 | +#> |.....................| 0.8624 | 1.212 | 1.112 | 1.142 | +#> | F| Forward Diff. | 25.62 | 2.072 | -0.2964 | -0.3658 | +#> |.....................| -0.9890 | -26.78 | -10.91 | 8.547 | +#> |.....................| 9.002 | -11.08 | -8.799 | -7.879 | +#> |<span style='font-weight: bold;'> 12</span>| 467.13453 | 0.9928 | -1.004 | -0.9113 | -0.8952 | +#> |.....................| -0.8474 | -0.7947 | -0.8448 | -0.8935 | +#> |.....................| -0.8932 | -0.8459 | -0.8523 | -0.8534 | +#> | U| 467.13453 | 93.43 | -5.376 | -0.9903 | -0.1958 | +#> |.....................| 2.123 | 2.008 | 1.191 | 0.7419 | +#> |.....................| 0.8604 | 1.215 | 1.114 | 1.145 | +#> | X|<span style='font-weight: bold;'> 467.13453</span> | 93.43 | 0.004628 | 0.2709 | 0.8222 | +#> |.....................| 8.355 | 2.008 | 1.191 | 0.7419 | +#> |.....................| 0.8604 | 1.215 | 1.114 | 1.145 | +#> | F| Forward Diff. | -59.86 | 2.021 | -0.3893 | -0.4093 | +#> |.....................| -1.140 | -28.00 | -11.13 | 7.926 | +#> |.....................| 9.918 | -10.90 | -8.684 | -7.680 | +#> |<span style='font-weight: bold;'> 13</span>| 466.72836 | 0.9971 | -1.005 | -0.9112 | -0.8951 | +#> |.....................| -0.8471 | -0.7882 | -0.8421 | -0.8957 | +#> |.....................| -0.8959 | -0.8428 | -0.8499 | -0.8513 | +#> | U| 466.72836 | 93.84 | -5.376 | -0.9902 | -0.1956 | +#> |.....................| 2.123 | 2.015 | 1.193 | 0.7403 | +#> |.....................| 0.8581 | 1.219 | 1.117 | 1.147 | +#> | X|<span style='font-weight: bold;'> 466.72836</span> | 93.84 | 0.004626 | 0.2709 | 0.8223 | +#> |.....................| 8.358 | 2.015 | 1.193 | 0.7403 | +#> |.....................| 0.8581 | 1.219 | 1.117 | 1.147 | +#> | F| Forward Diff. | 18.13 | 2.039 | -0.3145 | -0.3694 | +#> |.....................| -1.015 | -26.10 | -10.63 | 8.044 | +#> |.....................| 8.616 | -10.80 | -8.580 | -7.637 | +#> |<span style='font-weight: bold;'> 14</span>| 466.53378 | 0.9925 | -1.005 | -0.9111 | -0.8950 | +#> |.....................| -0.8468 | -0.7815 | -0.8394 | -0.8978 | +#> |.....................| -0.8981 | -0.8400 | -0.8477 | -0.8494 | +#> | U| 466.53378 | 93.40 | -5.377 | -0.9901 | -0.1956 | +#> |.....................| 2.123 | 2.021 | 1.195 | 0.7387 | +#> |.....................| 0.8562 | 1.222 | 1.119 | 1.149 | +#> | X|<span style='font-weight: bold;'> 466.53378</span> | 93.40 | 0.004623 | 0.2709 | 0.8224 | +#> |.....................| 8.360 | 2.021 | 1.195 | 0.7387 | +#> |.....................| 0.8562 | 1.222 | 1.119 | 1.149 | +#> | F| Forward Diff. | -63.81 | 1.989 | -0.4067 | -0.4178 | +#> |.....................| -1.167 | -26.39 | -10.45 | 7.924 | +#> |.....................| 8.221 | -10.62 | -8.445 | -7.432 | +#> |<span style='font-weight: bold;'> 15</span>| 466.13347 | 0.9972 | -1.006 | -0.9110 | -0.8949 | +#> |.....................| -0.8464 | -0.7752 | -0.8368 | -0.9000 | +#> |.....................| -0.9002 | -0.8369 | -0.8452 | -0.8472 | +#> | U| 466.13347 | 93.85 | -5.377 | -0.9900 | -0.1954 | +#> |.....................| 2.124 | 2.027 | 1.196 | 0.7370 | +#> |.....................| 0.8543 | 1.226 | 1.122 | 1.152 | +#> | X|<span style='font-weight: bold;'> 466.13347</span> | 93.85 | 0.004620 | 0.2709 | 0.8225 | +#> |.....................| 8.363 | 2.027 | 1.196 | 0.7370 | +#> |.....................| 0.8543 | 1.226 | 1.122 | 1.152 | +#> | F| Forward Diff. | 18.92 | 2.012 | -0.3108 | -0.3757 | +#> |.....................| -1.021 | -25.52 | -10.81 | 7.279 | +#> |.....................| 9.661 | -10.54 | -8.331 | -7.395 | +#> |<span style='font-weight: bold;'> 16</span>| 465.94504 | 0.9925 | -1.006 | -0.9109 | -0.8948 | +#> |.....................| -0.8461 | -0.7686 | -0.8339 | -0.9019 | +#> |.....................| -0.9028 | -0.8341 | -0.8430 | -0.8453 | +#> | U| 465.94504 | 93.41 | -5.378 | -0.9899 | -0.1953 | +#> |.....................| 2.124 | 2.034 | 1.198 | 0.7356 | +#> |.....................| 0.8521 | 1.229 | 1.124 | 1.154 | +#> | X|<span style='font-weight: bold;'> 465.94504</span> | 93.41 | 0.004618 | 0.2709 | 0.8226 | +#> |.....................| 8.366 | 2.034 | 1.198 | 0.7356 | +#> |.....................| 0.8521 | 1.229 | 1.124 | 1.154 | +#> | F| Forward Diff. | -61.65 | 1.961 | -0.4097 | -0.4254 | +#> |.....................| -1.181 | -25.22 | -10.13 | 7.338 | +#> |.....................| 9.206 | -10.38 | -8.223 | -7.205 | +#> |<span style='font-weight: bold;'> 17</span>| 465.56754 | 0.9973 | -1.007 | -0.9108 | -0.8946 | +#> |.....................| -0.8457 | -0.7626 | -0.8312 | -0.9037 | +#> |.....................| -0.9058 | -0.8309 | -0.8405 | -0.8432 | +#> | U| 465.56754 | 93.86 | -5.378 | -0.9898 | -0.1952 | +#> |.....................| 2.125 | 2.040 | 1.199 | 0.7342 | +#> |.....................| 0.8494 | 1.233 | 1.127 | 1.156 | +#> | X|<span style='font-weight: bold;'> 465.56754</span> | 93.86 | 0.004615 | 0.2710 | 0.8227 | +#> |.....................| 8.369 | 2.040 | 1.199 | 0.7342 | +#> |.....................| 0.8494 | 1.233 | 1.127 | 1.156 | +#> | F| Forward Diff. | 20.78 | 1.982 | -0.3060 | -0.3796 | +#> |.....................| -1.026 | -23.61 | -9.859 | 7.282 | +#> |.....................| 6.603 | -10.29 | -8.096 | -7.167 | +#> |<span style='font-weight: bold;'> 18</span>| 465.36858 | 0.9928 | -1.008 | -0.9107 | -0.8945 | +#> |.....................| -0.8454 | -0.7560 | -0.8284 | -0.9059 | +#> |.....................| -0.9077 | -0.8278 | -0.8381 | -0.8410 | +#> | U| 465.36858 | 93.44 | -5.379 | -0.9897 | -0.1950 | +#> |.....................| 2.125 | 2.046 | 1.201 | 0.7326 | +#> |.....................| 0.8477 | 1.237 | 1.130 | 1.159 | +#> | X|<span style='font-weight: bold;'> 465.36858</span> | 93.44 | 0.004612 | 0.2710 | 0.8228 | +#> |.....................| 8.372 | 2.046 | 1.201 | 0.7326 | +#> |.....................| 0.8477 | 1.237 | 1.130 | 1.159 | +#> | F| Forward Diff. | -55.43 | 1.935 | -0.4028 | -0.4254 | +#> |.....................| -1.182 | -23.34 | -9.189 | 7.305 | +#> |.....................| 7.555 | -10.07 | -7.946 | -6.960 | +#> |<span style='font-weight: bold;'> 19</span>| 465.01863 | 0.9972 | -1.008 | -0.9105 | -0.8943 | +#> |.....................| -0.8449 | -0.7499 | -0.8257 | -0.9082 | +#> |.....................| -0.9092 | -0.8240 | -0.8352 | -0.8386 | +#> | U| 465.01863 | 93.84 | -5.380 | -0.9895 | -0.1948 | +#> |.....................| 2.125 | 2.052 | 1.203 | 0.7308 | +#> |.....................| 0.8464 | 1.241 | 1.133 | 1.161 | +#> | X|<span style='font-weight: bold;'> 465.01863</span> | 93.84 | 0.004609 | 0.2710 | 0.8230 | +#> |.....................| 8.376 | 2.052 | 1.203 | 0.7308 | +#> |.....................| 0.8464 | 1.241 | 1.133 | 1.161 | +#> | F| Forward Diff. | 18.74 | 1.956 | -0.3105 | -0.3857 | +#> |.....................| -1.041 | -22.36 | -9.386 | 7.151 | +#> |.....................| 7.639 | -9.969 | -7.832 | -6.900 | +#> |<span style='font-weight: bold;'> 20</span>| 464.81883 | 0.9930 | -1.009 | -0.9104 | -0.8942 | +#> |.....................| -0.8445 | -0.7435 | -0.8230 | -0.9105 | +#> |.....................| -0.9115 | -0.8207 | -0.8326 | -0.8363 | +#> | U| 464.81883 | 93.45 | -5.381 | -0.9894 | -0.1947 | +#> |.....................| 2.126 | 2.058 | 1.204 | 0.7291 | +#> |.....................| 0.8444 | 1.245 | 1.136 | 1.164 | +#> | X|<span style='font-weight: bold;'> 464.81883</span> | 93.45 | 0.004605 | 0.2710 | 0.8231 | +#> |.....................| 8.380 | 2.058 | 1.204 | 0.7291 | +#> |.....................| 0.8444 | 1.245 | 1.136 | 1.164 | +#> | F| Forward Diff. | -51.40 | 1.910 | -0.3971 | -0.4173 | +#> |.....................| -1.192 | -21.85 | -8.569 | 7.088 | +#> |.....................| 7.257 | -9.784 | -7.694 | -6.698 | +#> |<span style='font-weight: bold;'> 21</span>| 464.49434 | 0.9973 | -1.010 | -0.9102 | -0.8940 | +#> |.....................| -0.8439 | -0.7380 | -0.8206 | -0.9131 | +#> |.....................| -0.9139 | -0.8168 | -0.8296 | -0.8338 | +#> | U| 464.49434 | 93.85 | -5.381 | -0.9892 | -0.1945 | +#> |.....................| 2.126 | 2.064 | 1.206 | 0.7271 | +#> |.....................| 0.8423 | 1.250 | 1.139 | 1.167 | +#> | X|<span style='font-weight: bold;'> 464.49434</span> | 93.85 | 0.004602 | 0.2711 | 0.8233 | +#> |.....................| 8.385 | 2.064 | 1.206 | 0.7271 | +#> |.....................| 0.8423 | 1.250 | 1.139 | 1.167 | +#> | F| Forward Diff. | 20.43 | 1.927 | -0.3065 | -0.3887 | +#> |.....................| -1.043 | -20.85 | -8.676 | 6.819 | +#> |.....................| 7.291 | -9.652 | -7.555 | -6.636 | +#> |<span style='font-weight: bold;'> 22</span>| 464.27900 | 0.9935 | -1.011 | -0.9101 | -0.8938 | +#> |.....................| -0.8433 | -0.7319 | -0.8180 | -0.9156 | +#> |.....................| -0.9164 | -0.8129 | -0.8266 | -0.8314 | +#> | U| 464.279 | 93.50 | -5.382 | -0.9891 | -0.1943 | +#> |.....................| 2.127 | 2.070 | 1.207 | 0.7252 | +#> |.....................| 0.8401 | 1.255 | 1.142 | 1.169 | +#> | X|<span style='font-weight: bold;'> 464.279</span> | 93.50 | 0.004598 | 0.2711 | 0.8234 | +#> |.....................| 8.389 | 2.070 | 1.207 | 0.7252 | +#> |.....................| 0.8401 | 1.255 | 1.142 | 1.169 | +#> | F| Forward Diff. | -42.65 | 1.884 | -0.3905 | -0.4168 | +#> |.....................| -1.174 | -21.12 | -8.566 | 6.431 | +#> |.....................| 8.301 | -9.439 | -7.399 | -6.436 | +#> |<span style='font-weight: bold;'> 23</span>| 463.98221 | 0.9971 | -1.012 | -0.9099 | -0.8935 | +#> |.....................| -0.8426 | -0.7266 | -0.8156 | -0.9179 | +#> |.....................| -0.9200 | -0.8088 | -0.8235 | -0.8288 | +#> | U| 463.98221 | 93.84 | -5.383 | -0.9889 | -0.1940 | +#> |.....................| 2.128 | 2.075 | 1.209 | 0.7235 | +#> |.....................| 0.8370 | 1.260 | 1.146 | 1.172 | +#> | X|<span style='font-weight: bold;'> 463.98221</span> | 93.84 | 0.004593 | 0.2711 | 0.8236 | +#> |.....................| 8.395 | 2.075 | 1.209 | 0.7235 | +#> |.....................| 0.8370 | 1.260 | 1.146 | 1.172 | +#> | F| Forward Diff. | 17.69 | 1.891 | -0.3039 | -0.3774 | +#> |.....................| -1.038 | -20.36 | -8.704 | 6.334 | +#> |.....................| 6.886 | -9.291 | -7.246 | -6.355 | +#> |<span style='font-weight: bold;'> 24</span>| 463.80345 | 0.9930 | -1.013 | -0.9097 | -0.8933 | +#> |.....................| -0.8421 | -0.7205 | -0.8127 | -0.9199 | +#> |.....................| -0.9227 | -0.8053 | -0.8209 | -0.8265 | +#> | U| 463.80345 | 93.45 | -5.384 | -0.9887 | -0.1939 | +#> |.....................| 2.128 | 2.081 | 1.210 | 0.7220 | +#> |.....................| 0.8346 | 1.264 | 1.148 | 1.175 | +#> | X|<span style='font-weight: bold;'> 463.80345</span> | 93.45 | 0.004590 | 0.2712 | 0.8238 | +#> |.....................| 8.399 | 2.081 | 1.210 | 0.7220 | +#> |.....................| 0.8346 | 1.264 | 1.148 | 1.175 | +#> | F| Forward Diff. | -49.16 | 1.846 | -0.3979 | -0.4233 | +#> |.....................| -1.191 | -20.11 | -8.128 | 6.150 | +#> |.....................| 7.842 | -9.114 | -7.113 | -6.163 | +#> |<span style='font-weight: bold;'> 25</span>| 463.50095 | 0.9970 | -1.014 | -0.9095 | -0.8930 | +#> |.....................| -0.8413 | -0.7152 | -0.8100 | -0.9219 | +#> |.....................| -0.9258 | -0.8011 | -0.8178 | -0.8240 | +#> | U| 463.50095 | 93.83 | -5.385 | -0.9885 | -0.1936 | +#> |.....................| 2.129 | 2.086 | 1.212 | 0.7205 | +#> |.....................| 0.8318 | 1.269 | 1.152 | 1.178 | +#> | X|<span style='font-weight: bold;'> 463.50095</span> | 93.83 | 0.004585 | 0.2712 | 0.8240 | +#> |.....................| 8.406 | 2.086 | 1.212 | 0.7205 | +#> |.....................| 0.8318 | 1.269 | 1.152 | 1.178 | +#> | F| Forward Diff. | 15.76 | 1.857 | -0.2989 | -0.3817 | +#> |.....................| -1.050 | -19.47 | -8.354 | 5.597 | +#> |.....................| 5.177 | -8.956 | -6.950 | -6.091 | +#> |<span style='font-weight: bold;'> 26</span>| 463.33971 | 0.9930 | -1.014 | -0.9093 | -0.8928 | +#> |.....................| -0.8408 | -0.7088 | -0.8070 | -0.9237 | +#> |.....................| -0.9274 | -0.7974 | -0.8150 | -0.8217 | +#> | U| 463.33971 | 93.45 | -5.386 | -0.9883 | -0.1934 | +#> |.....................| 2.129 | 2.092 | 1.214 | 0.7192 | +#> |.....................| 0.8304 | 1.273 | 1.155 | 1.180 | +#> | X|<span style='font-weight: bold;'> 463.33971</span> | 93.45 | 0.004581 | 0.2712 | 0.8242 | +#> |.....................| 8.411 | 2.092 | 1.214 | 0.7192 | +#> |.....................| 0.8304 | 1.273 | 1.155 | 1.180 | +#> | F| Forward Diff. | -49.38 | 1.817 | -0.3945 | -0.4254 | +#> |.....................| -1.192 | -18.49 | -7.219 | 6.140 | +#> |.....................| 6.147 | -8.752 | -6.775 | -5.892 | +#> |<span style='font-weight: bold;'> 27</span>| 463.06378 | 0.9971 | -1.016 | -0.9091 | -0.8925 | +#> |.....................| -0.8398 | -0.7035 | -0.8044 | -0.9255 | +#> |.....................| -0.9274 | -0.7927 | -0.8116 | -0.8189 | +#> | U| 463.06378 | 93.84 | -5.387 | -0.9881 | -0.1930 | +#> |.....................| 2.130 | 2.097 | 1.215 | 0.7178 | +#> |.....................| 0.8305 | 1.279 | 1.159 | 1.184 | +#> | X|<span style='font-weight: bold;'> 463.06378</span> | 93.84 | 0.004575 | 0.2713 | 0.8245 | +#> |.....................| 8.419 | 2.097 | 1.215 | 0.7178 | +#> |.....................| 0.8305 | 1.279 | 1.159 | 1.184 | +#> | F| Forward Diff. | 17.15 | 1.839 | -0.2941 | -0.3829 | +#> |.....................| -1.046 | -18.21 | -7.786 | 5.595 | +#> |.....................| 7.714 | -8.592 | -6.652 | -5.814 | +#> |<span style='font-weight: bold;'> 28</span>| 462.87224 | 0.9938 | -1.017 | -0.9088 | -0.8922 | +#> |.....................| -0.8390 | -0.6982 | -0.8019 | -0.9277 | +#> |.....................| -0.9311 | -0.7885 | -0.8085 | -0.8163 | +#> | U| 462.87224 | 93.52 | -5.388 | -0.9879 | -0.1927 | +#> |.....................| 2.131 | 2.102 | 1.217 | 0.7161 | +#> |.....................| 0.8272 | 1.284 | 1.162 | 1.186 | +#> | X|<span style='font-weight: bold;'> 462.87224</span> | 93.52 | 0.004570 | 0.2713 | 0.8247 | +#> |.....................| 8.425 | 2.102 | 1.217 | 0.7161 | +#> |.....................| 0.8272 | 1.284 | 1.162 | 1.186 | +#> | F| Forward Diff. | -35.81 | 1.797 | -0.3699 | -0.4180 | +#> |.....................| -1.164 | -17.54 | -6.949 | 5.683 | +#> |.....................| 5.938 | -8.368 | -6.484 | -5.617 | +#> |<span style='font-weight: bold;'> 29</span>| 462.64279 | 0.9976 | -1.018 | -0.9085 | -0.8918 | +#> |.....................| -0.8379 | -0.6938 | -0.7998 | -0.9297 | +#> |.....................| -0.9347 | -0.7837 | -0.8051 | -0.8136 | +#> | U| 462.64279 | 93.88 | -5.390 | -0.9876 | -0.1923 | +#> |.....................| 2.132 | 2.107 | 1.218 | 0.7146 | +#> |.....................| 0.8240 | 1.289 | 1.166 | 1.189 | +#> | X|<span style='font-weight: bold;'> 462.64279</span> | 93.88 | 0.004563 | 0.2714 | 0.8250 | +#> |.....................| 8.435 | 2.107 | 1.218 | 0.7146 | +#> |.....................| 0.8240 | 1.289 | 1.166 | 1.189 | +#> | F| Forward Diff. | 23.89 | 1.802 | -0.2695 | -0.3764 | +#> |.....................| -1.014 | -17.48 | -7.590 | 5.234 | +#> |.....................| 7.275 | -8.199 | -6.306 | -5.540 | +#> |<span style='font-weight: bold;'> 30</span>| 462.43086 | 0.9946 | -1.020 | -0.9083 | -0.8914 | +#> |.....................| -0.8367 | -0.6890 | -0.7974 | -0.9317 | +#> |.....................| -0.9381 | -0.7789 | -0.8017 | -0.8108 | +#> | U| 462.43086 | 93.61 | -5.391 | -0.9873 | -0.1919 | +#> |.....................| 2.134 | 2.111 | 1.219 | 0.7131 | +#> |.....................| 0.8211 | 1.295 | 1.169 | 1.193 | +#> | X|<span style='font-weight: bold;'> 462.43086</span> | 93.61 | 0.004556 | 0.2715 | 0.8254 | +#> |.....................| 8.445 | 2.111 | 1.219 | 0.7131 | +#> |.....................| 0.8211 | 1.295 | 1.169 | 1.193 | +#> | F| Forward Diff. | -22.12 | 1.763 | -0.3409 | -0.4033 | +#> |.....................| -1.105 | -16.76 | -6.743 | 5.132 | +#> |.....................| 5.573 | -7.935 | -6.123 | -5.337 | +#> |<span style='font-weight: bold;'> 31</span>| 462.24769 | 0.9981 | -1.021 | -0.9079 | -0.8909 | +#> |.....................| -0.8355 | -0.6838 | -0.7950 | -0.9332 | +#> |.....................| -0.9404 | -0.7741 | -0.7984 | -0.8080 | +#> | U| 462.24769 | 93.94 | -5.393 | -0.9870 | -0.1915 | +#> |.....................| 2.135 | 2.117 | 1.221 | 0.7120 | +#> |.....................| 0.8190 | 1.301 | 1.173 | 1.196 | +#> | X|<span style='font-weight: bold;'> 462.24769</span> | 93.94 | 0.004549 | 0.2715 | 0.8258 | +#> |.....................| 8.455 | 2.117 | 1.221 | 0.7120 | +#> |.....................| 0.8190 | 1.301 | 1.173 | 1.196 | +#> | F| Forward Diff. | 32.76 | 1.771 | -0.2440 | -0.3645 | +#> |.....................| -0.9678 | -16.08 | -6.874 | 5.077 | +#> |.....................| 5.606 | -7.758 | -5.959 | -5.256 | +#> |<span style='font-weight: bold;'> 32</span>| 462.04894 | 0.9949 | -1.023 | -0.9076 | -0.8904 | +#> |.....................| -0.8341 | -0.6790 | -0.7932 | -0.9353 | +#> |.....................| -0.9395 | -0.7687 | -0.7947 | -0.8049 | +#> | U| 462.04894 | 93.63 | -5.395 | -0.9866 | -0.1909 | +#> |.....................| 2.136 | 2.121 | 1.222 | 0.7104 | +#> |.....................| 0.8198 | 1.307 | 1.177 | 1.199 | +#> | X|<span style='font-weight: bold;'> 462.04894</span> | 93.63 | 0.004540 | 0.2716 | 0.8262 | +#> |.....................| 8.467 | 2.121 | 1.222 | 0.7104 | +#> |.....................| 0.8198 | 1.307 | 1.177 | 1.199 | +#> | F| Forward Diff. | -16.92 | 1.743 | -0.3189 | -0.3951 | +#> |.....................| -1.072 | -15.84 | -6.430 | 4.847 | +#> |.....................| 5.467 | -7.483 | -5.756 | -5.023 | +#> |<span style='font-weight: bold;'> 33</span>| 461.88553 | 0.9980 | -1.025 | -0.9073 | -0.8898 | +#> |.....................| -0.8327 | -0.6736 | -0.7912 | -0.9375 | +#> |.....................| -0.9397 | -0.7637 | -0.7912 | -0.8019 | +#> | U| 461.88553 | 93.92 | -5.397 | -0.9863 | -0.1904 | +#> |.....................| 2.138 | 2.126 | 1.223 | 0.7088 | +#> |.....................| 0.8197 | 1.313 | 1.181 | 1.203 | +#> | X|<span style='font-weight: bold;'> 461.88553</span> | 93.92 | 0.004531 | 0.2716 | 0.8266 | +#> |.....................| 8.479 | 2.126 | 1.223 | 0.7088 | +#> |.....................| 0.8197 | 1.313 | 1.181 | 1.203 | +#> | F| Forward Diff. | 30.55 | 1.755 | -0.2327 | -0.3563 | +#> |.....................| -0.9551 | -15.13 | -6.434 | 4.973 | +#> |.....................| 5.515 | -7.304 | -5.584 | -4.904 | +#> |<span style='font-weight: bold;'> 34</span>| 461.69674 | 0.9949 | -1.028 | -0.9069 | -0.8892 | +#> |.....................| -0.8309 | -0.6692 | -0.7896 | -0.9402 | +#> |.....................| -0.9399 | -0.7583 | -0.7876 | -0.7990 | +#> | U| 461.69674 | 93.63 | -5.400 | -0.9859 | -0.1897 | +#> |.....................| 2.139 | 2.131 | 1.224 | 0.7067 | +#> |.....................| 0.8195 | 1.320 | 1.185 | 1.206 | +#> | X|<span style='font-weight: bold;'> 461.69674</span> | 93.63 | 0.004519 | 0.2717 | 0.8272 | +#> |.....................| 8.494 | 2.131 | 1.224 | 0.7067 | +#> |.....................| 0.8195 | 1.320 | 1.185 | 1.206 | +#> | F| Forward Diff. | -16.57 | 1.720 | -0.3086 | -0.3856 | +#> |.....................| -1.039 | -14.73 | -5.908 | 4.823 | +#> |.....................| 5.359 | -7.008 | -5.393 | -4.695 | +#> |<span style='font-weight: bold;'> 35</span>| 461.54208 | 0.9978 | -1.031 | -0.9065 | -0.8885 | +#> |.....................| -0.8293 | -0.6648 | -0.7883 | -0.9440 | +#> |.....................| -0.9414 | -0.7533 | -0.7842 | -0.7963 | +#> | U| 461.54208 | 93.91 | -5.402 | -0.9855 | -0.1891 | +#> |.....................| 2.141 | 2.135 | 1.225 | 0.7038 | +#> |.....................| 0.8182 | 1.325 | 1.189 | 1.209 | +#> | X|<span style='font-weight: bold;'> 461.54208</span> | 93.91 | 0.004507 | 0.2718 | 0.8277 | +#> |.....................| 8.508 | 2.135 | 1.225 | 0.7038 | +#> |.....................| 0.8182 | 1.325 | 1.189 | 1.209 | +#> | F| Forward Diff. | 27.49 | 1.722 | -0.2172 | -0.3438 | +#> |.....................| -0.9069 | -13.76 | -5.979 | 4.702 | +#> |.....................| 5.353 | -6.828 | -5.231 | -4.587 | +#> |<span style='font-weight: bold;'> 36</span>| 461.38014 | 0.9949 | -1.034 | -0.9061 | -0.8878 | +#> |.....................| -0.8274 | -0.6624 | -0.7872 | -0.9482 | +#> |.....................| -0.9437 | -0.7482 | -0.7807 | -0.7935 | +#> | U| 461.38014 | 93.63 | -5.405 | -0.9851 | -0.1883 | +#> |.....................| 2.143 | 2.137 | 1.225 | 0.7007 | +#> |.....................| 0.8162 | 1.332 | 1.192 | 1.212 | +#> | X|<span style='font-weight: bold;'> 461.38014</span> | 93.63 | 0.004492 | 0.2719 | 0.8283 | +#> |.....................| 8.524 | 2.137 | 1.225 | 0.7007 | +#> |.....................| 0.8162 | 1.332 | 1.192 | 1.212 | +#> | F| Forward Diff. | -16.54 | 1.681 | -0.2967 | -0.3702 | +#> |.....................| -1.003 | -14.15 | -5.693 | 4.358 | +#> |.....................| 5.078 | -6.560 | -5.051 | -4.397 | +#> |<span style='font-weight: bold;'> 37</span>| 461.22820 | 0.9976 | -1.038 | -0.9057 | -0.8870 | +#> |.....................| -0.8255 | -0.6585 | -0.7854 | -0.9513 | +#> |.....................| -0.9460 | -0.7433 | -0.7774 | -0.7908 | +#> | U| 461.2282 | 93.88 | -5.409 | -0.9847 | -0.1876 | +#> |.....................| 2.145 | 2.141 | 1.226 | 0.6983 | +#> |.....................| 0.8141 | 1.337 | 1.196 | 1.215 | +#> | X|<span style='font-weight: bold;'> 461.2282</span> | 93.88 | 0.004476 | 0.2720 | 0.8290 | +#> |.....................| 8.540 | 2.141 | 1.226 | 0.6983 | +#> |.....................| 0.8141 | 1.337 | 1.196 | 1.215 | +#> | F| Forward Diff. | 22.68 | 1.675 | -0.2117 | -0.3293 | +#> |.....................| -0.8651 | -13.27 | -5.458 | 4.237 | +#> |.....................| 3.708 | -6.326 | -4.874 | -4.289 | +#> |<span style='font-weight: bold;'> 38</span>| 461.10880 | 0.9948 | -1.041 | -0.9053 | -0.8864 | +#> |.....................| -0.8238 | -0.6532 | -0.7845 | -0.9533 | +#> |.....................| -0.9419 | -0.7394 | -0.7747 | -0.7885 | +#> | U| 461.1088 | 93.62 | -5.412 | -0.9844 | -0.1869 | +#> |.....................| 2.146 | 2.146 | 1.227 | 0.6968 | +#> |.....................| 0.8177 | 1.342 | 1.199 | 1.218 | +#> | X|<span style='font-weight: bold;'> 461.1088</span> | 93.62 | 0.004461 | 0.2720 | 0.8295 | +#> |.....................| 8.555 | 2.146 | 1.227 | 0.6968 | +#> |.....................| 0.8177 | 1.342 | 1.199 | 1.218 | +#> | F| Forward Diff. | -17.23 | 1.655 | -0.2888 | -0.3567 | +#> |.....................| -0.9524 | -13.71 | -5.652 | 3.877 | +#> |.....................| 5.125 | -6.149 | -4.743 | -4.110 | +#> |<span style='font-weight: bold;'> 39</span>| 460.99174 | 0.9974 | -1.045 | -0.9049 | -0.8856 | +#> |.....................| -0.8221 | -0.6468 | -0.7824 | -0.9536 | +#> |.....................| -0.9388 | -0.7360 | -0.7723 | -0.7867 | +#> | U| 460.99174 | 93.87 | -5.416 | -0.9840 | -0.1862 | +#> |.....................| 2.148 | 2.153 | 1.228 | 0.6966 | +#> |.....................| 0.8204 | 1.346 | 1.202 | 1.220 | +#> | X|<span style='font-weight: bold;'> 460.99174</span> | 93.87 | 0.004444 | 0.2721 | 0.8301 | +#> |.....................| 8.569 | 2.153 | 1.228 | 0.6966 | +#> |.....................| 0.8204 | 1.346 | 1.202 | 1.220 | +#> | F| Forward Diff. | 21.44 | 1.663 | -0.2166 | -0.3206 | +#> |.....................| -0.8444 | -13.00 | -5.647 | 3.881 | +#> |.....................| 5.370 | -6.036 | -4.631 | -4.039 | +#> |<span style='font-weight: bold;'> 40</span>| 460.85317 | 0.9948 | -1.049 | -0.9044 | -0.8849 | +#> |.....................| -0.8203 | -0.6417 | -0.7791 | -0.9516 | +#> |.....................| -0.9438 | -0.7341 | -0.7712 | -0.7862 | +#> | U| 460.85317 | 93.62 | -5.420 | -0.9835 | -0.1854 | +#> |.....................| 2.150 | 2.158 | 1.230 | 0.6981 | +#> |.....................| 0.8161 | 1.348 | 1.203 | 1.220 | +#> | X|<span style='font-weight: bold;'> 460.85317</span> | 93.62 | 0.004425 | 0.2722 | 0.8308 | +#> |.....................| 8.585 | 2.158 | 1.230 | 0.6981 | +#> |.....................| 0.8161 | 1.348 | 1.203 | 1.220 | +#> | F| Forward Diff. | -17.08 | 1.613 | -0.2650 | -0.3380 | +#> |.....................| -0.8994 | -12.83 | -5.261 | 3.879 | +#> |.....................| 3.650 | -5.911 | -4.518 | -3.985 | +#> |<span style='font-weight: bold;'> 41</span>| 460.73362 | 0.9974 | -1.054 | -0.9040 | -0.8841 | +#> |.....................| -0.8184 | -0.6359 | -0.7754 | -0.9517 | +#> |.....................| -0.9423 | -0.7308 | -0.7693 | -0.7845 | +#> | U| 460.73362 | 93.86 | -5.425 | -0.9831 | -0.1846 | +#> |.....................| 2.152 | 2.163 | 1.232 | 0.6980 | +#> |.....................| 0.8173 | 1.352 | 1.205 | 1.222 | +#> | X|<span style='font-weight: bold;'> 460.73362</span> | 93.86 | 0.004404 | 0.2723 | 0.8314 | +#> |.....................| 8.601 | 2.163 | 1.232 | 0.6980 | +#> |.....................| 0.8173 | 1.352 | 1.205 | 1.222 | +#> | F| Forward Diff. | 20.68 | 1.612 | -0.1811 | -0.2966 | +#> |.....................| -0.7710 | -11.91 | -4.976 | 4.011 | +#> |.....................| 3.788 | -5.788 | -4.468 | -3.936 | +#> |<span style='font-weight: bold;'> 42</span>| 460.64877 | 0.9948 | -1.058 | -0.9038 | -0.8835 | +#> |.....................| -0.8171 | -0.6318 | -0.7737 | -0.9543 | +#> |.....................| -0.9372 | -0.7272 | -0.7669 | -0.7822 | +#> | U| 460.64877 | 93.62 | -5.429 | -0.9829 | -0.1841 | +#> |.....................| 2.153 | 2.167 | 1.233 | 0.6961 | +#> |.....................| 0.8219 | 1.357 | 1.208 | 1.225 | +#> | X|<span style='font-weight: bold;'> 460.64877</span> | 93.62 | 0.004387 | 0.2723 | 0.8319 | +#> |.....................| 8.612 | 2.167 | 1.233 | 0.6961 | +#> |.....................| 0.8219 | 1.357 | 1.208 | 1.225 | +#> | F| Forward Diff. | -16.17 | 1.594 | -0.2646 | -0.3254 | +#> |.....................| -0.8335 | -11.77 | -4.666 | 3.810 | +#> |.....................| 5.289 | -5.625 | -4.348 | -3.754 | +#> |<span style='font-weight: bold;'> 43</span>| 460.54180 | 0.9972 | -1.063 | -0.9035 | -0.8829 | +#> |.....................| -0.8158 | -0.6297 | -0.7745 | -0.9584 | +#> |.....................| -0.9393 | -0.7227 | -0.7634 | -0.7794 | +#> | U| 460.5418 | 93.85 | -5.434 | -0.9826 | -0.1834 | +#> |.....................| 2.154 | 2.169 | 1.233 | 0.6929 | +#> |.....................| 0.8200 | 1.362 | 1.211 | 1.228 | +#> | X|<span style='font-weight: bold;'> 460.5418</span> | 93.85 | 0.004366 | 0.2724 | 0.8324 | +#> |.....................| 8.623 | 2.169 | 1.233 | 0.6929 | +#> |.....................| 0.8200 | 1.362 | 1.211 | 1.228 | +#> | F| Forward Diff. | 18.48 | 1.582 | -0.1851 | -0.2851 | +#> |.....................| -0.7462 | -11.38 | -4.808 | 3.651 | +#> |.....................| 5.261 | -5.402 | -4.159 | -3.623 | +#> |<span style='font-weight: bold;'> 44</span>| 460.43711 | 0.9948 | -1.067 | -0.9032 | -0.8823 | +#> |.....................| -0.8147 | -0.6284 | -0.7753 | -0.9609 | +#> |.....................| -0.9464 | -0.7199 | -0.7613 | -0.7778 | +#> | U| 460.43711 | 93.63 | -5.438 | -0.9823 | -0.1829 | +#> |.....................| 2.156 | 2.171 | 1.232 | 0.6911 | +#> |.....................| 0.8138 | 1.365 | 1.214 | 1.230 | +#> | X|<span style='font-weight: bold;'> 460.43711</span> | 93.63 | 0.004347 | 0.2724 | 0.8329 | +#> |.....................| 8.632 | 2.171 | 1.232 | 0.6911 | +#> |.....................| 0.8138 | 1.365 | 1.214 | 1.230 | +#> |<span style='font-weight: bold;'> 45</span>| 460.35910 | 0.9948 | -1.072 | -0.9029 | -0.8817 | +#> |.....................| -0.8135 | -0.6285 | -0.7770 | -0.9633 | +#> |.....................| -0.9542 | -0.7172 | -0.7594 | -0.7765 | +#> | U| 460.3591 | 93.63 | -5.443 | -0.9820 | -0.1822 | +#> |.....................| 2.157 | 2.170 | 1.231 | 0.6893 | +#> |.....................| 0.8069 | 1.368 | 1.216 | 1.231 | +#> | X|<span style='font-weight: bold;'> 460.3591</span> | 93.63 | 0.004325 | 0.2725 | 0.8334 | +#> |.....................| 8.643 | 2.170 | 1.231 | 0.6893 | +#> |.....................| 0.8069 | 1.368 | 1.216 | 1.231 | +#> |<span style='font-weight: bold;'> 46</span>| 460.06586 | 0.9948 | -1.095 | -0.9016 | -0.8789 | +#> |.....................| -0.8080 | -0.6294 | -0.7850 | -0.9744 | +#> |.....................| -0.9902 | -0.7052 | -0.7507 | -0.7704 | +#> | U| 460.06586 | 93.63 | -5.466 | -0.9807 | -0.1794 | +#> |.....................| 2.162 | 2.170 | 1.227 | 0.6809 | +#> |.....................| 0.7753 | 1.383 | 1.225 | 1.238 | +#> | X|<span style='font-weight: bold;'> 460.06586</span> | 93.63 | 0.004227 | 0.2728 | 0.8358 | +#> |.....................| 8.691 | 2.170 | 1.227 | 0.6809 | +#> |.....................| 0.7753 | 1.383 | 1.225 | 1.238 | +#> |<span style='font-weight: bold;'> 47</span>| 459.86897 | 0.9949 | -1.169 | -0.8972 | -0.8697 | +#> |.....................| -0.7899 | -0.6321 | -0.8109 | -1.010 | +#> |.....................| -1.107 | -0.6662 | -0.7224 | -0.7508 | +#> | U| 459.86897 | 93.63 | -5.541 | -0.9763 | -0.1702 | +#> |.....................| 2.180 | 2.167 | 1.211 | 0.6537 | +#> |.....................| 0.6731 | 1.429 | 1.256 | 1.260 | +#> | X|<span style='font-weight: bold;'> 459.86897</span> | 93.63 | 0.003924 | 0.2736 | 0.8435 | +#> |.....................| 8.849 | 2.167 | 1.211 | 0.6537 | +#> |.....................| 0.6731 | 1.429 | 1.256 | 1.260 | +#> | F| Forward Diff. | -18.09 | 0.8663 | 0.2544 | 0.003114 | +#> |.....................| -0.1212 | -11.64 | -7.047 | 0.1395 | +#> |.....................| -6.727 | -2.881 | -1.866 | -2.263 | +#> |<span style='font-weight: bold;'> 48</span>| 458.58262 | 0.9946 | -1.323 | -0.9067 | -0.8597 | +#> |.....................| -0.7710 | -0.5295 | -0.7001 | -0.9650 | +#> |.....................| -1.113 | -0.6398 | -0.7228 | -0.7390 | +#> | U| 458.58262 | 93.60 | -5.695 | -0.9858 | -0.1602 | +#> |.....................| 2.199 | 2.267 | 1.277 | 0.6880 | +#> |.....................| 0.6674 | 1.460 | 1.256 | 1.273 | +#> | X|<span style='font-weight: bold;'> 458.58262</span> | 93.60 | 0.003363 | 0.2717 | 0.8520 | +#> |.....................| 9.019 | 2.267 | 1.277 | 0.6880 | +#> |.....................| 0.6674 | 1.460 | 1.256 | 1.273 | +#> | F| Forward Diff. | -24.91 | 0.5848 | -0.03458 | 0.2475 | +#> |.....................| 0.3762 | -4.573 | -0.04388 | 1.648 | +#> |.....................| -5.878 | -2.073 | -1.935 | -2.146 | +#> |<span style='font-weight: bold;'> 49</span>| 460.44377 | 0.9922 | -1.558 | -0.9059 | -0.8818 | +#> |.....................| -0.8081 | -0.3861 | -0.8607 | -1.070 | +#> |.....................| -0.9432 | -0.5131 | -0.5915 | -0.5851 | +#> | U| 460.44377 | 93.38 | -5.929 | -0.9849 | -0.1824 | +#> |.....................| 2.162 | 2.407 | 1.182 | 0.6086 | +#> |.....................| 0.8166 | 1.611 | 1.400 | 1.447 | +#> | X|<span style='font-weight: bold;'> 460.44377</span> | 93.38 | 0.002660 | 0.2719 | 0.8333 | +#> |.....................| 8.690 | 2.407 | 1.182 | 0.6086 | +#> |.....................| 0.8166 | 1.611 | 1.400 | 1.447 | +#> |<span style='font-weight: bold;'> 50</span>| 458.18867 | 0.9958 | -1.393 | -0.9065 | -0.8663 | +#> |.....................| -0.7821 | -0.4865 | -0.7479 | -0.9965 | +#> |.....................| -1.062 | -0.6019 | -0.6835 | -0.6930 | +#> | U| 458.18867 | 93.71 | -5.765 | -0.9855 | -0.1668 | +#> |.....................| 2.188 | 2.309 | 1.248 | 0.6642 | +#> |.....................| 0.7122 | 1.506 | 1.299 | 1.325 | +#> | X|<span style='font-weight: bold;'> 458.18867</span> | 93.71 | 0.003136 | 0.2718 | 0.8463 | +#> |.....................| 8.919 | 2.309 | 1.248 | 0.6642 | +#> |.....................| 0.7122 | 1.506 | 1.299 | 1.325 | +#> | F| Forward Diff. | -3.049 | 0.4396 | -0.1330 | 0.02964 | +#> |.....................| -0.08039 | -2.599 | -3.012 | -0.1957 | +#> |.....................| -2.463 | -0.6721 | 0.3494 | 0.7476 | +#> |<span style='font-weight: bold;'> 51</span>| 458.45407 | 0.9980 | -1.449 | -0.8787 | -0.8738 | +#> |.....................| -0.7836 | -0.4935 | -0.7244 | -1.061 | +#> |.....................| -1.034 | -0.5419 | -0.6952 | -0.7610 | +#> | U| 458.45407 | 93.92 | -5.821 | -0.9579 | -0.1743 | +#> |.....................| 2.187 | 2.302 | 1.262 | 0.6155 | +#> |.....................| 0.7366 | 1.577 | 1.286 | 1.249 | +#> | X|<span style='font-weight: bold;'> 458.45407</span> | 93.92 | 0.002965 | 0.2773 | 0.8400 | +#> |.....................| 8.906 | 2.302 | 1.262 | 0.6155 | +#> |.....................| 0.7366 | 1.577 | 1.286 | 1.249 | +#> |<span style='font-weight: bold;'> 52</span>| 458.19883 | 0.9985 | -1.406 | -0.9001 | -0.8680 | +#> |.....................| -0.7823 | -0.4861 | -0.7404 | -1.011 | +#> |.....................| -1.054 | -0.5879 | -0.6864 | -0.7089 | +#> | U| 458.19883 | 93.97 | -5.778 | -0.9792 | -0.1685 | +#> |.....................| 2.188 | 2.309 | 1.253 | 0.6534 | +#> |.....................| 0.7193 | 1.522 | 1.296 | 1.307 | +#> | X|<span style='font-weight: bold;'> 458.19883</span> | 93.97 | 0.003096 | 0.2731 | 0.8449 | +#> |.....................| 8.917 | 2.309 | 1.253 | 0.6534 | +#> |.....................| 0.7193 | 1.522 | 1.296 | 1.307 | +#> |<span style='font-weight: bold;'> 53</span>| 458.20478 | 0.9986 | -1.399 | -0.9039 | -0.8670 | +#> |.....................| -0.7821 | -0.4848 | -0.7433 | -1.002 | +#> |.....................| -1.058 | -0.5961 | -0.6848 | -0.6996 | +#> | U| 458.20478 | 93.98 | -5.770 | -0.9830 | -0.1675 | +#> |.....................| 2.188 | 2.311 | 1.251 | 0.6601 | +#> |.....................| 0.7162 | 1.512 | 1.297 | 1.318 | +#> | X|<span style='font-weight: bold;'> 458.20478</span> | 93.98 | 0.003120 | 0.2723 | 0.8458 | +#> |.....................| 8.919 | 2.311 | 1.251 | 0.6601 | +#> |.....................| 0.7162 | 1.512 | 1.297 | 1.318 | +#> |<span style='font-weight: bold;'> 54</span>| 458.21371 | 0.9986 | -1.394 | -0.9063 | -0.8663 | +#> |.....................| -0.7820 | -0.4840 | -0.7451 | -0.9963 | +#> |.....................| -1.060 | -0.6013 | -0.6838 | -0.6937 | +#> | U| 458.21371 | 93.98 | -5.765 | -0.9854 | -0.1669 | +#> |.....................| 2.188 | 2.311 | 1.250 | 0.6644 | +#> |.....................| 0.7142 | 1.506 | 1.298 | 1.325 | +#> | X|<span style='font-weight: bold;'> 458.21371</span> | 93.98 | 0.003135 | 0.2718 | 0.8463 | +#> |.....................| 8.920 | 2.311 | 1.250 | 0.6644 | +#> |.....................| 0.7142 | 1.506 | 1.298 | 1.325 | +#> |<span style='font-weight: bold;'> 55</span>| 458.18572 | 0.9965 | -1.393 | -0.9064 | -0.8663 | +#> |.....................| -0.7820 | -0.4858 | -0.7472 | -0.9964 | +#> |.....................| -1.062 | -0.6017 | -0.6836 | -0.6932 | +#> | U| 458.18572 | 93.79 | -5.765 | -0.9855 | -0.1668 | +#> |.....................| 2.188 | 2.310 | 1.249 | 0.6643 | +#> |.....................| 0.7128 | 1.506 | 1.299 | 1.325 | +#> | X|<span style='font-weight: bold;'> 458.18572</span> | 93.79 | 0.003136 | 0.2718 | 0.8463 | +#> |.....................| 8.919 | 2.310 | 1.249 | 0.6643 | +#> |.....................| 0.7128 | 1.506 | 1.299 | 1.325 | +#> | F| Forward Diff. | 5.905 | 0.4355 | -0.1157 | 0.02634 | +#> |.....................| -0.05151 | -1.735 | -2.785 | -0.07657 | +#> |.....................| -2.587 | -0.1320 | 0.06282 | 0.8041 | +#> |<span style='font-weight: bold;'> 56</span>| 458.18221 | 0.9957 | -1.394 | -0.9063 | -0.8663 | +#> |.....................| -0.7820 | -0.4856 | -0.7465 | -0.9968 | +#> |.....................| -1.061 | -0.6016 | -0.6835 | -0.6937 | +#> | U| 458.18221 | 93.70 | -5.765 | -0.9853 | -0.1669 | +#> |.....................| 2.188 | 2.310 | 1.249 | 0.6640 | +#> |.....................| 0.7132 | 1.506 | 1.299 | 1.325 | +#> | X|<span style='font-weight: bold;'> 458.18221</span> | 93.70 | 0.003135 | 0.2718 | 0.8463 | +#> |.....................| 8.920 | 2.310 | 1.249 | 0.6640 | +#> |.....................| 0.7132 | 1.506 | 1.299 | 1.325 | +#> | F| Forward Diff. | -4.339 | 0.4378 | -0.1282 | 0.03581 | +#> |.....................| -0.09329 | -1.978 | -2.551 | -0.01933 | +#> |.....................| -3.951 | -0.1424 | 0.01723 | 0.8408 | +#> |<span style='font-weight: bold;'> 57</span>| 458.17882 | 0.9963 | -1.394 | -0.9061 | -0.8663 | +#> |.....................| -0.7819 | -0.4855 | -0.7459 | -0.9972 | +#> |.....................| -1.060 | -0.6016 | -0.6832 | -0.6941 | +#> | U| 458.17882 | 93.76 | -5.766 | -0.9852 | -0.1669 | +#> |.....................| 2.188 | 2.310 | 1.250 | 0.6637 | +#> |.....................| 0.7139 | 1.506 | 1.299 | 1.324 | +#> | X|<span style='font-weight: bold;'> 458.17882</span> | 93.76 | 0.003134 | 0.2719 | 0.8463 | +#> |.....................| 8.920 | 2.310 | 1.250 | 0.6637 | +#> |.....................| 0.7139 | 1.506 | 1.299 | 1.324 | +#> | F| Forward Diff. | 2.737 | 0.4289 | -0.1193 | 0.04099 | +#> |.....................| -0.07175 | -2.104 | -2.655 | -0.1084 | +#> |.....................| -2.489 | -0.08715 | 0.1037 | 0.7775 | +#> |<span style='font-weight: bold;'> 58</span>| 458.17628 | 0.9955 | -1.394 | -0.9061 | -0.8663 | +#> |.....................| -0.7819 | -0.4849 | -0.7451 | -0.9972 | +#> |.....................| -1.060 | -0.6016 | -0.6832 | -0.6943 | +#> | U| 458.17628 | 93.69 | -5.766 | -0.9851 | -0.1669 | +#> |.....................| 2.188 | 2.311 | 1.250 | 0.6637 | +#> |.....................| 0.7145 | 1.506 | 1.299 | 1.324 | +#> | X|<span style='font-weight: bold;'> 458.17628</span> | 93.69 | 0.003133 | 0.2719 | 0.8463 | +#> |.....................| 8.920 | 2.311 | 1.250 | 0.6637 | +#> |.....................| 0.7145 | 1.506 | 1.299 | 1.324 | +#> | F| Forward Diff. | -5.829 | 0.4364 | -0.1238 | 0.03009 | +#> |.....................| -0.09450 | -1.871 | -2.366 | 0.01771 | +#> |.....................| -2.486 | -0.08743 | 0.03350 | 0.7982 | +#> |<span style='font-weight: bold;'> 59</span>| 458.17323 | 0.9963 | -1.395 | -0.9059 | -0.8664 | +#> |.....................| -0.7819 | -0.4846 | -0.7446 | -0.9977 | +#> |.....................| -1.059 | -0.6018 | -0.6829 | -0.6949 | +#> | U| 458.17323 | 93.77 | -5.766 | -0.9850 | -0.1669 | +#> |.....................| 2.188 | 2.311 | 1.250 | 0.6633 | +#> |.....................| 0.7149 | 1.506 | 1.299 | 1.323 | +#> | X|<span style='font-weight: bold;'> 458.17323</span> | 93.77 | 0.003132 | 0.2719 | 0.8463 | +#> |.....................| 8.921 | 2.311 | 1.250 | 0.6633 | +#> |.....................| 0.7149 | 1.506 | 1.299 | 1.323 | +#> | F| Forward Diff. | 3.135 | 0.4259 | -0.1111 | 0.03860 | +#> |.....................| -0.07150 | -1.713 | -2.294 | -0.1635 | +#> |.....................| -3.755 | -0.1071 | 0.1242 | 0.7274 | +#> |<span style='font-weight: bold;'> 60</span>| 458.17055 | 0.9957 | -1.395 | -0.9058 | -0.8664 | +#> |.....................| -0.7818 | -0.4843 | -0.7440 | -0.9980 | +#> |.....................| -1.058 | -0.6018 | -0.6828 | -0.6953 | +#> | U| 458.17055 | 93.70 | -5.766 | -0.9848 | -0.1669 | +#> |.....................| 2.188 | 2.311 | 1.251 | 0.6631 | +#> |.....................| 0.7157 | 1.506 | 1.300 | 1.323 | +#> | X|<span style='font-weight: bold;'> 458.17055</span> | 93.70 | 0.003131 | 0.2719 | 0.8463 | +#> |.....................| 8.921 | 2.311 | 1.251 | 0.6631 | +#> |.....................| 0.7157 | 1.506 | 1.300 | 1.323 | +#> | F| Forward Diff. | -3.767 | 0.4346 | -0.1027 | 0.03296 | +#> |.....................| -0.07232 | -2.503 | -3.089 | -0.1630 | +#> |.....................| -2.382 | -0.08570 | 0.1151 | 0.7161 | +#> |<span style='font-weight: bold;'> 61</span>| 458.16819 | 0.9965 | -1.395 | -0.9058 | -0.8664 | +#> |.....................| -0.7818 | -0.4837 | -0.7432 | -0.9981 | +#> |.....................| -1.058 | -0.6018 | -0.6828 | -0.6955 | +#> | U| 458.16819 | 93.79 | -5.767 | -0.9848 | -0.1669 | +#> |.....................| 2.188 | 2.312 | 1.251 | 0.6630 | +#> |.....................| 0.7162 | 1.506 | 1.300 | 1.322 | +#> | X|<span style='font-weight: bold;'> 458.16819</span> | 93.79 | 0.003130 | 0.2719 | 0.8462 | +#> |.....................| 8.921 | 2.312 | 1.251 | 0.6630 | +#> |.....................| 0.7162 | 1.506 | 1.300 | 1.322 | +#> | F| Forward Diff. | 6.568 | 0.4333 | -0.07429 | 0.03599 | +#> |.....................| -0.03802 | -2.553 | -3.191 | -0.5393 | +#> |.....................| -0.9714 | -0.8035 | 0.1031 | 0.6902 | +#> |<span style='font-weight: bold;'> 62</span>| 458.16513 | 0.9957 | -1.396 | -0.9056 | -0.8666 | +#> |.....................| -0.7821 | -0.4835 | -0.7425 | -0.9983 | +#> |.....................| -1.057 | -0.6019 | -0.6824 | -0.6959 | +#> | U| 458.16513 | 93.70 | -5.767 | -0.9847 | -0.1672 | +#> |.....................| 2.188 | 2.312 | 1.252 | 0.6629 | +#> |.....................| 0.7164 | 1.506 | 1.300 | 1.322 | +#> | X|<span style='font-weight: bold;'> 458.16513</span> | 93.70 | 0.003129 | 0.2720 | 0.8461 | +#> |.....................| 8.919 | 2.312 | 1.252 | 0.6629 | +#> |.....................| 0.7164 | 1.506 | 1.300 | 1.322 | +#> | F| Forward Diff. | -3.933 | 0.4306 | -0.09800 | 0.02413 | +#> |.....................| -0.09225 | -1.469 | -2.000 | -0.05194 | +#> |.....................| -3.675 | -0.07209 | 0.09082 | 0.7196 | +#> |<span style='font-weight: bold;'> 63</span>| 458.16261 | 0.9962 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4834 | -0.7420 | -0.9986 | +#> |.....................| -1.057 | -0.6017 | -0.6820 | -0.6964 | +#> | U| 458.16261 | 93.76 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.312 | 1.252 | 0.6626 | +#> |.....................| 0.7170 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16261</span> | 93.76 | 0.003127 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.312 | 1.252 | 0.6626 | +#> |.....................| 0.7170 | 1.506 | 1.300 | 1.321 | +#> | F| Forward Diff. | 2.233 | 0.4197 | -0.09277 | 0.03004 | +#> |.....................| -0.08165 | -1.772 | -2.245 | -0.08206 | +#> |.....................| -2.339 | -0.1510 | 0.07888 | 0.6887 | +#> |<span style='font-weight: bold;'> 64</span>| 458.16062 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16062 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16062</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | M| Mixed Diff. | -6.515 | 0.4169 | -0.1028 |-1.670e+05 | +#> |.....................| -0.1097 | -2.956 | -2.997 | -0.5657 | +#> |.....................| -4.153 | -0.6659 | -0.7853 | 0.1256 | +#> |<span style='font-weight: bold;'> 65</span>| 458.16519 | 0.9948 | -1.397 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4822 | -0.7405 | -0.9986 | +#> |.....................| -1.055 | -0.6016 | -0.6821 | -0.6969 | +#> | U| 458.16519 | 93.62 | -5.768 | -0.9844 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.253 | 0.6627 | +#> |.....................| 0.7183 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16519</span> | 93.62 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.253 | 0.6627 | +#> |.....................| 0.7183 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 66</span>| 458.16209 | 0.9951 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4825 | -0.7409 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6968 | +#> | U| 458.16209 | 93.65 | -5.768 | -0.9844 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.253 | 0.6626 | +#> |.....................| 0.7180 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16209</span> | 93.65 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.253 | 0.6626 | +#> |.....................| 0.7180 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 67</span>| 458.16115 | 0.9953 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4827 | -0.7410 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16115 | 93.67 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.253 | 0.6626 | +#> |.....................| 0.7178 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16115</span> | 93.67 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.253 | 0.6626 | +#> |.....................| 0.7178 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 68</span>| 458.16084 | 0.9954 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4827 | -0.7411 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16084 | 93.68 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16084</span> | 93.68 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 69</span>| 458.16072 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16072 | 93.68 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16072</span> | 93.68 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 70</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 71</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 72</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 73</span>| 458.16072 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16072 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16072</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 74</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 75</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 76</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 77</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 78</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 79</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 80</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 81</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 82</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 83</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 84</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 85</span>| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16068</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 86</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 87</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 88</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 89</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 90</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 91</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> |<span style='font-weight: bold;'> 92</span>| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | +#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | +#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | +#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | +#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> | X|<span style='font-weight: bold;'> 458.16067</span> | 93.69 | 0.003126 | 0.2720 | 0.8460 | +#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | +#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | +#> 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: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</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: gradient problems with initial estimate and covariance; see $scaleInfo</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'>#> 1: 93.5791 -5.6199 -2.0817 -3.9984 -1.2037 0.1481 4.5359 1.6042 1.1515 2.4545 0.4989 0.5230 19.1822 10.0277 +#> 2: 93.5157 -5.6781 -1.9742 -4.0546 -1.1333 0.1109 4.4678 1.5240 1.0939 2.3318 0.4740 0.6338 12.8885 7.4711 +#> 3: 93.2898 -5.7047 -1.8559 -4.1328 -1.0939 0.0438 5.0096 1.4478 1.1939 2.2152 0.4503 0.6021 11.0381 5.1444 +#> 4: 93.0426 -5.7814 -1.8501 -4.1839 -1.0410 0.0594 6.3802 1.4778 1.2229 2.1556 0.4278 0.5972 10.2381 4.4049 +#> 5: 92.9134 -5.8482 -1.8162 -4.2071 -1.0582 0.0732 6.9858 1.8242 1.1718 2.3151 0.4064 0.5822 9.8642 4.4088 +#> 6: 92.7655 -5.8047 -1.8535 -4.2041 -0.9870 0.0611 6.6365 1.7739 1.1619 2.2910 0.3861 0.5531 8.6374 4.0594 +#> 7: 93.0259 -5.8252 -1.9173 -4.2093 -0.9549 0.0995 6.3047 2.2731 1.1038 2.1765 0.3668 0.5255 9.0819 3.0678 +#> 8: 93.1406 -5.7510 -1.9019 -4.2213 -0.9559 0.1508 5.9894 2.5908 1.0919 2.0948 0.3484 0.4992 8.3332 2.3703 +#> 9: 93.3980 -5.5162 -1.9512 -4.2707 -0.9026 0.1570 5.6900 2.4612 1.0373 2.3579 0.3310 0.4742 7.7762 2.1692 +#> 10: 93.5148 -5.4966 -1.9184 -4.2482 -0.9045 0.1396 5.4055 2.3382 0.9855 2.2400 0.3145 0.4652 7.5796 1.9233 +#> 11: 93.1833 -5.5679 -1.9315 -4.2869 -0.9148 0.1713 5.1352 2.2213 0.9362 2.1465 0.2987 0.4622 7.5181 1.8003 +#> 12: 92.9902 -5.7249 -1.9741 -4.3054 -0.9148 0.1927 4.8784 2.9298 0.8975 2.3858 0.2838 0.5005 7.3638 1.7074 +#> 13: 92.5821 -5.7143 -1.9662 -4.3403 -0.8940 0.1595 4.6345 2.8035 0.9305 2.5370 0.2696 0.4755 7.1732 1.6333 +#> 14: 92.1385 -5.5571 -1.9874 -4.2935 -0.8815 0.1762 5.5000 2.6634 0.9011 2.4102 0.2561 0.5012 7.1920 1.7020 +#> 15: 92.1244 -5.5198 -1.9701 -4.3134 -0.8984 0.1704 5.2250 2.5302 0.9705 2.4401 0.2433 0.4839 7.4072 1.6160 +#> 16: 92.6306 -5.4666 -1.9776 -4.3023 -0.8906 0.1737 4.9638 2.4037 1.0278 2.3181 0.2312 0.5183 7.5105 1.6033 +#> 17: 92.5769 -5.4886 -2.0034 -4.3892 -0.8863 0.1967 5.6659 2.2835 1.0796 2.7700 0.2196 0.5138 7.6495 1.4656 +#> 18: 92.0321 -5.5257 -2.0086 -4.3651 -0.8914 0.1869 6.5345 2.1693 1.0771 2.6315 0.2086 0.4906 7.8248 1.4297 +#> 19: 92.5497 -5.5509 -1.9892 -4.3590 -0.8947 0.2148 6.2078 2.0936 1.0629 2.4999 0.1992 0.4847 7.8809 1.4881 +#> 20: 92.3638 -5.5322 -1.9943 -4.3507 -0.9153 0.1787 6.2176 2.1784 1.0242 2.5190 0.1923 0.4604 7.7900 1.5147 +#> 21: 92.3946 -5.5963 -1.9984 -4.3234 -0.9031 0.1961 5.9067 2.4305 0.9962 2.3930 0.1827 0.4374 7.6671 1.5182 +#> 22: 92.3389 -5.7757 -1.9686 -4.3485 -0.9054 0.1677 5.6113 3.2010 0.9650 2.4493 0.1907 0.4220 7.1305 1.5425 +#> 23: 92.5054 -5.7766 -1.9947 -4.3613 -0.9069 0.1781 5.3308 3.2506 0.9932 2.5478 0.1868 0.4217 7.6690 1.4526 +#> 24: 92.5865 -5.8597 -1.9691 -4.4676 -0.8950 0.1755 5.0642 4.0954 0.9471 3.5482 0.1891 0.4438 7.2397 1.6349 +#> 25: 92.3775 -5.8727 -1.9577 -4.4964 -0.8955 0.1477 4.8354 3.8906 0.9557 3.5054 0.2003 0.4216 6.7966 1.5576 +#> 26: 92.2427 -5.9696 -1.9672 -4.4384 -0.9063 0.1733 4.5937 4.0917 0.9924 3.3326 0.1918 0.4341 6.9377 1.5723 +#> 27: 92.7312 -5.8434 -1.9590 -4.3655 -0.9095 0.1669 4.4448 3.8871 1.0032 3.1660 0.2006 0.4320 7.1970 1.5118 +#> 28: 92.7033 -5.8759 -1.9827 -4.3776 -0.9145 0.1844 4.5885 3.6928 0.9750 3.0077 0.2093 0.4104 6.8745 1.4865 +#> 29: 92.5242 -5.8627 -1.9806 -4.4623 -0.9142 0.2069 5.2823 3.5081 0.9748 3.5849 0.2098 0.4120 6.9735 1.5115 +#> 30: 92.2312 -5.8332 -1.9739 -4.3699 -0.9100 0.1624 5.0182 3.5473 0.9553 3.4056 0.2102 0.3914 6.8547 1.5172 +#> 31: 92.1659 -5.7898 -1.9642 -4.3956 -0.9105 0.1625 4.7672 3.3700 0.9442 3.2571 0.2071 0.3795 6.5191 1.5452 +#> 32: 92.5436 -5.7968 -1.9642 -4.3987 -0.9179 0.1110 4.5289 3.2015 0.9382 3.0943 0.2024 0.3605 6.5921 1.5105 +#> 33: 92.7837 -5.8155 -1.9539 -4.3145 -0.9157 0.1398 4.3024 3.3616 0.9119 2.9395 0.1981 0.3494 6.2870 1.6036 +#> 34: 93.0500 -5.8853 -1.9587 -4.2507 -0.9146 0.1455 4.0873 4.3592 0.9129 2.7926 0.1961 0.3319 6.3493 1.6059 +#> 35: 93.1208 -5.8581 -1.9614 -4.2722 -0.9127 0.1255 4.0645 4.1413 0.9262 2.6529 0.1964 0.3157 6.1337 1.6010 +#> 36: 93.1002 -5.8598 -1.9886 -4.2092 -0.9076 0.1192 4.2392 3.9342 0.9566 2.5203 0.2015 0.3222 6.5326 1.4847 +#> 37: 92.8242 -5.6228 -1.9655 -4.2054 -0.9099 0.1010 6.8190 3.7375 0.9087 2.3943 0.1942 0.3141 6.2613 1.6015 +#> 38: 93.1512 -5.5747 -1.9736 -4.2054 -0.9115 0.0887 6.4781 3.5506 0.8904 2.2746 0.1930 0.3298 6.4960 1.5750 +#> 39: 92.9998 -5.5416 -1.9750 -4.2124 -0.9101 0.0953 6.1542 3.3731 0.9013 2.1608 0.1858 0.3204 6.6470 1.5705 +#> 40: 93.2158 -5.7057 -1.9587 -4.2101 -0.9122 0.0630 5.8464 3.2044 0.9350 2.1357 0.1851 0.3044 6.6842 1.5069 +#> 41: 93.0585 -5.5453 -1.9306 -4.2101 -0.9021 0.0531 5.5541 3.0442 0.9458 2.1673 0.1851 0.2892 6.3923 1.5949 +#> 42: 93.0958 -5.4512 -1.9484 -4.2227 -0.8959 0.0649 5.2764 2.8920 0.9571 2.1930 0.1829 0.2747 6.3082 1.5985 +#> 43: 93.2333 -5.5398 -1.9391 -4.2400 -0.8972 0.0870 5.0126 2.7474 0.9913 2.2830 0.1984 0.2720 6.0810 1.6131 +#> 44: 92.9479 -5.5648 -1.9227 -4.2468 -0.9104 0.0963 4.7620 2.6100 0.9682 2.2976 0.2038 0.2648 5.8461 1.6955 +#> 45: 93.0244 -5.6247 -1.9379 -4.2588 -0.9093 0.0865 5.2997 2.4894 0.9837 2.3100 0.2039 0.2844 5.9439 1.6121 +#> 46: 92.5959 -5.6240 -1.9513 -4.2588 -0.9172 0.0923 5.3111 2.5081 1.0158 2.3100 0.2050 0.2702 6.0141 1.6189 +#> 47: 92.8483 -5.5823 -1.9529 -4.2684 -0.9194 0.0770 6.2469 2.3827 1.0328 2.3567 0.2104 0.2567 6.0472 1.5858 +#> 48: 92.6210 -5.6336 -1.9379 -4.3049 -0.9054 0.0747 7.5721 2.3177 1.0379 2.5427 0.2103 0.2439 6.0431 1.5860 +#> 49: 92.6337 -5.6723 -1.9486 -4.2879 -0.8985 0.0773 7.1935 2.6572 1.0181 2.4515 0.2056 0.2559 6.0895 1.5217 +#> 50: 92.2413 -5.7138 -1.9587 -4.2804 -0.8926 0.0774 8.1551 2.9779 1.0282 2.4807 0.2090 0.2510 6.2355 1.5223 +#> 51: 92.2223 -5.6765 -1.9496 -4.2971 -0.8840 0.1034 7.7638 3.0625 1.0017 2.6024 0.2075 0.2384 6.3495 1.6621 +#> 52: 92.4242 -5.6573 -1.9408 -4.2943 -0.8993 0.1136 8.3190 2.9093 1.0044 2.4822 0.2163 0.2411 6.0611 1.5241 +#> 53: 92.6070 -5.5921 -1.9397 -4.2873 -0.9046 0.0904 10.3681 2.7639 1.0098 2.4895 0.2194 0.2393 6.1728 1.5264 +#> 54: 92.9339 -5.6194 -1.9292 -4.2950 -0.9006 0.1010 9.9150 2.6257 1.0088 2.4268 0.2346 0.2492 5.9203 1.5693 +#> 55: 93.4640 -5.5851 -1.8969 -4.2614 -0.9065 0.1058 10.3986 2.4944 1.0204 2.3055 0.2257 0.2403 5.7030 1.5717 +#> 56: 93.3646 -5.5851 -1.9127 -4.3130 -0.9196 0.1077 9.8787 2.3697 1.0067 2.6259 0.2261 0.2370 5.7389 1.5053 +#> 57: 93.5408 -5.4962 -1.9150 -4.3285 -0.9148 0.0880 9.3848 2.2512 0.9903 2.6118 0.2160 0.2494 5.7530 1.5780 +#> 58: 93.5195 -5.4358 -1.9459 -4.3041 -0.9076 0.1022 8.9155 2.1386 1.0220 2.5253 0.2220 0.2578 6.0138 1.4494 +#> 59: 93.5906 -5.4624 -1.9507 -4.3065 -0.9124 0.1374 8.4698 2.0317 1.0230 2.5539 0.2212 0.2449 5.7538 1.6021 +#> 60: 93.3308 -5.3784 -1.9540 -4.2417 -0.9173 0.1337 8.0463 1.9301 1.0298 2.4262 0.2173 0.2327 5.8841 1.4634 +#> 61: 93.3506 -5.4000 -1.9688 -4.2389 -0.9130 0.0942 7.6440 1.8336 1.0437 2.3049 0.2216 0.2210 6.0098 1.4243 +#> 62: 93.6969 -5.4175 -1.9467 -4.2389 -0.9135 0.1315 7.2618 1.7419 1.0213 2.2519 0.2250 0.2149 5.6278 1.4755 +#> 63: 93.6188 -5.3860 -1.9295 -4.2637 -0.9222 0.1196 7.8033 1.6548 1.0340 2.2699 0.2282 0.2282 5.6763 1.4755 +#> 64: 93.6782 -5.4118 -1.9518 -4.2655 -0.9298 0.1055 8.3519 1.5721 1.0227 2.4426 0.2317 0.2560 5.8006 1.4724 +#> 65: 93.5253 -5.4313 -1.9314 -4.2538 -0.9245 0.0919 7.9343 1.4980 1.0771 2.3486 0.2249 0.2635 5.8752 1.4850 +#> 66: 93.3192 -5.5672 -1.9715 -4.2575 -0.9224 0.1404 8.2293 1.9722 1.0233 2.3758 0.2365 0.2546 5.9462 1.5148 +#> 67: 93.0765 -5.4861 -1.9673 -4.2472 -0.9103 0.0935 8.3227 1.8736 0.9889 2.3305 0.2493 0.2419 5.7836 1.4946 +#> 68: 93.2666 -5.4963 -1.9635 -4.2435 -0.9093 0.0940 9.2911 1.7800 1.0050 2.3179 0.2495 0.2298 5.7104 1.4797 +#> 69: 93.3894 -5.5666 -1.9342 -4.2325 -0.9227 0.0957 9.0211 2.0287 1.0012 2.3052 0.2483 0.2348 5.8939 1.5158 +#> 70: 93.2671 -5.5710 -1.9486 -4.2723 -0.9323 0.1062 8.5700 2.1251 0.9714 2.3266 0.2498 0.2466 6.1562 1.5041 +#> 71: 92.9975 -5.5829 -1.9507 -4.2632 -0.9317 0.1166 8.1415 2.0322 0.9403 2.3654 0.2373 0.2454 5.8668 1.5122 +#> 72: 92.6364 -5.5255 -1.9888 -4.2605 -0.9255 0.1062 8.8866 1.9306 0.9680 2.4488 0.2314 0.2438 6.2101 1.5098 +#> 73: 92.4442 -5.5679 -1.9880 -4.3501 -0.9070 0.0972 9.1986 1.9203 0.9597 3.1091 0.2369 0.2412 6.1257 1.5029 +#> 74: 92.3866 -5.5447 -1.9895 -4.3137 -0.9004 0.0898 10.2222 1.8961 0.9573 2.9536 0.2494 0.2361 6.0474 1.4875 +#> 75: 92.2491 -5.6481 -1.9591 -4.3587 -0.8991 0.1028 9.7111 2.2694 1.0140 2.9121 0.2524 0.2243 6.0995 1.4780 +#> 76: 92.4656 -5.6014 -1.9860 -4.3538 -0.9015 0.0978 11.3121 2.1560 0.9861 2.9372 0.2489 0.2314 6.0996 1.4464 +#> 77: 92.5076 -5.5929 -1.9560 -4.3624 -0.9051 0.1008 12.0483 2.0482 1.0212 3.0132 0.2551 0.2378 5.9595 1.5081 +#> 78: 92.5987 -5.7000 -1.9592 -4.3611 -0.9131 0.0958 11.4458 2.3873 1.0062 2.9848 0.2549 0.2372 6.0385 1.4666 +#> 79: 92.4883 -5.7675 -1.9900 -4.4226 -0.9163 0.1153 10.8735 2.7867 0.9616 3.4984 0.2546 0.2309 5.9441 1.4722 +#> 80: 92.1716 -5.7782 -1.9810 -4.4398 -0.9122 0.1193 10.3299 3.0280 0.9642 3.6766 0.2520 0.2291 6.3013 1.4698 +#> 81: 92.1145 -5.8494 -1.9836 -4.3634 -0.9196 0.1013 9.8134 3.1850 0.9160 3.4927 0.2562 0.2409 6.2458 1.4664 +#> 82: 92.3761 -5.9668 -1.9722 -4.3888 -0.9240 0.1139 9.9738 3.9484 0.8923 3.3519 0.2434 0.2318 6.0987 1.4847 +#> 83: 92.7805 -6.1135 -1.9335 -4.3600 -0.9273 0.1027 11.2060 4.7684 0.8932 3.1843 0.2454 0.2202 5.9824 1.4920 +#> 84: 92.9601 -6.2190 -1.9374 -4.3187 -0.9376 0.1140 10.6457 5.6632 0.9077 3.0250 0.2464 0.2188 5.9979 1.5152 +#> 85: 92.4579 -6.1486 -1.9398 -4.3269 -0.9417 0.0979 10.1134 5.3800 0.9011 2.8738 0.2446 0.2330 5.7007 1.5648 +#> 86: 92.3580 -6.2177 -1.9549 -4.3287 -0.9510 0.1073 9.6077 5.1608 0.9318 2.7301 0.2497 0.2214 5.9916 1.5305 +#> 87: 92.8919 -6.3309 -1.9480 -4.3285 -0.9647 0.1009 9.1273 6.4577 0.9494 2.7023 0.2408 0.2126 5.9053 1.4313 +#> 88: 93.0621 -6.1220 -1.9623 -4.3341 -0.9624 0.1300 8.6710 6.1349 0.9563 2.6593 0.2404 0.2130 6.1925 1.4510 +#> 89: 92.7711 -6.2636 -1.9545 -4.3520 -0.9496 0.1227 8.2374 6.2143 0.9791 2.5862 0.2346 0.2333 5.9772 1.4523 +#> 90: 92.9148 -6.5481 -1.9586 -4.3275 -0.9496 0.1096 7.8255 8.2617 0.9787 2.4647 0.2346 0.2216 5.9136 1.4247 +#> 91: 92.8129 -6.4655 -1.9753 -4.3287 -0.9435 0.1210 9.1893 7.8487 0.9642 2.5304 0.2354 0.2268 5.9129 1.4229 +#> 92: 93.1090 -6.4752 -1.9841 -4.3533 -0.9428 0.1509 10.1133 7.7232 0.9160 2.6037 0.2457 0.2265 5.8601 1.4646 +#> 93: 93.4781 -6.3780 -1.9909 -4.3713 -0.9450 0.1544 9.6076 7.3370 0.9153 2.7656 0.2485 0.2499 5.9150 1.5180 +#> 94: 93.2125 -6.3021 -1.9798 -4.3459 -0.9470 0.1520 9.6738 6.9702 0.9314 2.6273 0.2428 0.2519 5.8752 1.4456 +#> 95: 93.0091 -5.9727 -1.9828 -4.3777 -0.9447 0.1370 9.6411 6.6217 0.9107 2.7137 0.2428 0.2556 5.8302 1.4477 +#> 96: 92.8731 -5.7813 -1.9952 -4.3343 -0.9352 0.1505 9.1590 6.2906 0.9011 2.5780 0.2366 0.2546 6.0545 1.4887 +#> 97: 92.7834 -5.8119 -1.9975 -4.3303 -0.9258 0.1231 8.8022 5.9760 0.9005 2.5331 0.2392 0.2419 5.9522 1.4754 +#> 98: 92.8447 -5.9773 -1.9940 -4.3353 -0.9301 0.1409 8.3621 5.6772 0.9244 2.4828 0.2426 0.2490 6.1027 1.4129 +#> 99: 93.1697 -5.8958 -1.9964 -4.3325 -0.9248 0.1411 7.9440 5.3934 0.9586 2.6138 0.2378 0.2545 6.2793 1.3719 +#> 100: 93.2536 -5.8481 -2.0009 -4.3408 -0.9304 0.1718 8.7965 5.1237 0.9290 2.6161 0.2398 0.2418 6.0908 1.4534 +#> 101: 93.2942 -5.8684 -1.9650 -4.3096 -0.9305 0.1496 9.7633 4.8675 0.9166 2.4853 0.2372 0.2565 5.9079 1.4948 +#> 102: 93.2636 -6.1363 -1.9517 -4.2653 -0.9235 0.1175 10.7772 5.1927 0.8944 2.3610 0.2448 0.2812 5.7748 1.5533 +#> 103: 92.6954 -5.9371 -1.9524 -4.2792 -0.9045 0.1288 10.2383 4.9331 0.8876 2.2429 0.2406 0.2720 5.5496 1.5601 +#> 104: 92.6149 -6.0650 -1.9532 -4.2752 -0.9048 0.0973 10.9914 4.6864 0.8845 2.1875 0.2475 0.2584 5.5593 1.4897 +#> 105: 92.8231 -5.9779 -1.9650 -4.2939 -0.9013 0.1112 10.4712 4.4521 0.9193 2.1985 0.2416 0.2455 5.4420 1.4910 +#> 106: 92.7599 -5.9602 -1.9594 -4.3018 -0.9026 0.1273 10.1396 4.2295 0.9308 2.1700 0.2453 0.2625 5.5458 1.4429 +#> 107: 93.1433 -5.9509 -1.9638 -4.2715 -0.9324 0.1385 9.6327 4.0415 0.9271 2.1026 0.2415 0.2626 5.4762 1.4286 +#> 108: 93.1354 -5.7359 -1.9691 -4.2962 -0.9256 0.1346 10.2794 3.8394 0.9387 2.1671 0.2412 0.2627 5.5107 1.4200 +#> 109: 92.9608 -5.8252 -1.9780 -4.3149 -0.9125 0.1564 9.7654 4.0619 0.9380 2.1731 0.2325 0.2657 5.8118 1.4379 +#> 110: 93.1043 -5.7632 -1.9874 -4.2868 -0.9113 0.1178 9.2771 3.8588 0.9420 2.1477 0.2214 0.2524 5.9352 1.4377 +#> 111: 92.8879 -5.7965 -1.9781 -4.2851 -0.9147 0.1107 8.8133 3.6659 0.9526 2.1891 0.2130 0.2398 5.6360 1.4461 +#> 112: 92.9347 -5.7484 -1.9460 -4.2825 -0.9195 0.1078 8.3726 3.4826 0.9710 2.2687 0.2051 0.2278 5.5771 1.5123 +#> 113: 92.7217 -5.7193 -1.9328 -4.2721 -0.9252 0.1021 7.9540 3.3085 1.0056 2.2848 0.2244 0.2164 5.7135 1.5082 +#> 114: 92.9944 -5.7382 -1.9414 -4.2835 -0.9210 0.1210 7.5563 3.1430 1.0184 2.2457 0.2260 0.2182 5.6799 1.4751 +#> 115: 93.1261 -5.8876 -1.9290 -4.2753 -0.9382 0.0960 9.7696 3.4406 1.0140 2.2745 0.2171 0.2073 5.3919 1.4919 +#> 116: 92.7669 -5.9842 -1.9484 -4.2828 -0.9504 0.1122 9.2811 4.1332 1.0202 2.2835 0.2160 0.2136 5.3651 1.5337 +#> 117: 92.9804 -5.9847 -1.9584 -4.2879 -0.9474 0.1234 9.2911 3.9265 0.9692 2.3115 0.2135 0.2163 5.1053 1.4774 +#> 118: 93.2853 -5.8443 -1.9494 -4.2700 -0.9400 0.1105 9.8572 3.7302 0.9736 2.2489 0.2192 0.2223 5.2416 1.4668 +#> 119: 93.2776 -5.8592 -1.9458 -4.2600 -0.9394 0.1072 9.3643 3.5437 0.9789 2.1964 0.2176 0.2205 5.2942 1.4847 +#> 120: 93.0335 -5.8156 -1.9453 -4.2623 -0.9437 0.1139 8.8961 3.3665 0.9698 2.2380 0.2206 0.2231 5.4427 1.4470 +#> 121: 93.0115 -5.8402 -1.9355 -4.2596 -0.9291 0.1138 8.4513 3.3018 0.9743 2.1463 0.2096 0.2120 5.1537 1.4487 +#> 122: 93.6277 -5.8852 -1.9276 -4.2787 -0.9419 0.1388 8.0287 3.4114 0.9438 2.1410 0.2072 0.2104 5.1198 1.5201 +#> 123: 93.4952 -6.0977 -1.9332 -4.2847 -0.9431 0.1412 7.6273 4.8225 0.9472 2.1335 0.2081 0.2129 5.2003 1.6193 +#> 124: 93.7207 -6.2280 -1.9105 -4.2692 -0.9551 0.1422 7.2459 5.4835 0.9657 2.0896 0.2148 0.2272 5.2901 1.5482 +#> 125: 93.6041 -6.0808 -1.9356 -4.2748 -0.9531 0.1184 7.0201 5.2094 0.9591 2.0421 0.2089 0.2158 5.3848 1.4896 +#> 126: 93.5193 -6.0164 -1.9296 -4.2890 -0.9600 0.1351 7.6848 4.9489 0.9931 2.1387 0.1989 0.2129 5.1988 1.4492 +#> 127: 93.7135 -5.9340 -1.9448 -4.2883 -0.9633 0.1428 8.3411 4.7014 0.9820 2.1192 0.1985 0.2046 5.3953 1.4985 +#> 128: 94.2312 -5.8849 -1.9404 -4.2754 -0.9633 0.1495 7.9240 4.4664 0.9884 2.0587 0.1902 0.2171 5.7113 1.4987 +#> 129: 94.0390 -5.8674 -1.9229 -4.3309 -0.9614 0.1472 8.5108 4.2430 1.0319 2.1023 0.1909 0.2154 5.5654 1.4294 +#> 130: 93.4178 -6.0458 -1.9224 -4.3364 -0.9560 0.1570 8.0852 4.4639 1.0184 2.2804 0.1869 0.2182 5.6585 1.4443 +#> 131: 93.5483 -6.2682 -1.9258 -4.3654 -0.9554 0.1449 7.6810 5.6020 1.0254 2.3477 0.1857 0.2230 5.4266 1.4324 +#> 132: 93.5180 -6.3297 -1.9204 -4.3577 -0.9640 0.1365 7.2969 5.5672 1.0354 2.3257 0.1788 0.2118 5.4913 1.4859 +#> 133: 93.4707 -6.0990 -1.9415 -4.3315 -0.9775 0.1232 6.9321 5.2888 1.0686 2.3421 0.1851 0.2012 5.8429 1.4618 +#> 134: 93.1012 -6.1236 -1.9308 -4.3409 -0.9654 0.1225 7.6471 5.0244 1.0517 2.4652 0.1947 0.2008 5.6902 1.5432 +#> 135: 93.2545 -6.1070 -1.9408 -4.3415 -0.9553 0.1228 9.2701 4.7732 1.0160 2.3607 0.1919 0.1907 5.5154 1.5317 +#> 136: 93.3338 -6.0321 -1.9336 -4.3074 -0.9598 0.1120 8.8066 4.5345 0.9652 2.2427 0.1999 0.2249 5.3667 1.6036 +#> 137: 93.5910 -6.0627 -1.9339 -4.3074 -0.9529 0.1407 8.3663 4.3078 0.9538 2.2128 0.1966 0.2195 5.2959 1.6015 +#> 138: 93.6338 -5.9702 -1.9252 -4.3105 -0.9615 0.1373 7.9480 4.0924 0.9875 2.2635 0.1964 0.2218 5.4532 1.5261 +#> 139: 93.6403 -5.8913 -1.9237 -4.2962 -0.9582 0.1165 8.0749 3.8878 0.9746 2.2457 0.1972 0.2125 5.9356 1.5173 +#> 140: 92.8503 -5.8314 -1.9452 -4.3180 -0.9487 0.1142 8.6356 3.6934 0.9933 2.2044 0.1961 0.2019 5.7908 1.5138 +#> 141: 93.1249 -6.0584 -1.9448 -4.3139 -0.9367 0.0950 8.9231 4.4196 1.0220 2.2246 0.2079 0.2077 6.0233 1.4339 +#> 142: 93.1846 -6.3026 -1.9152 -4.3093 -0.9392 0.0866 10.1508 5.9592 1.0562 2.3325 0.2082 0.2133 5.5285 1.4832 +#> 143: 92.4682 -6.1485 -1.9146 -4.2812 -0.9376 0.0260 9.6433 5.6613 1.0618 2.3594 0.2000 0.2027 6.0573 1.4428 +#> 144: 92.7792 -6.1108 -1.8939 -4.2740 -0.9341 0.0765 9.1611 5.3782 1.0917 2.3074 0.2057 0.2240 6.2141 1.4953 +#> 145: 93.1314 -6.2086 -1.8939 -4.3580 -0.9341 0.0741 8.7031 5.1093 1.0931 2.7164 0.2105 0.2229 5.8543 1.4855 +#> 146: 93.2254 -6.2170 -1.8998 -4.3724 -0.9311 0.0677 8.2679 5.0506 1.0811 2.8434 0.2049 0.2118 5.5455 1.4763 +#> 147: 93.3264 -6.0136 -1.8998 -4.3853 -0.9328 0.0817 9.4673 4.7980 1.0668 2.8512 0.2009 0.2114 5.5518 1.5225 +#> 148: 93.2298 -5.9143 -1.8921 -4.5001 -0.9296 0.1057 8.9939 4.5581 1.0563 3.8266 0.1982 0.2043 5.5242 1.5614 +#> 149: 93.3604 -5.9894 -1.8832 -4.5223 -0.9338 0.0858 8.5442 4.3302 1.0544 4.3930 0.1986 0.2003 5.4353 1.4957 +#> 150: 93.4715 -5.9630 -1.8833 -4.4796 -0.9335 0.0827 8.1170 4.1137 1.0912 4.1733 0.1984 0.1903 5.7477 1.4554 +#> 151: 93.3385 -5.8026 -1.9052 -4.4507 -0.9368 0.0684 8.7726 3.9080 1.1249 3.9647 0.2074 0.1808 5.7693 1.4400 +#> 152: 93.1682 -5.8529 -1.9441 -4.3545 -0.9309 0.0752 8.8042 3.1783 1.0496 3.0168 0.2069 0.1688 5.9161 1.4565 +#> 153: 93.0559 -6.0261 -1.9425 -4.3431 -0.9327 0.1016 9.1435 3.9939 1.0120 2.8470 0.1894 0.1509 5.4435 1.5486 +#> 154: 92.8582 -6.0887 -1.9278 -4.3094 -0.9352 0.1064 8.4316 4.2991 0.9819 2.6257 0.1907 0.1609 5.4587 1.5208 +#> 155: 93.3200 -5.8480 -1.9149 -4.3363 -0.9294 0.1143 9.6700 3.1734 0.9942 2.6441 0.1824 0.1906 5.5193 1.6410 +#> 156: 93.3199 -5.9053 -1.9213 -4.3163 -0.9369 0.1291 7.5899 3.5902 0.9823 2.4648 0.1770 0.1956 5.3816 1.5356 +#> 157: 93.2434 -5.8763 -1.9161 -4.3035 -0.9549 0.1075 8.4137 3.2576 0.9935 2.5007 0.1795 0.1852 5.4053 1.5706 +#> 158: 93.1494 -5.9243 -1.8929 -4.3162 -0.9680 0.1296 8.2959 3.3262 1.0029 2.4943 0.1866 0.1921 5.4369 1.5510 +#> 159: 93.5683 -6.0335 -1.9127 -4.3040 -0.9675 0.1271 7.7222 4.0079 0.9768 2.5765 0.1869 0.2028 5.7165 1.4968 +#> 160: 93.9417 -6.0018 -1.9085 -4.2818 -0.9611 0.1161 5.8791 4.4991 0.9658 2.4933 0.1878 0.1986 6.0579 1.5272 +#> 161: 94.1252 -5.9264 -1.8943 -4.2805 -0.9645 0.0860 4.9517 3.6307 0.9754 2.4988 0.1934 0.1785 5.7457 1.5878 +#> 162: 93.9389 -5.7613 -1.8946 -4.2410 -0.9752 0.0898 6.7269 2.5865 1.0184 2.4379 0.1933 0.1908 5.9052 1.5215 +#> 163: 93.5890 -5.7243 -1.8992 -4.2636 -0.9722 0.0759 8.4484 2.5137 1.0151 2.3869 0.1928 0.1889 5.4694 1.5048 +#> 164: 93.9751 -5.7314 -1.8786 -4.3271 -0.9702 0.1020 6.6884 2.5136 1.0133 2.8395 0.1907 0.1998 5.4625 1.4854 +#> 165: 93.9708 -5.7409 -1.8856 -4.3129 -0.9616 0.1094 5.8809 2.4589 1.0401 2.6662 0.1912 0.1998 5.4339 1.4549 +#> 166: 93.9265 -5.6937 -1.9134 -4.3080 -0.9702 0.1151 5.6940 2.4086 1.0065 2.6864 0.1983 0.1987 5.6907 1.4857 +#> 167: 93.4157 -5.7312 -1.9163 -4.3286 -0.9638 0.1216 5.1230 2.5468 1.0487 2.5930 0.1917 0.1940 5.5938 1.4267 +#> 168: 93.3701 -5.8757 -1.9196 -4.3493 -0.9579 0.1134 6.0802 3.3929 1.0517 2.6981 0.1888 0.2063 5.4125 1.4365 +#> 169: 93.4342 -6.0262 -1.9041 -4.3347 -0.9526 0.0997 6.0780 3.6349 1.0623 2.7344 0.1946 0.1978 5.4930 1.4594 +#> 170: 93.3751 -6.1195 -1.9093 -4.3541 -0.9872 0.0834 6.8972 4.0337 1.0763 2.8428 0.2077 0.2005 5.6759 1.4455 +#> 171: 93.3603 -6.0360 -1.9196 -4.4632 -0.9763 0.0866 7.4236 3.6025 1.0684 3.7611 0.2046 0.1894 5.6282 1.4414 +#> 172: 93.2776 -5.9538 -1.9031 -4.4815 -0.9779 0.1024 5.4751 3.2802 1.0599 3.9487 0.2115 0.1990 5.6116 1.4230 +#> 173: 93.4470 -5.8580 -1.9193 -4.4170 -0.9641 0.0957 5.6416 2.8005 1.0440 3.4509 0.2066 0.1863 5.5804 1.4485 +#> 174: 93.2952 -5.8590 -1.9010 -4.3600 -0.9640 0.0789 6.4314 2.9503 1.0808 2.9773 0.2045 0.1969 5.4423 1.4421 +#> 175: 93.3756 -5.7733 -1.8959 -4.3621 -0.9504 0.0609 6.1723 2.5287 1.0950 3.0019 0.2127 0.2053 5.4338 1.4470 +#> 176: 93.1450 -5.8266 -1.9053 -4.3401 -0.9457 0.0633 6.5237 3.0522 1.0942 2.9464 0.2134 0.2021 5.6501 1.3664 +#> 177: 92.7723 -5.9978 -1.9231 -4.3529 -0.9524 0.0658 7.4519 4.2374 1.0640 3.0260 0.2158 0.2146 5.9180 1.4100 +#> 178: 92.7261 -5.9836 -1.9189 -4.3349 -0.9576 0.0768 5.5211 4.2557 1.0611 2.8827 0.2169 0.2088 5.8872 1.4206 +#> 179: 92.9599 -6.0071 -1.9259 -4.3081 -0.9581 0.0657 6.0953 3.8205 1.0816 2.6709 0.2122 0.2014 5.8221 1.4026 +#> 180: 93.0831 -6.1544 -1.9400 -4.3018 -0.9496 0.0411 4.2312 4.9005 1.1064 2.6542 0.2143 0.2221 6.3264 1.3820 +#> 181: 92.8840 -6.0889 -1.9364 -4.3200 -0.9566 0.0861 4.2186 4.4615 1.0930 2.7270 0.2142 0.2424 6.0486 1.4035 +#> 182: 93.1913 -6.1457 -1.9384 -4.3085 -0.9606 0.0733 6.2878 4.6026 1.0917 2.6393 0.2131 0.2151 5.7042 1.4952 +#> 183: 93.1218 -6.3114 -1.9355 -4.2883 -0.9742 0.0741 7.2675 5.1377 1.0914 2.5060 0.2220 0.2111 5.5099 1.4097 +#> 184: 93.1462 -6.3147 -1.9068 -4.2880 -0.9653 0.0893 7.6928 5.6510 1.0563 2.5066 0.2256 0.2201 5.4138 1.5319 +#> 185: 93.1825 -6.3608 -1.9265 -4.2815 -0.9549 0.0873 7.1340 5.9801 1.0363 2.4788 0.2177 0.1958 5.4202 1.4569 +#> 186: 93.6270 -6.1413 -1.9278 -4.2702 -0.9696 0.1185 6.7652 4.5535 1.0400 2.3673 0.2163 0.1932 5.3005 1.5012 +#> 187: 93.9922 -6.3364 -1.9269 -4.2702 -0.9729 0.1197 7.7694 6.1592 0.9948 2.3673 0.2196 0.2091 5.3075 1.5105 +#> 188: 93.8884 -6.0236 -1.9207 -4.2928 -0.9900 0.1343 7.8090 4.2847 0.9840 2.4238 0.2195 0.1966 5.2861 1.5607 +#> 189: 94.3110 -6.0809 -1.9145 -4.2826 -0.9840 0.1224 8.5580 4.0998 0.9800 2.4505 0.2294 0.1840 5.7107 1.5180 +#> 190: 94.0039 -6.0996 -1.9140 -4.2793 -0.9782 0.1429 10.6594 4.1655 0.9796 2.4415 0.2297 0.1960 5.7533 1.5720 +#> 191: 93.9692 -6.1129 -1.9362 -4.3261 -0.9705 0.1462 8.8201 4.3146 1.0124 2.4625 0.2287 0.2049 5.5670 1.5206 +#> 192: 93.3178 -5.9759 -1.9192 -4.3378 -0.9664 0.1434 8.8047 3.7150 1.0282 2.4137 0.2243 0.1977 5.3858 1.4599 +#> 193: 93.1427 -5.9388 -1.9391 -4.3211 -0.9650 0.1401 7.1862 3.2835 1.0218 2.3216 0.2163 0.1866 5.3930 1.5017 +#> 194: 93.0588 -6.0605 -1.9361 -4.3350 -0.9462 0.1330 6.8930 4.0020 1.0166 2.3186 0.2057 0.1818 5.2535 1.5075 +#> 195: 93.1820 -6.1201 -1.9579 -4.3034 -0.9534 0.1557 8.1300 4.4218 0.9932 2.1873 0.2099 0.1834 5.4862 1.4698 +#> 196: 93.2230 -5.8879 -1.9725 -4.2965 -0.9584 0.1390 8.1307 3.0777 1.0051 2.1597 0.2089 0.1683 5.7058 1.3970 +#> 197: 93.3504 -5.8829 -1.9677 -4.3075 -0.9577 0.1638 6.7115 3.0660 1.0050 2.1377 0.2024 0.1642 5.4691 1.5016 +#> 198: 93.3016 -5.8771 -1.9885 -4.3241 -0.9605 0.1562 6.4722 3.0381 0.9727 2.2053 0.1975 0.1683 5.3434 1.4885 +#> 199: 93.2464 -5.8787 -1.9871 -4.3430 -0.9528 0.1751 4.5894 3.0445 0.9748 2.2247 0.1886 0.1780 5.4469 1.4405 +#> 200: 93.3474 -5.7995 -1.9767 -4.3298 -0.9480 0.1947 4.7024 2.8535 0.9895 2.2234 0.1951 0.2012 5.5130 1.4641 +#> 201: 93.3231 -5.8169 -1.9737 -4.3268 -0.9510 0.1804 4.4248 2.8913 0.9738 2.2141 0.1955 0.2057 5.5422 1.4843 +#> 202: 93.3484 -5.8009 -1.9732 -4.3240 -0.9519 0.1674 4.4068 2.8084 0.9736 2.2040 0.1959 0.2033 5.5843 1.4744 +#> 203: 93.2617 -5.7915 -1.9678 -4.3211 -0.9535 0.1629 4.5333 2.7678 0.9877 2.1980 0.1961 0.2023 5.6265 1.4811 +#> 204: 93.2210 -5.8071 -1.9647 -4.3220 -0.9504 0.1629 4.6144 2.8347 0.9922 2.1938 0.1937 0.2013 5.5745 1.4988 +#> 205: 93.1914 -5.8104 -1.9667 -4.3225 -0.9484 0.1593 4.5880 2.8639 0.9931 2.1952 0.1916 0.1979 5.5960 1.5057 +#> 206: 93.1827 -5.8348 -1.9697 -4.3236 -0.9498 0.1587 4.7189 3.0353 0.9929 2.2016 0.1922 0.1947 5.6096 1.5136 +#> 207: 93.2017 -5.8760 -1.9714 -4.3239 -0.9518 0.1592 4.8171 3.2659 0.9947 2.2042 0.1927 0.1910 5.6413 1.5078 +#> 208: 93.2226 -5.8819 -1.9736 -4.3261 -0.9532 0.1610 4.8241 3.2964 0.9957 2.2122 0.1938 0.1878 5.6704 1.5031 +#> 209: 93.2158 -5.8786 -1.9743 -4.3278 -0.9538 0.1595 4.6275 3.2763 0.9963 2.2279 0.1950 0.1848 5.6758 1.5038 +#> 210: 93.2216 -5.8798 -1.9746 -4.3286 -0.9535 0.1589 4.5667 3.2857 0.9974 2.2473 0.1948 0.1834 5.6707 1.5054 +#> 211: 93.2238 -5.8847 -1.9763 -4.3302 -0.9530 0.1591 4.5745 3.2932 0.9956 2.2576 0.1948 0.1823 5.6691 1.4990 +#> 212: 93.2242 -5.8893 -1.9777 -4.3323 -0.9532 0.1600 4.6203 3.2955 0.9938 2.2704 0.1958 0.1814 5.6732 1.4994 +#> 213: 93.2246 -5.8950 -1.9756 -4.3345 -0.9532 0.1588 4.7363 3.3106 0.9894 2.2864 0.1960 0.1791 5.6401 1.5015 +#> 214: 93.2056 -5.9070 -1.9740 -4.3368 -0.9532 0.1586 4.7814 3.3538 0.9888 2.3047 0.1960 0.1761 5.6265 1.5008 +#> 215: 93.2126 -5.9157 -1.9720 -4.3405 -0.9533 0.1580 4.9117 3.3916 0.9890 2.3191 0.1959 0.1742 5.6054 1.5015 +#> 216: 93.2161 -5.9242 -1.9716 -4.3423 -0.9533 0.1594 5.0163 3.4425 0.9897 2.3291 0.1959 0.1739 5.5975 1.5005 +#> 217: 93.2193 -5.9351 -1.9715 -4.3445 -0.9537 0.1614 4.9927 3.5085 0.9905 2.3309 0.1957 0.1739 5.5905 1.5024 +#> 218: 93.1973 -5.9314 -1.9725 -4.3479 -0.9548 0.1640 5.0502 3.4902 0.9918 2.3344 0.1952 0.1740 5.5909 1.5046 +#> 219: 93.1938 -5.9312 -1.9729 -4.3508 -0.9539 0.1664 5.0446 3.4901 0.9922 2.3365 0.1949 0.1746 5.5808 1.5046 +#> 220: 93.1994 -5.9424 -1.9734 -4.3531 -0.9536 0.1683 5.0462 3.5593 0.9917 2.3370 0.1945 0.1754 5.5831 1.5055 +#> 221: 93.2015 -5.9511 -1.9746 -4.3550 -0.9537 0.1702 5.1062 3.6002 0.9899 2.3368 0.1945 0.1762 5.5731 1.5043 +#> 222: 93.2057 -5.9653 -1.9756 -4.3571 -0.9541 0.1718 5.1727 3.6876 0.9886 2.3364 0.1943 0.1776 5.5813 1.5047 +#> 223: 93.1998 -5.9723 -1.9761 -4.3592 -0.9540 0.1726 5.1866 3.7239 0.9871 2.3428 0.1940 0.1791 5.5702 1.5047 +#> 224: 93.2042 -5.9799 -1.9768 -4.3615 -0.9540 0.1734 5.1516 3.7613 0.9849 2.3531 0.1934 0.1809 5.5705 1.5039 +#> 225: 93.1974 -5.9813 -1.9776 -4.3648 -0.9540 0.1740 5.1225 3.7676 0.9840 2.3663 0.1929 0.1834 5.5698 1.5030 +#> 226: 93.1963 -5.9807 -1.9777 -4.3679 -0.9535 0.1751 5.1632 3.7694 0.9839 2.3785 0.1927 0.1850 5.5676 1.5069 +#> 227: 93.1912 -5.9740 -1.9783 -4.3707 -0.9533 0.1768 5.1987 3.7421 0.9835 2.3931 0.1922 0.1855 5.5597 1.5091 +#> 228: 93.1902 -5.9799 -1.9792 -4.3745 -0.9533 0.1784 5.2070 3.7641 0.9825 2.4134 0.1917 0.1861 5.5502 1.5086 +#> 229: 93.1903 -5.9894 -1.9805 -4.3792 -0.9533 0.1796 5.2398 3.8109 0.9812 2.4382 0.1910 0.1870 5.5486 1.5075 +#> 230: 93.1833 -5.9946 -1.9816 -4.3836 -0.9530 0.1814 5.2357 3.8346 0.9800 2.4614 0.1904 0.1883 5.5515 1.5065 +#> 231: 93.1740 -6.0001 -1.9834 -4.3871 -0.9528 0.1833 5.2848 3.8635 0.9783 2.4814 0.1898 0.1893 5.5526 1.5057 +#> 232: 93.1581 -6.0071 -1.9852 -4.3904 -0.9523 0.1857 5.3056 3.8967 0.9766 2.5002 0.1891 0.1904 5.5571 1.5057 +#> 233: 93.1417 -6.0131 -1.9865 -4.3933 -0.9517 0.1869 5.3290 3.9227 0.9745 2.5129 0.1885 0.1909 5.5609 1.5069 +#> 234: 93.1245 -6.0198 -1.9878 -4.3961 -0.9514 0.1880 5.3062 3.9567 0.9731 2.5269 0.1886 0.1916 5.5645 1.5074 +#> 235: 93.1084 -6.0269 -1.9885 -4.3985 -0.9514 0.1892 5.3213 3.9969 0.9729 2.5390 0.1887 0.1931 5.5722 1.5065 +#> 236: 93.1037 -6.0382 -1.9897 -4.4009 -0.9517 0.1899 5.3601 4.0674 0.9744 2.5501 0.1886 0.1949 5.5811 1.5066 +#> 237: 93.0989 -6.0432 -1.9906 -4.4031 -0.9518 0.1909 5.3744 4.0877 0.9755 2.5623 0.1885 0.1964 5.5890 1.5051 +#> 238: 93.0932 -6.0433 -1.9912 -4.4041 -0.9521 0.1915 5.4192 4.0775 0.9772 2.5698 0.1886 0.1980 5.5980 1.5029 +#> 239: 93.0943 -6.0475 -1.9913 -4.4056 -0.9520 0.1921 5.4483 4.0960 0.9792 2.5785 0.1888 0.1997 5.5999 1.5011 +#> 240: 93.0904 -6.0498 -1.9909 -4.4070 -0.9520 0.1925 5.4921 4.1095 0.9814 2.5867 0.1887 0.2011 5.5974 1.5013 +#> 241: 93.0883 -6.0508 -1.9910 -4.4086 -0.9520 0.1931 5.5503 4.1140 0.9827 2.5966 0.1887 0.2023 5.6049 1.4997 +#> 242: 93.0884 -6.0487 -1.9916 -4.4102 -0.9517 0.1940 5.5634 4.1021 0.9831 2.6059 0.1886 0.2039 5.6116 1.5005 +#> 243: 93.0836 -6.0466 -1.9920 -4.4123 -0.9517 0.1950 5.5786 4.0878 0.9837 2.6204 0.1887 0.2054 5.6217 1.5000 +#> 244: 93.0756 -6.0477 -1.9926 -4.4149 -0.9517 0.1956 5.5827 4.0904 0.9843 2.6385 0.1887 0.2070 5.6306 1.4995 +#> 245: 93.0664 -6.0533 -1.9930 -4.4174 -0.9514 0.1963 5.6228 4.1208 0.9857 2.6549 0.1888 0.2086 5.6346 1.4996 +#> 246: 93.0643 -6.0543 -1.9931 -4.4200 -0.9511 0.1969 5.6236 4.1257 0.9872 2.6735 0.1886 0.2096 5.6381 1.4989 +#> 247: 93.0631 -6.0568 -1.9929 -4.4227 -0.9511 0.1974 5.6045 4.1389 0.9889 2.6910 0.1886 0.2107 5.6408 1.4984 +#> 248: 93.0636 -6.0567 -1.9924 -4.4264 -0.9513 0.1974 5.6016 4.1412 0.9906 2.7225 0.1886 0.2117 5.6424 1.4992 +#> 249: 93.0727 -6.0560 -1.9920 -4.4302 -0.9514 0.1973 5.6088 4.1383 0.9922 2.7584 0.1885 0.2125 5.6441 1.4992 +#> 250: 93.0865 -6.0551 -1.9915 -4.4337 -0.9512 0.1973 5.6127 4.1386 0.9941 2.7852 0.1884 0.2135 5.6522 1.4977 +#> 251: 93.0887 -6.0551 -1.9910 -4.4364 -0.9511 0.1967 5.5869 4.1455 0.9964 2.8060 0.1883 0.2146 5.6561 1.4968 +#> 252: 93.0877 -6.0522 -1.9904 -4.4376 -0.9511 0.1964 5.5778 4.1346 0.9987 2.8151 0.1883 0.2155 5.6583 1.4964 +#> 253: 93.0843 -6.0518 -1.9897 -4.4391 -0.9512 0.1961 5.5948 4.1323 1.0011 2.8253 0.1884 0.2164 5.6588 1.4972 +#> 254: 93.0818 -6.0518 -1.9896 -4.4399 -0.9512 0.1957 5.6122 4.1352 1.0016 2.8319 0.1882 0.2169 5.6573 1.4991 +#> 255: 93.0838 -6.0524 -1.9895 -4.4401 -0.9514 0.1954 5.6310 4.1366 1.0025 2.8408 0.1880 0.2174 5.6584 1.4996 +#> 256: 93.0850 -6.0579 -1.9892 -4.4400 -0.9515 0.1948 5.6526 4.1752 1.0043 2.8482 0.1879 0.2181 5.6611 1.4979 +#> 257: 93.0868 -6.0600 -1.9890 -4.4391 -0.9517 0.1940 5.6742 4.1941 1.0055 2.8499 0.1878 0.2189 5.6649 1.4985 +#> 258: 93.0873 -6.0606 -1.9888 -4.4391 -0.9518 0.1932 5.7088 4.2037 1.0066 2.8552 0.1877 0.2196 5.6668 1.4983 +#> 259: 93.0912 -6.0650 -1.9882 -4.4377 -0.9519 0.1925 5.7494 4.2300 1.0080 2.8537 0.1877 0.2204 5.6729 1.4977 +#> 260: 93.0964 -6.0699 -1.9874 -4.4362 -0.9519 0.1918 5.7609 4.2588 1.0100 2.8513 0.1877 0.2212 5.6792 1.4974 +#> 261: 93.1014 -6.0737 -1.9866 -4.4350 -0.9522 0.1913 5.7971 4.2807 1.0115 2.8496 0.1877 0.2220 5.6812 1.4969 +#> 262: 93.1064 -6.0734 -1.9859 -4.4346 -0.9526 0.1909 5.7936 4.2719 1.0129 2.8505 0.1877 0.2228 5.6824 1.4958 +#> 263: 93.1092 -6.0783 -1.9850 -4.4344 -0.9530 0.1906 5.8078 4.2973 1.0141 2.8525 0.1879 0.2233 5.6815 1.4954 +#> 264: 93.1128 -6.0830 -1.9842 -4.4338 -0.9535 0.1901 5.8245 4.3273 1.0146 2.8527 0.1880 0.2237 5.6768 1.4958 +#> 265: 93.1198 -6.0874 -1.9834 -4.4331 -0.9541 0.1895 5.8467 4.3490 1.0149 2.8522 0.1880 0.2238 5.6693 1.4965 +#> 266: 93.1284 -6.0890 -1.9828 -4.4327 -0.9546 0.1888 5.8350 4.3488 1.0149 2.8513 0.1881 0.2239 5.6650 1.4970 +#> 267: 93.1380 -6.0926 -1.9819 -4.4326 -0.9549 0.1883 5.8440 4.3677 1.0156 2.8526 0.1883 0.2240 5.6609 1.4974 +#> 268: 93.1480 -6.0915 -1.9810 -4.4321 -0.9552 0.1873 5.8565 4.3552 1.0163 2.8522 0.1886 0.2238 5.6537 1.4990 +#> 269: 93.1539 -6.0910 -1.9803 -4.4314 -0.9556 0.1866 5.8709 4.3438 1.0179 2.8503 0.1888 0.2237 5.6495 1.4989 +#> 270: 93.1620 -6.0898 -1.9798 -4.4311 -0.9561 0.1861 5.8678 4.3301 1.0197 2.8507 0.1890 0.2235 5.6466 1.4984 +#> 271: 93.1668 -6.0881 -1.9792 -4.4305 -0.9565 0.1857 5.8508 4.3147 1.0209 2.8487 0.1891 0.2234 5.6487 1.4997 +#> 272: 93.1725 -6.0848 -1.9787 -4.4300 -0.9569 0.1855 5.8431 4.2948 1.0217 2.8474 0.1894 0.2233 5.6488 1.5000 +#> 273: 93.1770 -6.0809 -1.9783 -4.4297 -0.9572 0.1850 5.8432 4.2739 1.0227 2.8470 0.1897 0.2235 5.6497 1.5000 +#> 274: 93.1797 -6.0774 -1.9776 -4.4299 -0.9574 0.1846 5.8549 4.2532 1.0243 2.8494 0.1901 0.2235 5.6511 1.5003 +#> 275: 93.1829 -6.0759 -1.9774 -4.4303 -0.9578 0.1845 5.8633 4.2387 1.0255 2.8514 0.1906 0.2234 5.6561 1.5010 +#> 276: 93.1846 -6.0764 -1.9771 -4.4303 -0.9581 0.1845 5.8738 4.2322 1.0267 2.8523 0.1911 0.2232 5.6554 1.5020 +#> 277: 93.1880 -6.0792 -1.9768 -4.4305 -0.9584 0.1844 5.8980 4.2423 1.0278 2.8541 0.1915 0.2229 5.6586 1.5019 +#> 278: 93.1920 -6.0791 -1.9766 -4.4307 -0.9586 0.1841 5.9368 4.2391 1.0289 2.8559 0.1919 0.2226 5.6600 1.5024 +#> 279: 93.1892 -6.0786 -1.9766 -4.4310 -0.9586 0.1839 5.9822 4.2309 1.0300 2.8584 0.1925 0.2226 5.6642 1.5015 +#> 280: 93.1868 -6.0782 -1.9765 -4.4311 -0.9587 0.1836 6.0381 4.2253 1.0311 2.8616 0.1930 0.2227 5.6686 1.5008 +#> 281: 93.1805 -6.0781 -1.9764 -4.4309 -0.9586 0.1832 6.0718 4.2228 1.0325 2.8626 0.1936 0.2227 5.6741 1.5002 +#> 282: 93.1780 -6.0768 -1.9762 -4.4318 -0.9585 0.1829 6.0867 4.2160 1.0341 2.8701 0.1941 0.2228 5.6740 1.4998 +#> 283: 93.1777 -6.0736 -1.9760 -4.4325 -0.9583 0.1825 6.1250 4.2003 1.0355 2.8768 0.1946 0.2228 5.6761 1.5010 +#> 284: 93.1745 -6.0726 -1.9757 -4.4337 -0.9582 0.1823 6.1509 4.1975 1.0370 2.8843 0.1951 0.2227 5.6764 1.5009 +#> 285: 93.1742 -6.0719 -1.9755 -4.4348 -0.9579 0.1820 6.1652 4.1936 1.0381 2.8910 0.1954 0.2225 5.6773 1.5011 +#> 286: 93.1706 -6.0698 -1.9754 -4.4356 -0.9576 0.1818 6.1840 4.1844 1.0394 2.8966 0.1958 0.2224 5.6780 1.5011 +#> 287: 93.1672 -6.0678 -1.9752 -4.4370 -0.9573 0.1816 6.2123 4.1767 1.0400 2.9079 0.1963 0.2224 5.6757 1.5015 +#> 288: 93.1628 -6.0658 -1.9753 -4.4379 -0.9572 0.1815 6.2355 4.1700 1.0407 2.9150 0.1967 0.2223 5.6742 1.5013 +#> 289: 93.1588 -6.0628 -1.9753 -4.4389 -0.9569 0.1818 6.2435 4.1565 1.0416 2.9217 0.1969 0.2218 5.6777 1.5007 +#> 290: 93.1560 -6.0590 -1.9754 -4.4399 -0.9565 0.1820 6.2564 4.1394 1.0425 2.9291 0.1971 0.2214 5.6778 1.5006 +#> 291: 93.1552 -6.0555 -1.9754 -4.4409 -0.9562 0.1821 6.2753 4.1246 1.0435 2.9375 0.1973 0.2210 5.6779 1.5009 +#> 292: 93.1546 -6.0541 -1.9754 -4.4415 -0.9558 0.1820 6.2881 4.1183 1.0444 2.9414 0.1975 0.2205 5.6762 1.5006 +#> 293: 93.1506 -6.0535 -1.9756 -4.4424 -0.9555 0.1821 6.2856 4.1182 1.0454 2.9474 0.1976 0.2200 5.6770 1.4994 +#> 294: 93.1453 -6.0520 -1.9758 -4.4424 -0.9553 0.1819 6.2733 4.1124 1.0463 2.9487 0.1979 0.2195 5.6792 1.4985 +#> 295: 93.1431 -6.0487 -1.9760 -4.4421 -0.9551 0.1820 6.2655 4.1009 1.0469 2.9498 0.1982 0.2190 5.6797 1.4989 +#> 296: 93.1425 -6.0460 -1.9760 -4.4432 -0.9548 0.1818 6.2801 4.0912 1.0478 2.9566 0.1984 0.2185 5.6795 1.4989 +#> 297: 93.1403 -6.0442 -1.9761 -4.4440 -0.9545 0.1818 6.2979 4.0836 1.0485 2.9626 0.1987 0.2182 5.6783 1.4978 +#> 298: 93.1400 -6.0438 -1.9763 -4.4440 -0.9543 0.1817 6.3069 4.0842 1.0492 2.9646 0.1989 0.2178 5.6783 1.4968 +#> 299: 93.1373 -6.0426 -1.9764 -4.4445 -0.9540 0.1813 6.3134 4.0790 1.0505 2.9694 0.1991 0.2175 5.6800 1.4953 +#> 300: 93.1340 -6.0412 -1.9764 -4.4450 -0.9538 0.1811 6.3192 4.0731 1.0516 2.9744 0.1993 0.2171 5.6782 1.4938 +#> 301: 93.1330 -6.0402 -1.9766 -4.4455 -0.9535 0.1808 6.3278 4.0685 1.0531 2.9784 0.1996 0.2167 5.6819 1.4925 +#> 302: 93.1308 -6.0402 -1.9768 -4.4457 -0.9534 0.1806 6.3417 4.0684 1.0549 2.9813 0.1998 0.2163 5.6824 1.4905 +#> 303: 93.1294 -6.0373 -1.9769 -4.4459 -0.9532 0.1804 6.3489 4.0538 1.0565 2.9838 0.2000 0.2159 5.6841 1.4890 +#> 304: 93.1304 -6.0345 -1.9771 -4.4461 -0.9530 0.1801 6.3543 4.0409 1.0581 2.9859 0.2002 0.2155 5.6869 1.4875 +#> 305: 93.1287 -6.0319 -1.9772 -4.4463 -0.9528 0.1800 6.3496 4.0293 1.0597 2.9882 0.2003 0.2151 5.6902 1.4867 +#> 306: 93.1261 -6.0301 -1.9775 -4.4474 -0.9527 0.1802 6.3479 4.0231 1.0614 2.9989 0.2003 0.2145 5.6963 1.4856 +#> 307: 93.1232 -6.0284 -1.9777 -4.4479 -0.9526 0.1802 6.3507 4.0135 1.0629 3.0036 0.2004 0.2141 5.6987 1.4849 +#> 308: 93.1192 -6.0264 -1.9779 -4.4483 -0.9524 0.1802 6.3641 4.0019 1.0644 3.0084 0.2004 0.2135 5.6991 1.4837 +#> 309: 93.1137 -6.0253 -1.9783 -4.4487 -0.9522 0.1803 6.3579 3.9953 1.0658 3.0133 0.2004 0.2130 5.7035 1.4826 +#> 310: 93.1100 -6.0223 -1.9787 -4.4489 -0.9520 0.1804 6.3423 3.9800 1.0665 3.0171 0.2005 0.2126 5.7061 1.4822 +#> 311: 93.1044 -6.0215 -1.9791 -4.4496 -0.9517 0.1804 6.3365 3.9744 1.0675 3.0251 0.2005 0.2121 5.7092 1.4816 +#> 312: 93.1006 -6.0206 -1.9795 -4.4501 -0.9516 0.1806 6.3317 3.9681 1.0688 3.0321 0.2006 0.2115 5.7128 1.4805 +#> 313: 93.0951 -6.0194 -1.9797 -4.4499 -0.9516 0.1805 6.3297 3.9609 1.0702 3.0333 0.2008 0.2109 5.7137 1.4805 +#> 314: 93.0922 -6.0192 -1.9800 -4.4497 -0.9515 0.1804 6.3486 3.9570 1.0715 3.0345 0.2009 0.2104 5.7144 1.4800 +#> 315: 93.0883 -6.0186 -1.9804 -4.4495 -0.9515 0.1803 6.3712 3.9528 1.0726 3.0351 0.2011 0.2100 5.7156 1.4794 +#> 316: 93.0808 -6.0182 -1.9808 -4.4492 -0.9514 0.1802 6.3979 3.9483 1.0738 3.0345 0.2013 0.2097 5.7164 1.4792 +#> 317: 93.0758 -6.0174 -1.9813 -4.4487 -0.9513 0.1801 6.4377 3.9428 1.0747 3.0327 0.2015 0.2094 5.7175 1.4787 +#> 318: 93.0713 -6.0166 -1.9816 -4.4484 -0.9513 0.1801 6.4856 3.9375 1.0757 3.0316 0.2017 0.2091 5.7197 1.4778 +#> 319: 93.0659 -6.0176 -1.9819 -4.4482 -0.9511 0.1800 6.5263 3.9425 1.0768 3.0313 0.2018 0.2088 5.7218 1.4772 +#> 320: 93.0607 -6.0165 -1.9822 -4.4484 -0.9510 0.1798 6.5554 3.9372 1.0777 3.0329 0.2019 0.2087 5.7236 1.4771 +#> 321: 93.0551 -6.0145 -1.9825 -4.4487 -0.9509 0.1797 6.5844 3.9275 1.0787 3.0368 0.2021 0.2085 5.7256 1.4766 +#> 322: 93.0531 -6.0130 -1.9827 -4.4491 -0.9507 0.1797 6.6073 3.9201 1.0797 3.0400 0.2021 0.2082 5.7250 1.4759 +#> 323: 93.0477 -6.0123 -1.9828 -4.4493 -0.9506 0.1794 6.6255 3.9149 1.0804 3.0420 0.2021 0.2080 5.7249 1.4756 +#> 324: 93.0425 -6.0107 -1.9829 -4.4498 -0.9504 0.1792 6.6282 3.9060 1.0813 3.0457 0.2022 0.2078 5.7250 1.4754 +#> 325: 93.0389 -6.0090 -1.9830 -4.4504 -0.9503 0.1792 6.6252 3.8965 1.0819 3.0496 0.2022 0.2077 5.7246 1.4749 +#> 326: 93.0411 -6.0093 -1.9832 -4.4509 -0.9503 0.1795 6.6358 3.8976 1.0827 3.0516 0.2022 0.2076 5.7248 1.4738 +#> 327: 93.0418 -6.0095 -1.9834 -4.4514 -0.9503 0.1797 6.6415 3.8962 1.0834 3.0533 0.2022 0.2075 5.7237 1.4737 +#> 328: 93.0434 -6.0093 -1.9835 -4.4520 -0.9503 0.1798 6.6621 3.8957 1.0841 3.0550 0.2022 0.2074 5.7247 1.4731 +#> 329: 93.0446 -6.0109 -1.9836 -4.4522 -0.9503 0.1798 6.6763 3.9048 1.0847 3.0543 0.2022 0.2072 5.7259 1.4725 +#> 330: 93.0451 -6.0133 -1.9838 -4.4518 -0.9503 0.1799 6.6859 3.9192 1.0852 3.0521 0.2022 0.2070 5.7252 1.4719 +#> 331: 93.0456 -6.0136 -1.9838 -4.4516 -0.9503 0.1799 6.6773 3.9217 1.0858 3.0505 0.2022 0.2067 5.7250 1.4715 +#> 332: 93.0463 -6.0133 -1.9839 -4.4515 -0.9504 0.1799 6.6560 3.9195 1.0863 3.0494 0.2022 0.2063 5.7255 1.4710 +#> 333: 93.0496 -6.0122 -1.9839 -4.4513 -0.9505 0.1800 6.6484 3.9134 1.0869 3.0474 0.2022 0.2060 5.7253 1.4705 +#> 334: 93.0520 -6.0105 -1.9838 -4.4513 -0.9505 0.1801 6.6314 3.9035 1.0877 3.0462 0.2022 0.2056 5.7259 1.4702 +#> 335: 93.0550 -6.0088 -1.9836 -4.4510 -0.9507 0.1800 6.6194 3.8941 1.0887 3.0451 0.2022 0.2051 5.7263 1.4702 +#> 336: 93.0554 -6.0081 -1.9834 -4.4509 -0.9508 0.1800 6.6100 3.8896 1.0896 3.0444 0.2022 0.2048 5.7266 1.4705 +#> 337: 93.0582 -6.0067 -1.9832 -4.4507 -0.9509 0.1800 6.6089 3.8805 1.0904 3.0445 0.2021 0.2044 5.7260 1.4706 +#> 338: 93.0631 -6.0073 -1.9831 -4.4507 -0.9511 0.1801 6.5993 3.8798 1.0908 3.0443 0.2021 0.2040 5.7250 1.4711 +#> 339: 93.0689 -6.0071 -1.9831 -4.4508 -0.9513 0.1803 6.5976 3.8749 1.0911 3.0442 0.2021 0.2037 5.7240 1.4714 +#> 340: 93.0694 -6.0085 -1.9831 -4.4507 -0.9516 0.1804 6.5915 3.8779 1.0914 3.0436 0.2022 0.2032 5.7227 1.4711 +#> 341: 93.0709 -6.0097 -1.9830 -4.4508 -0.9518 0.1804 6.5862 3.8803 1.0915 3.0429 0.2023 0.2026 5.7213 1.4715 +#> 342: 93.0741 -6.0104 -1.9829 -4.4507 -0.9521 0.1804 6.5894 3.8812 1.0918 3.0417 0.2024 0.2022 5.7204 1.4714 +#> 343: 93.0781 -6.0122 -1.9829 -4.4505 -0.9523 0.1804 6.5907 3.8870 1.0921 3.0410 0.2024 0.2016 5.7202 1.4712 +#> 344: 93.0818 -6.0134 -1.9829 -4.4503 -0.9525 0.1804 6.5908 3.8895 1.0926 3.0400 0.2025 0.2011 5.7182 1.4712 +#> 345: 93.0850 -6.0148 -1.9829 -4.4500 -0.9528 0.1806 6.5984 3.8931 1.0926 3.0387 0.2026 0.2006 5.7169 1.4712 +#> 346: 93.0849 -6.0155 -1.9831 -4.4502 -0.9529 0.1807 6.6079 3.8986 1.0931 3.0401 0.2028 0.2002 5.7172 1.4716 +#> 347: 93.0859 -6.0161 -1.9832 -4.4503 -0.9530 0.1809 6.6307 3.9028 1.0941 3.0404 0.2029 0.1998 5.7170 1.4712 +#> 348: 93.0885 -6.0173 -1.9833 -4.4503 -0.9532 0.1809 6.6470 3.9096 1.0951 3.0404 0.2030 0.1993 5.7174 1.4708 +#> 349: 93.0894 -6.0189 -1.9835 -4.4503 -0.9534 0.1810 6.6443 3.9190 1.0955 3.0410 0.2031 0.1989 5.7175 1.4707 +#> 350: 93.0924 -6.0196 -1.9836 -4.4502 -0.9535 0.1813 6.6543 3.9218 1.0957 3.0409 0.2032 0.1983 5.7182 1.4705 +#> 351: 93.0938 -6.0203 -1.9838 -4.4503 -0.9536 0.1814 6.6630 3.9233 1.0963 3.0417 0.2032 0.1977 5.7189 1.4703 +#> 352: 93.0946 -6.0210 -1.9838 -4.4505 -0.9537 0.1816 6.6698 3.9263 1.0968 3.0432 0.2033 0.1973 5.7196 1.4701 +#> 353: 93.0969 -6.0214 -1.9839 -4.4505 -0.9538 0.1818 6.6837 3.9270 1.0973 3.0442 0.2034 0.1968 5.7199 1.4701 +#> 354: 93.1014 -6.0199 -1.9839 -4.4504 -0.9539 0.1817 6.7040 3.9204 1.0978 3.0438 0.2034 0.1962 5.7191 1.4703 +#> 355: 93.1035 -6.0197 -1.9838 -4.4502 -0.9539 0.1816 6.7119 3.9222 1.0983 3.0433 0.2034 0.1957 5.7194 1.4706 +#> 356: 93.1055 -6.0198 -1.9839 -4.4496 -0.9539 0.1815 6.7302 3.9277 1.0989 3.0409 0.2035 0.1952 5.7206 1.4707 +#> 357: 93.1080 -6.0188 -1.9837 -4.4490 -0.9540 0.1813 6.7558 3.9243 1.0997 3.0386 0.2035 0.1948 5.7217 1.4706 +#> 358: 93.1111 -6.0182 -1.9835 -4.4484 -0.9541 0.1812 6.7733 3.9204 1.1005 3.0365 0.2035 0.1944 5.7209 1.4700 +#> 359: 93.1148 -6.0175 -1.9834 -4.4481 -0.9542 0.1811 6.7997 3.9151 1.1012 3.0355 0.2035 0.1940 5.7191 1.4696 +#> 360: 93.1157 -6.0176 -1.9832 -4.4478 -0.9543 0.1810 6.8133 3.9155 1.1017 3.0340 0.2035 0.1937 5.7158 1.4691 +#> 361: 93.1169 -6.0185 -1.9830 -4.4476 -0.9544 0.1808 6.8098 3.9232 1.1022 3.0328 0.2035 0.1934 5.7143 1.4690 +#> 362: 93.1173 -6.0205 -1.9829 -4.4472 -0.9545 0.1805 6.8125 3.9361 1.1024 3.0319 0.2035 0.1931 5.7137 1.4693 +#> 363: 93.1162 -6.0230 -1.9828 -4.4467 -0.9545 0.1801 6.8240 3.9524 1.1025 3.0312 0.2035 0.1928 5.7125 1.4695 +#> 364: 93.1173 -6.0240 -1.9826 -4.4464 -0.9546 0.1799 6.8341 3.9575 1.1027 3.0307 0.2035 0.1924 5.7092 1.4695 +#> 365: 93.1199 -6.0259 -1.9824 -4.4462 -0.9547 0.1796 6.8476 3.9687 1.1028 3.0316 0.2036 0.1920 5.7073 1.4695 +#> 366: 93.1220 -6.0277 -1.9821 -4.4461 -0.9548 0.1793 6.8542 3.9777 1.1032 3.0319 0.2037 0.1916 5.7060 1.4694 +#> 367: 93.1230 -6.0287 -1.9819 -4.4460 -0.9548 0.1791 6.8633 3.9829 1.1038 3.0331 0.2038 0.1914 5.7056 1.4693 +#> 368: 93.1255 -6.0276 -1.9816 -4.4459 -0.9549 0.1789 6.8734 3.9764 1.1038 3.0341 0.2038 0.1912 5.7050 1.4695 +#> 369: 93.1258 -6.0263 -1.9814 -4.4461 -0.9549 0.1787 6.8756 3.9698 1.1039 3.0357 0.2039 0.1910 5.7031 1.4697 +#> 370: 93.1288 -6.0252 -1.9811 -4.4463 -0.9548 0.1785 6.8892 3.9639 1.1039 3.0375 0.2040 0.1909 5.7029 1.4701 +#> 371: 93.1317 -6.0245 -1.9810 -4.4467 -0.9548 0.1784 6.8974 3.9601 1.1037 3.0391 0.2040 0.1907 5.7037 1.4700 +#> 372: 93.1346 -6.0233 -1.9811 -4.4465 -0.9548 0.1781 6.9042 3.9536 1.1035 3.0386 0.2040 0.1905 5.7038 1.4700 +#> 373: 93.1340 -6.0234 -1.9810 -4.4461 -0.9547 0.1778 6.9034 3.9548 1.1034 3.0371 0.2039 0.1903 5.7040 1.4698 +#> 374: 93.1324 -6.0230 -1.9811 -4.4456 -0.9547 0.1775 6.9080 3.9527 1.1034 3.0349 0.2038 0.1901 5.7055 1.4691 +#> 375: 93.1309 -6.0226 -1.9812 -4.4451 -0.9546 0.1773 6.9093 3.9493 1.1034 3.0334 0.2037 0.1899 5.7063 1.4683 +#> 376: 93.1298 -6.0215 -1.9811 -4.4447 -0.9546 0.1770 6.9039 3.9432 1.1035 3.0319 0.2036 0.1897 5.7064 1.4678 +#> 377: 93.1296 -6.0209 -1.9811 -4.4443 -0.9546 0.1768 6.8932 3.9390 1.1036 3.0305 0.2035 0.1895 5.7056 1.4672 +#> 378: 93.1292 -6.0200 -1.9810 -4.4438 -0.9545 0.1764 6.8850 3.9349 1.1037 3.0288 0.2034 0.1892 5.7068 1.4667 +#> 379: 93.1284 -6.0196 -1.9808 -4.4432 -0.9544 0.1760 6.8766 3.9318 1.1038 3.0266 0.2033 0.1890 5.7072 1.4665 +#> 380: 93.1304 -6.0182 -1.9806 -4.4425 -0.9543 0.1756 6.8737 3.9249 1.1040 3.0236 0.2033 0.1888 5.7074 1.4662 +#> 381: 93.1315 -6.0169 -1.9804 -4.4417 -0.9542 0.1754 6.8707 3.9193 1.1040 3.0210 0.2032 0.1886 5.7066 1.4661 +#> 382: 93.1331 -6.0160 -1.9801 -4.4409 -0.9542 0.1750 6.8645 3.9150 1.1040 3.0187 0.2032 0.1885 5.7063 1.4664 +#> 383: 93.1334 -6.0153 -1.9800 -4.4403 -0.9542 0.1746 6.8599 3.9123 1.1037 3.0167 0.2032 0.1882 5.7074 1.4665 +#> 384: 93.1328 -6.0140 -1.9801 -4.4397 -0.9540 0.1742 6.8600 3.9074 1.1034 3.0149 0.2031 0.1879 5.7072 1.4667 +#> 385: 93.1306 -6.0137 -1.9801 -4.4392 -0.9539 0.1739 6.8449 3.9073 1.1031 3.0137 0.2030 0.1876 5.7084 1.4665 +#> 386: 93.1281 -6.0134 -1.9801 -4.4388 -0.9539 0.1735 6.8356 3.9088 1.1028 3.0123 0.2029 0.1872 5.7087 1.4667 +#> 387: 93.1267 -6.0141 -1.9801 -4.4384 -0.9537 0.1732 6.8364 3.9150 1.1025 3.0110 0.2028 0.1869 5.7101 1.4669 +#> 388: 93.1252 -6.0142 -1.9801 -4.4380 -0.9536 0.1730 6.8374 3.9192 1.1022 3.0097 0.2028 0.1866 5.7110 1.4670 +#> 389: 93.1223 -6.0140 -1.9801 -4.4375 -0.9535 0.1728 6.8334 3.9209 1.1019 3.0083 0.2028 0.1862 5.7105 1.4674 +#> 390: 93.1221 -6.0144 -1.9800 -4.4371 -0.9534 0.1726 6.8248 3.9256 1.1014 3.0068 0.2028 0.1859 5.7098 1.4675 +#> 391: 93.1210 -6.0149 -1.9799 -4.4365 -0.9533 0.1725 6.8339 3.9293 1.1011 3.0054 0.2028 0.1856 5.7109 1.4678 +#> 392: 93.1193 -6.0145 -1.9799 -4.4360 -0.9532 0.1724 6.8360 3.9279 1.1009 3.0040 0.2028 0.1852 5.7107 1.4678 +#> 393: 93.1200 -6.0149 -1.9799 -4.4357 -0.9532 0.1723 6.8461 3.9287 1.1005 3.0019 0.2028 0.1849 5.7100 1.4678 +#> 394: 93.1202 -6.0138 -1.9799 -4.4355 -0.9532 0.1723 6.8520 3.9229 1.1006 3.0003 0.2028 0.1846 5.7085 1.4679 +#> 395: 93.1203 -6.0134 -1.9800 -4.4354 -0.9532 0.1723 6.8583 3.9200 1.1005 2.9987 0.2027 0.1844 5.7072 1.4680 +#> 396: 93.1195 -6.0131 -1.9800 -4.4353 -0.9532 0.1724 6.8593 3.9169 1.1004 2.9969 0.2027 0.1842 5.7062 1.4676 +#> 397: 93.1195 -6.0130 -1.9801 -4.4352 -0.9532 0.1724 6.8591 3.9143 1.1004 2.9958 0.2027 0.1839 5.7046 1.4675 +#> 398: 93.1200 -6.0128 -1.9801 -4.4352 -0.9532 0.1725 6.8522 3.9125 1.1004 2.9945 0.2028 0.1836 5.7032 1.4675 +#> 399: 93.1200 -6.0135 -1.9803 -4.4351 -0.9531 0.1726 6.8471 3.9166 1.1003 2.9933 0.2028 0.1833 5.7032 1.4673 +#> 400: 93.1204 -6.0139 -1.9803 -4.4351 -0.9531 0.1727 6.8438 3.9191 1.1003 2.9918 0.2027 0.1832 5.7026 1.4671 +#> 401: 93.1198 -6.0139 -1.9804 -4.4351 -0.9530 0.1728 6.8373 3.9186 1.1004 2.9901 0.2027 0.1831 5.7015 1.4670 +#> 402: 93.1199 -6.0141 -1.9804 -4.4351 -0.9530 0.1729 6.8357 3.9194 1.1005 2.9882 0.2027 0.1830 5.7003 1.4671 +#> 403: 93.1196 -6.0155 -1.9804 -4.4350 -0.9530 0.1730 6.8285 3.9255 1.1007 2.9863 0.2026 0.1829 5.7001 1.4671 +#> 404: 93.1183 -6.0164 -1.9805 -4.4350 -0.9531 0.1732 6.8204 3.9308 1.1009 2.9843 0.2026 0.1829 5.7008 1.4670 +#> 405: 93.1178 -6.0161 -1.9805 -4.4350 -0.9532 0.1733 6.8205 3.9286 1.1012 2.9823 0.2025 0.1829 5.7013 1.4669 +#> 406: 93.1176 -6.0171 -1.9806 -4.4348 -0.9533 0.1735 6.8253 3.9319 1.1013 2.9801 0.2025 0.1828 5.7026 1.4666 +#> 407: 93.1168 -6.0185 -1.9807 -4.4348 -0.9533 0.1736 6.8290 3.9373 1.1015 2.9788 0.2024 0.1830 5.7033 1.4664 +#> 408: 93.1165 -6.0198 -1.9808 -4.4349 -0.9534 0.1738 6.8217 3.9428 1.1017 2.9773 0.2023 0.1830 5.7047 1.4663 +#> 409: 93.1165 -6.0210 -1.9809 -4.4350 -0.9534 0.1741 6.8208 3.9505 1.1019 2.9761 0.2021 0.1830 5.7055 1.4661 +#> 410: 93.1169 -6.0230 -1.9810 -4.4351 -0.9535 0.1745 6.8239 3.9617 1.1020 2.9751 0.2020 0.1829 5.7052 1.4658 +#> 411: 93.1166 -6.0237 -1.9811 -4.4353 -0.9536 0.1748 6.8234 3.9664 1.1020 2.9741 0.2019 0.1829 5.7043 1.4657 +#> 412: 93.1164 -6.0235 -1.9812 -4.4355 -0.9536 0.1751 6.8205 3.9643 1.1020 2.9735 0.2017 0.1827 5.7053 1.4654 +#> 413: 93.1182 -6.0232 -1.9814 -4.4356 -0.9537 0.1755 6.8133 3.9615 1.1020 2.9726 0.2016 0.1825 5.7070 1.4650 +#> 414: 93.1190 -6.0226 -1.9815 -4.4360 -0.9537 0.1760 6.8113 3.9578 1.1021 2.9726 0.2015 0.1825 5.7081 1.4648 +#> 415: 93.1183 -6.0226 -1.9817 -4.4364 -0.9538 0.1765 6.8081 3.9557 1.1021 2.9725 0.2014 0.1824 5.7085 1.4646 +#> 416: 93.1185 -6.0238 -1.9818 -4.4369 -0.9538 0.1768 6.8134 3.9617 1.1020 2.9734 0.2013 0.1822 5.7103 1.4645 +#> 417: 93.1190 -6.0245 -1.9819 -4.4373 -0.9540 0.1770 6.8164 3.9664 1.1022 2.9743 0.2012 0.1819 5.7102 1.4650 +#> 418: 93.1219 -6.0256 -1.9818 -4.4376 -0.9542 0.1773 6.8206 3.9710 1.1026 2.9745 0.2011 0.1816 5.7110 1.4655 +#> 419: 93.1255 -6.0261 -1.9817 -4.4381 -0.9543 0.1776 6.8183 3.9714 1.1030 2.9759 0.2010 0.1814 5.7134 1.4659 +#> 420: 93.1294 -6.0262 -1.9816 -4.4385 -0.9546 0.1779 6.8113 3.9704 1.1033 2.9768 0.2009 0.1810 5.7156 1.4666 +#> 421: 93.1319 -6.0259 -1.9815 -4.4392 -0.9547 0.1781 6.7989 3.9685 1.1036 2.9786 0.2008 0.1808 5.7171 1.4676 +#> 422: 93.1338 -6.0263 -1.9814 -4.4398 -0.9548 0.1783 6.7922 3.9681 1.1038 2.9806 0.2006 0.1808 5.7179 1.4681 +#> 423: 93.1353 -6.0266 -1.9813 -4.4406 -0.9550 0.1786 6.7868 3.9674 1.1040 2.9837 0.2006 0.1808 5.7181 1.4687 +#> 424: 93.1374 -6.0270 -1.9811 -4.4414 -0.9550 0.1787 6.7758 3.9674 1.1043 2.9866 0.2004 0.1807 5.7198 1.4693 +#> 425: 93.1383 -6.0270 -1.9811 -4.4420 -0.9551 0.1787 6.7547 3.9674 1.1042 2.9887 0.2003 0.1806 5.7211 1.4702 +#> 426: 93.1400 -6.0268 -1.9811 -4.4427 -0.9551 0.1789 6.7376 3.9654 1.1043 2.9917 0.2002 0.1805 5.7241 1.4706 +#> 427: 93.1391 -6.0268 -1.9811 -4.4433 -0.9552 0.1790 6.7196 3.9634 1.1045 2.9951 0.2001 0.1805 5.7271 1.4710 +#> 428: 93.1404 -6.0268 -1.9810 -4.4442 -0.9552 0.1792 6.7104 3.9628 1.1044 2.9999 0.2000 0.1803 5.7282 1.4712 +#> 429: 93.1431 -6.0265 -1.9810 -4.4450 -0.9553 0.1793 6.7029 3.9612 1.1045 3.0043 0.1999 0.1803 5.7293 1.4716 +#> 430: 93.1464 -6.0263 -1.9809 -4.4457 -0.9554 0.1795 6.6962 3.9606 1.1046 3.0074 0.1999 0.1802 5.7291 1.4724 +#> 431: 93.1485 -6.0267 -1.9809 -4.4460 -0.9555 0.1797 6.6865 3.9623 1.1046 3.0082 0.1998 0.1802 5.7287 1.4726 +#> 432: 93.1509 -6.0277 -1.9808 -4.4462 -0.9556 0.1798 6.6843 3.9658 1.1047 3.0086 0.1998 0.1801 5.7280 1.4727 +#> 433: 93.1528 -6.0289 -1.9806 -4.4464 -0.9557 0.1798 6.6840 3.9714 1.1049 3.0087 0.1998 0.1801 5.7282 1.4729 +#> 434: 93.1555 -6.0286 -1.9804 -4.4467 -0.9557 0.1798 6.6870 3.9693 1.1052 3.0094 0.1997 0.1800 5.7277 1.4729 +#> 435: 93.1574 -6.0290 -1.9803 -4.4467 -0.9558 0.1798 6.6893 3.9712 1.1055 3.0095 0.1996 0.1800 5.7278 1.4727 +#> 436: 93.1594 -6.0299 -1.9802 -4.4468 -0.9558 0.1798 6.6934 3.9749 1.1059 3.0103 0.1996 0.1801 5.7271 1.4727 +#> 437: 93.1600 -6.0311 -1.9800 -4.4469 -0.9558 0.1797 6.7010 3.9812 1.1065 3.0110 0.1996 0.1801 5.7275 1.4727 +#> 438: 93.1617 -6.0318 -1.9799 -4.4471 -0.9559 0.1796 6.7120 3.9865 1.1069 3.0121 0.1995 0.1801 5.7271 1.4727 +#> 439: 93.1634 -6.0329 -1.9798 -4.4472 -0.9559 0.1795 6.7279 3.9930 1.1075 3.0127 0.1995 0.1802 5.7268 1.4727 +#> 440: 93.1644 -6.0332 -1.9797 -4.4473 -0.9559 0.1794 6.7338 3.9962 1.1080 3.0136 0.1994 0.1803 5.7270 1.4726 +#> 441: 93.1654 -6.0335 -1.9795 -4.4477 -0.9558 0.1794 6.7435 3.9988 1.1085 3.0155 0.1994 0.1805 5.7274 1.4728 +#> 442: 93.1670 -6.0340 -1.9792 -4.4480 -0.9558 0.1794 6.7493 4.0028 1.1091 3.0173 0.1993 0.1808 5.7282 1.4729 +#> 443: 93.1685 -6.0346 -1.9790 -4.4485 -0.9558 0.1793 6.7577 4.0073 1.1092 3.0202 0.1992 0.1811 5.7267 1.4732 +#> 444: 93.1671 -6.0346 -1.9789 -4.4491 -0.9558 0.1792 6.7559 4.0069 1.1093 3.0238 0.1992 0.1813 5.7258 1.4733 +#> 445: 93.1655 -6.0355 -1.9789 -4.4497 -0.9557 0.1790 6.7552 4.0127 1.1094 3.0276 0.1992 0.1814 5.7262 1.4733 +#> 446: 93.1641 -6.0361 -1.9787 -4.4501 -0.9557 0.1789 6.7579 4.0169 1.1096 3.0306 0.1991 0.1816 5.7262 1.4732 +#> 447: 93.1628 -6.0363 -1.9786 -4.4503 -0.9556 0.1787 6.7680 4.0196 1.1099 3.0318 0.1991 0.1818 5.7258 1.4729 +#> 448: 93.1629 -6.0371 -1.9787 -4.4509 -0.9556 0.1786 6.7705 4.0248 1.1100 3.0358 0.1990 0.1820 5.7267 1.4725 +#> 449: 93.1626 -6.0381 -1.9785 -4.4510 -0.9556 0.1784 6.7800 4.0298 1.1101 3.0368 0.1989 0.1822 5.7266 1.4722 +#> 450: 93.1614 -6.0386 -1.9782 -4.4514 -0.9556 0.1782 6.7796 4.0316 1.1103 3.0392 0.1989 0.1824 5.7260 1.4720 +#> 451: 93.1603 -6.0397 -1.9779 -4.4518 -0.9556 0.1780 6.7799 4.0381 1.1107 3.0416 0.1988 0.1827 5.7264 1.4720 +#> 452: 93.1610 -6.0406 -1.9775 -4.4522 -0.9556 0.1777 6.7813 4.0424 1.1111 3.0443 0.1988 0.1828 5.7268 1.4719 +#> 453: 93.1618 -6.0414 -1.9771 -4.4523 -0.9556 0.1774 6.7814 4.0490 1.1115 3.0456 0.1987 0.1830 5.7262 1.4721 +#> 454: 93.1625 -6.0415 -1.9767 -4.4525 -0.9555 0.1771 6.7799 4.0499 1.1118 3.0473 0.1986 0.1831 5.7260 1.4723 +#> 455: 93.1636 -6.0412 -1.9765 -4.4528 -0.9555 0.1769 6.7778 4.0489 1.1123 3.0496 0.1985 0.1832 5.7268 1.4722 +#> 456: 93.1653 -6.0401 -1.9762 -4.4532 -0.9554 0.1768 6.7703 4.0441 1.1127 3.0517 0.1983 0.1834 5.7282 1.4725 +#> 457: 93.1672 -6.0396 -1.9760 -4.4535 -0.9554 0.1766 6.7683 4.0427 1.1129 3.0539 0.1982 0.1835 5.7281 1.4727 +#> 458: 93.1692 -6.0398 -1.9757 -4.4539 -0.9554 0.1765 6.7627 4.0450 1.1132 3.0570 0.1981 0.1835 5.7294 1.4729 +#> 459: 93.1708 -6.0402 -1.9756 -4.4542 -0.9554 0.1763 6.7615 4.0483 1.1133 3.0596 0.1980 0.1836 5.7320 1.4728 +#> 460: 93.1710 -6.0401 -1.9755 -4.4544 -0.9553 0.1762 6.7629 4.0487 1.1135 3.0615 0.1979 0.1835 5.7323 1.4730 +#> 461: 93.1708 -6.0403 -1.9755 -4.4546 -0.9552 0.1762 6.7639 4.0492 1.1136 3.0631 0.1978 0.1834 5.7321 1.4729 +#> 462: 93.1707 -6.0405 -1.9755 -4.4548 -0.9552 0.1760 6.7657 4.0506 1.1136 3.0647 0.1977 0.1833 5.7323 1.4727 +#> 463: 93.1690 -6.0403 -1.9755 -4.4548 -0.9551 0.1759 6.7607 4.0494 1.1136 3.0651 0.1976 0.1832 5.7332 1.4726 +#> 464: 93.1673 -6.0400 -1.9755 -4.4548 -0.9551 0.1758 6.7588 4.0480 1.1138 3.0652 0.1975 0.1832 5.7344 1.4724 +#> 465: 93.1657 -6.0399 -1.9755 -4.4548 -0.9550 0.1756 6.7601 4.0474 1.1138 3.0652 0.1974 0.1831 5.7350 1.4724 +#> 466: 93.1656 -6.0406 -1.9754 -4.4548 -0.9549 0.1755 6.7589 4.0514 1.1139 3.0658 0.1973 0.1831 5.7355 1.4723 +#> 467: 93.1657 -6.0408 -1.9753 -4.4548 -0.9549 0.1754 6.7558 4.0525 1.1139 3.0664 0.1972 0.1831 5.7358 1.4725 +#> 468: 93.1664 -6.0411 -1.9752 -4.4551 -0.9548 0.1753 6.7546 4.0551 1.1140 3.0679 0.1971 0.1832 5.7358 1.4723 +#> 469: 93.1667 -6.0412 -1.9751 -4.4552 -0.9547 0.1752 6.7547 4.0554 1.1141 3.0676 0.1970 0.1833 5.7354 1.4721 +#> 470: 93.1664 -6.0413 -1.9750 -4.4552 -0.9546 0.1751 6.7579 4.0564 1.1143 3.0676 0.1969 0.1833 5.7352 1.4718 +#> 471: 93.1656 -6.0411 -1.9750 -4.4553 -0.9545 0.1750 6.7611 4.0555 1.1142 3.0681 0.1968 0.1834 5.7354 1.4715 +#> 472: 93.1644 -6.0408 -1.9751 -4.4554 -0.9544 0.1749 6.7577 4.0542 1.1142 3.0686 0.1968 0.1834 5.7362 1.4712 +#> 473: 93.1632 -6.0405 -1.9751 -4.4554 -0.9543 0.1749 6.7527 4.0526 1.1141 3.0686 0.1967 0.1835 5.7363 1.4708 +#> 474: 93.1619 -6.0405 -1.9752 -4.4555 -0.9542 0.1748 6.7479 4.0521 1.1140 3.0689 0.1967 0.1835 5.7366 1.4705 +#> 475: 93.1609 -6.0413 -1.9753 -4.4557 -0.9542 0.1748 6.7469 4.0558 1.1139 3.0698 0.1967 0.1835 5.7379 1.4702 +#> 476: 93.1607 -6.0411 -1.9754 -4.4556 -0.9542 0.1747 6.7414 4.0549 1.1139 3.0697 0.1966 0.1835 5.7388 1.4698 +#> 477: 93.1597 -6.0413 -1.9754 -4.4560 -0.9542 0.1747 6.7321 4.0560 1.1137 3.0733 0.1966 0.1836 5.7392 1.4697 +#> 478: 93.1591 -6.0421 -1.9754 -4.4563 -0.9542 0.1745 6.7239 4.0608 1.1137 3.0765 0.1965 0.1836 5.7399 1.4697 +#> 479: 93.1589 -6.0438 -1.9754 -4.4564 -0.9542 0.1744 6.7150 4.0719 1.1136 3.0785 0.1964 0.1838 5.7421 1.4695 +#> 480: 93.1594 -6.0459 -1.9754 -4.4566 -0.9542 0.1742 6.7102 4.0895 1.1135 3.0807 0.1964 0.1839 5.7446 1.4695 +#> 481: 93.1604 -6.0472 -1.9754 -4.4570 -0.9542 0.1741 6.7104 4.1016 1.1135 3.0848 0.1964 0.1841 5.7456 1.4693 +#> 482: 93.1584 -6.0486 -1.9754 -4.4573 -0.9542 0.1739 6.7061 4.1152 1.1136 3.0877 0.1964 0.1842 5.7464 1.4690 +#> 483: 93.1561 -6.0501 -1.9754 -4.4576 -0.9541 0.1737 6.7067 4.1286 1.1135 3.0903 0.1963 0.1843 5.7475 1.4688 +#> 484: 93.1545 -6.0507 -1.9754 -4.4578 -0.9541 0.1737 6.7113 4.1362 1.1134 3.0918 0.1963 0.1845 5.7488 1.4687 +#> 485: 93.1524 -6.0507 -1.9754 -4.4583 -0.9540 0.1736 6.7094 4.1381 1.1134 3.0970 0.1964 0.1847 5.7496 1.4685 +#> 486: 93.1510 -6.0508 -1.9754 -4.4586 -0.9540 0.1735 6.7118 4.1405 1.1134 3.0996 0.1964 0.1847 5.7502 1.4682 +#> 487: 93.1495 -6.0507 -1.9755 -4.4591 -0.9539 0.1734 6.7128 4.1406 1.1134 3.1037 0.1965 0.1848 5.7510 1.4680 +#> 488: 93.1494 -6.0502 -1.9756 -4.4597 -0.9538 0.1734 6.7171 4.1384 1.1135 3.1081 0.1965 0.1848 5.7508 1.4677 +#> 489: 93.1497 -6.0497 -1.9756 -4.4604 -0.9538 0.1734 6.7188 4.1358 1.1135 3.1133 0.1966 0.1847 5.7499 1.4675 +#> 490: 93.1507 -6.0486 -1.9757 -4.4607 -0.9538 0.1735 6.7206 4.1319 1.1136 3.1157 0.1967 0.1847 5.7498 1.4672 +#> 491: 93.1507 -6.0476 -1.9757 -4.4612 -0.9537 0.1735 6.7141 4.1270 1.1136 3.1187 0.1968 0.1846 5.7503 1.4672 +#> 492: 93.1507 -6.0470 -1.9758 -4.4618 -0.9536 0.1735 6.7140 4.1238 1.1139 3.1218 0.1969 0.1846 5.7511 1.4669 +#> 493: 93.1513 -6.0468 -1.9758 -4.4623 -0.9535 0.1736 6.7214 4.1232 1.1141 3.1246 0.1970 0.1845 5.7514 1.4668 +#> 494: 93.1511 -6.0467 -1.9759 -4.4629 -0.9534 0.1737 6.7332 4.1232 1.1144 3.1278 0.1971 0.1845 5.7512 1.4664 +#> 495: 93.1511 -6.0464 -1.9761 -4.4635 -0.9533 0.1738 6.7377 4.1218 1.1145 3.1309 0.1972 0.1845 5.7515 1.4661 +#> 496: 93.1498 -6.0465 -1.9762 -4.4639 -0.9532 0.1739 6.7412 4.1241 1.1147 3.1325 0.1974 0.1845 5.7514 1.4657 +#> 497: 93.1482 -6.0467 -1.9764 -4.4644 -0.9532 0.1741 6.7506 4.1259 1.1149 3.1346 0.1975 0.1846 5.7513 1.4652 +#> 498: 93.1479 -6.0465 -1.9765 -4.4647 -0.9531 0.1743 6.7588 4.1263 1.1150 3.1357 0.1977 0.1846 5.7511 1.4648 +#> 499: 93.1462 -6.0455 -1.9766 -4.4651 -0.9530 0.1745 6.7659 4.1219 1.1152 3.1374 0.1978 0.1847 5.7515 1.4645 +#> 500: 93.1455 -6.0439 -1.9768 -4.4657 -0.9529 0.1747 6.7747 4.1151 1.1154 3.1404 0.1980 0.1848 5.7516 1.4641</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='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'>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_A1 |f_parent_qlogis | log_k1 | +#> |.....................| log_k2 | g_qlogis |sigma_parent | sigma_A1 | +#> |.....................| o1 | o2 | o3 | o4 | +#> <span style='text-decoration: underline;'>|.....................| o5 | o6 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 488.12318 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 488.12318 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 488.12318</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | G| Gill Diff. | 52.24 | 2.364 | -0.1419 | 0.08101 | +#> |.....................| -0.5200 | 0.08781 | -28.20 | -16.37 | +#> |.....................| 14.83 | 13.24 | -12.01 | -2.482 | +#> <span style='text-decoration: underline;'>|.....................| 5.466 | -10.09 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 2642.5634 | 0.2192 | -1.035 | -0.9096 | -0.9332 | +#> |.....................| -0.9743 | -0.8898 | -0.4296 | -0.6255 | +#> |.....................| -1.099 | -1.073 | -0.6891 | -0.8357 | +#> <span style='text-decoration: underline;'>|.....................| -0.9567 | -0.7180 |...........|...........|</span> +#> | U| 2642.5634 | 20.48 | -5.348 | -0.9517 | -1.954 | +#> |.....................| -4.421 | 0.1928 | 2.469 | 1.224 | +#> |.....................| 0.5606 | 0.7036 | 1.386 | 1.005 | +#> <span style='text-decoration: underline;'>|.....................| 0.7896 | 1.336 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 2642.5634</span> | 20.48 | 0.004759 | 0.2785 | 0.1417 | +#> |.....................| 0.01202 | 0.5480 | 2.469 | 1.224 | +#> |.....................| 0.5606 | 0.7036 | 1.386 | 1.005 | +#> <span style='text-decoration: underline;'>|.....................| 0.7896 | 1.336 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 546.98314 | 0.9219 | -1.004 | -0.9115 | -0.9321 | +#> |.....................| -0.9813 | -0.8886 | -0.8089 | -0.8458 | +#> |.....................| -0.9000 | -0.8944 | -0.8506 | -0.8691 | +#> <span style='text-decoration: underline;'>|.....................| -0.8831 | -0.8538 |...........|...........|</span> +#> | U| 546.98314 | 86.13 | -5.316 | -0.9535 | -1.953 | +#> |.....................| -4.428 | 0.1930 | 2.082 | 1.104 | +#> |.....................| 0.7044 | 0.8599 | 1.196 | 0.9723 | +#> <span style='text-decoration: underline;'>|.....................| 0.8529 | 1.178 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 546.98314</span> | 86.13 | 0.004913 | 0.2782 | 0.1419 | +#> |.....................| 0.01193 | 0.5481 | 2.082 | 1.104 | +#> |.....................| 0.7044 | 0.8599 | 1.196 | 0.9723 | +#> <span style='text-decoration: underline;'>|.....................| 0.8529 | 1.178 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 506.37737 | 0.9922 | -1.000 | -0.9117 | -0.9320 | +#> |.....................| -0.9820 | -0.8885 | -0.8469 | -0.8679 | +#> |.....................| -0.8800 | -0.8766 | -0.8668 | -0.8724 | +#> <span style='text-decoration: underline;'>|.....................| -0.8758 | -0.8674 |...........|...........|</span> +#> | U| 506.37737 | 92.70 | -5.313 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.043 | 1.092 | +#> |.....................| 0.7187 | 0.8755 | 1.177 | 0.9691 | +#> <span style='text-decoration: underline;'>|.....................| 0.8592 | 1.163 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.37737</span> | 92.70 | 0.004928 | 0.2781 | 0.1419 | +#> |.....................| 0.01193 | 0.5481 | 2.043 | 1.092 | +#> |.....................| 0.7187 | 0.8755 | 1.177 | 0.9691 | +#> <span style='text-decoration: underline;'>|.....................| 0.8592 | 1.163 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 506.42840 | 0.9992 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8507 | -0.8701 | +#> |.....................| -0.8780 | -0.8748 | -0.8684 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8751 | -0.8687 |...........|...........|</span> +#> | U| 506.4284 | 93.35 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.091 | +#> |.....................| 0.7202 | 0.8771 | 1.175 | 0.9688 | +#> <span style='text-decoration: underline;'>|.....................| 0.8598 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.4284</span> | 93.35 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.091 | +#> |.....................| 0.7202 | 0.8771 | 1.175 | 0.9688 | +#> <span style='text-decoration: underline;'>|.....................| 0.8598 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 506.47762 | 0.9999 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.47762 | 93.42 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.47762</span> | 93.42 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 506.48298 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48298 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48298</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 506.48363 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48363 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48363</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | +#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | +#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | +#> <span style='text-decoration: underline;'>|.....................| -0.8750 | -0.8689 |...........|...........|</span> +#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | +#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 506.48371</span> | 93.43 | 0.004930 | 0.2781 | 0.1419 | +#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | +#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | +#> <span style='text-decoration: underline;'>|.....................| 0.8599 | 1.161 |...........|...........|</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='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> -</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> -#> <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[5]+THETA[5];</span> -#> <span class='message'>rx_expr_14~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_16~t*rx_expr_14;</span> -#> <span class='message'>rx_expr_17~1+rx_expr_16;</span> -#> <span class='message'>rx_expr_19~rx_expr_7-(rx_expr_8);</span> -#> <span class='message'>rx_expr_21~exp(rx_expr_19);</span> -#> <span class='message'>d/dt(parent)=-rx_expr_21*parent/(rx_expr_17);</span> -#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> -#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> -#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_21*parent*f_parent_to_A1/(rx_expr_17);</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_15~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_15+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_15+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_18~rx_expr_10*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_18)*(rx_expr_0)+(rx_expr_4+rx_expr_18)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_expr_12~Rx_pow_di(THETA[7],2);</span> -#> <span class='message'>rx_expr_13~Rx_pow_di(THETA[6],2);</span> -#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_18)*(rx_expr_0)+(rx_expr_4+rx_expr_18)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_12+rx_expr_13)*(rx_expr_0)+(rx_expr_12*Rx_pow_di(((rx_expr_4+rx_expr_18)*(rx_expr_1)),2)+rx_expr_13)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=THETA[2];</span> -#> <span class='message'>f_parent_qlogis=THETA[3];</span> -#> <span class='message'>log_alpha=THETA[4];</span> -#> <span class='message'>log_beta=THETA[5];</span> -#> <span class='message'>sigma_low=THETA[6];</span> -#> <span class='message'>rsd_high=THETA[7];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=ETA[2];</span> -#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> -#> <span class='message'>eta.log_alpha=ETA[4];</span> -#> <span class='message'>eta.log_beta=ETA[5];</span> -#> <span class='message'>parent_0_model=rx_expr_6;</span> -#> <span class='message'>k_A1=rx_expr_11;</span> -#> <span class='message'>alpha=exp(rx_expr_7);</span> -#> <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: 8.455 0.377 8.841</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> -#> <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(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(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~A1*(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_expr_17*Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)+rx_expr_18)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=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_A1=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_A1=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_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 0.679 18.41</span></div><div class='input'> +</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_A1 |f_parent_qlogis | log_alpha | +#> |.....................| log_beta | sigma_low | rsd_high | o1 | +#> |.....................| o2 | o3 | o4 | o5 | +#> |<span style='font-weight: bold;'> 1</span>| 504.82714 | 1.000 | -1.000 | -0.9114 | -0.8944 | +#> |.....................| -0.8457 | -0.8687 | -0.8916 | -0.8768 | +#> |.....................| -0.8745 | -0.8676 | -0.8705 | -0.8704 | +#> | U| 504.82714 | 93.12 | -5.303 | -0.9442 | -0.1065 | +#> |.....................| 2.291 | 1.160 | 0.03005 | 0.7578 | +#> |.....................| 0.8738 | 1.213 | 1.069 | 1.072 | +#> | X|<span style='font-weight: bold;'> 504.82714</span> | 93.12 | 0.004975 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.160 | 0.03005 | 0.7578 | +#> |.....................| 0.8738 | 1.213 | 1.069 | 1.072 | +#> | G| Gill Diff. | 73.79 | 2.406 | 0.05615 | 0.2285 | +#> |.....................| 0.009051 | -72.42 | -25.46 | 1.201 | +#> |.....................| 11.89 | -10.88 | -9.982 | -10.81 | +#> |<span style='font-weight: bold;'> 2</span>| 4107.3121 | 0.3213 | -1.022 | -0.9119 | -0.8965 | +#> |.....................| -0.8458 | -0.2026 | -0.6574 | -0.8879 | +#> |.....................| -0.9839 | -0.7675 | -0.7787 | -0.7710 | +#> | U| 4107.3121 | 29.92 | -5.326 | -0.9447 | -0.1086 | +#> |.....................| 2.291 | 1.546 | 0.03357 | 0.7494 | +#> |.....................| 0.7782 | 1.335 | 1.167 | 1.179 | +#> | X|<span style='font-weight: bold;'> 4107.3121</span> | 29.92 | 0.004866 | 0.2800 | 0.8971 | +#> |.....................| 9.883 | 1.546 | 0.03357 | 0.7494 | +#> |.....................| 0.7782 | 1.335 | 1.167 | 1.179 | +#> |<span style='font-weight: bold;'> 3</span>| 528.17103 | 0.9321 | -1.002 | -0.9115 | -0.8946 | +#> |.....................| -0.8457 | -0.8021 | -0.8682 | -0.8779 | +#> |.....................| -0.8854 | -0.8576 | -0.8613 | -0.8605 | +#> | U| 528.17103 | 86.80 | -5.306 | -0.9442 | -0.1067 | +#> |.....................| 2.291 | 1.198 | 0.03041 | 0.7570 | +#> |.....................| 0.8642 | 1.226 | 1.079 | 1.083 | +#> | X|<span style='font-weight: bold;'> 528.17103</span> | 86.80 | 0.004964 | 0.2800 | 0.8988 | +#> |.....................| 9.884 | 1.198 | 0.03041 | 0.7570 | +#> |.....................| 0.8642 | 1.226 | 1.079 | 1.083 | +#> |<span style='font-weight: bold;'> 4</span>| 503.95550 | 0.9892 | -1.000 | -0.9114 | -0.8944 | +#> |.....................| -0.8457 | -0.8581 | -0.8879 | -0.8770 | +#> |.....................| -0.8762 | -0.8660 | -0.8691 | -0.8689 | +#> | U| 503.9555 | 92.11 | -5.304 | -0.9442 | -0.1066 | +#> |.....................| 2.291 | 1.166 | 0.03011 | 0.7577 | +#> |.....................| 0.8723 | 1.215 | 1.070 | 1.074 | +#> | X|<span style='font-weight: bold;'> 503.9555</span> | 92.11 | 0.004973 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.166 | 0.03011 | 0.7577 | +#> |.....................| 0.8723 | 1.215 | 1.070 | 1.074 | +#> | F| Forward Diff. | -82.12 | 2.266 | -0.2557 | 0.1457 | +#> |.....................| -0.3150 | -70.09 | -26.27 | 1.274 | +#> |.....................| 9.305 | -11.84 | -9.592 | -10.45 | +#> |<span style='font-weight: bold;'> 5</span>| 503.06948 | 1.000 | -1.001 | -0.9114 | -0.8944 | +#> |.....................| -0.8456 | -0.8479 | -0.8841 | -0.8772 | +#> |.....................| -0.8776 | -0.8643 | -0.8677 | -0.8674 | +#> | U| 503.06948 | 93.16 | -5.304 | -0.9442 | -0.1066 | +#> |.....................| 2.291 | 1.172 | 0.03017 | 0.7575 | +#> |.....................| 0.8711 | 1.217 | 1.072 | 1.075 | +#> | X|<span style='font-weight: bold;'> 503.06948</span> | 93.16 | 0.004971 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.172 | 0.03017 | 0.7575 | +#> |.....................| 0.8711 | 1.217 | 1.072 | 1.075 | +#> | F| Forward Diff. | 78.20 | 2.380 | 0.07920 | 0.2489 | +#> |.....................| 0.04185 | -69.32 | -24.13 | 1.306 | +#> |.....................| 9.997 | -11.88 | -9.541 | -10.51 | +#> |<span style='font-weight: bold;'> 6</span>| 502.21512 | 0.9895 | -1.001 | -0.9114 | -0.8945 | +#> |.....................| -0.8456 | -0.8375 | -0.8805 | -0.8774 | +#> |.....................| -0.8791 | -0.8625 | -0.8662 | -0.8658 | +#> | U| 502.21512 | 92.14 | -5.304 | -0.9442 | -0.1066 | +#> |.....................| 2.291 | 1.178 | 0.03022 | 0.7574 | +#> |.....................| 0.8698 | 1.220 | 1.073 | 1.077 | +#> | X|<span style='font-weight: bold;'> 502.21512</span> | 92.14 | 0.004969 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.178 | 0.03022 | 0.7574 | +#> |.....................| 0.8698 | 1.220 | 1.073 | 1.077 | +#> | F| Forward Diff. | -79.18 | 2.245 | -0.2400 | 0.1569 | +#> |.....................| -0.2882 | -67.02 | -25.09 | 1.000 | +#> |.....................| 9.365 | -11.67 | -9.440 | -10.32 | +#> |<span style='font-weight: bold;'> 7</span>| 501.33312 | 1.000 | -1.001 | -0.9114 | -0.8945 | +#> |.....................| -0.8456 | -0.8270 | -0.8765 | -0.8775 | +#> |.....................| -0.8805 | -0.8607 | -0.8647 | -0.8642 | +#> | U| 501.33312 | 93.14 | -5.305 | -0.9441 | -0.1067 | +#> |.....................| 2.291 | 1.184 | 0.03028 | 0.7573 | +#> |.....................| 0.8685 | 1.222 | 1.075 | 1.079 | +#> | X|<span style='font-weight: bold;'> 501.33312</span> | 93.14 | 0.004968 | 0.2801 | 0.8988 | +#> |.....................| 9.884 | 1.184 | 0.03028 | 0.7573 | +#> |.....................| 0.8685 | 1.222 | 1.075 | 1.079 | +#> | F| Forward Diff. | 73.96 | 2.351 | 0.08380 | 0.2565 | +#> |.....................| 0.05289 | -66.42 | -23.08 | 0.9343 | +#> |.....................| 11.48 | -11.71 | -9.377 | -10.38 | +#> |<span style='font-weight: bold;'> 8</span>| 500.50460 | 0.9897 | -1.002 | -0.9114 | -0.8946 | +#> |.....................| -0.8456 | -0.8163 | -0.8728 | -0.8777 | +#> |.....................| -0.8824 | -0.8588 | -0.8632 | -0.8625 | +#> | U| 500.5046 | 92.16 | -5.305 | -0.9442 | -0.1067 | +#> |.....................| 2.291 | 1.190 | 0.03034 | 0.7572 | +#> |.....................| 0.8669 | 1.224 | 1.077 | 1.081 | +#> | X|<span style='font-weight: bold;'> 500.5046</span> | 92.16 | 0.004966 | 0.2801 | 0.8988 | +#> |.....................| 9.884 | 1.190 | 0.03034 | 0.7572 | +#> |.....................| 0.8669 | 1.224 | 1.077 | 1.081 | +#> | F| Forward Diff. | -76.85 | 2.219 | -0.2273 | 0.1675 | +#> |.....................| -0.2752 | -63.09 | -23.56 | 1.068 | +#> |.....................| 8.794 | -11.52 | -9.279 | -10.19 | +#> |<span style='font-weight: bold;'> 9</span>| 499.65692 | 1.000 | -1.002 | -0.9113 | -0.8946 | +#> |.....................| -0.8456 | -0.8056 | -0.8689 | -0.8779 | +#> |.....................| -0.8839 | -0.8568 | -0.8617 | -0.8608 | +#> | U| 499.65692 | 93.14 | -5.306 | -0.9441 | -0.1067 | +#> |.....................| 2.291 | 1.196 | 0.03040 | 0.7570 | +#> |.....................| 0.8655 | 1.226 | 1.078 | 1.082 | +#> | X|<span style='font-weight: bold;'> 499.65692</span> | 93.14 | 0.004964 | 0.2801 | 0.8988 | +#> |.....................| 9.885 | 1.196 | 0.03040 | 0.7570 | +#> |.....................| 0.8655 | 1.226 | 1.078 | 1.082 | +#> | F| Forward Diff. | 72.32 | 2.320 | 0.09176 | 0.2615 | +#> |.....................| 0.06934 | -62.36 | -21.54 | 1.140 | +#> |.....................| 9.404 | -11.56 | -9.216 | -10.24 | +#> |<span style='font-weight: bold;'> 10</span>| 498.81870 | 0.9902 | -1.003 | -0.9114 | -0.8946 | +#> |.....................| -0.8456 | -0.7946 | -0.8650 | -0.8781 | +#> |.....................| -0.8856 | -0.8548 | -0.8600 | -0.8589 | +#> | U| 498.8187 | 92.21 | -5.306 | -0.9441 | -0.1068 | +#> |.....................| 2.291 | 1.203 | 0.03045 | 0.7569 | +#> |.....................| 0.8641 | 1.229 | 1.080 | 1.084 | +#> | X|<span style='font-weight: bold;'> 498.8187</span> | 92.21 | 0.004962 | 0.2801 | 0.8987 | +#> |.....................| 9.885 | 1.203 | 0.03045 | 0.7569 | +#> |.....................| 0.8641 | 1.229 | 1.080 | 1.084 | +#> | F| Forward Diff. | -70.56 | 2.198 | -0.2057 | 0.1798 | +#> |.....................| -0.2468 | -59.74 | -22.28 | 0.8150 | +#> |.....................| 7.180 | -11.33 | -9.109 | -10.05 | +#> |<span style='font-weight: bold;'> 11</span>| 497.99655 | 1.000 | -1.003 | -0.9113 | -0.8947 | +#> |.....................| -0.8455 | -0.7835 | -0.8609 | -0.8782 | +#> |.....................| -0.8869 | -0.8527 | -0.8583 | -0.8571 | +#> | U| 497.99655 | 93.13 | -5.306 | -0.9441 | -0.1068 | +#> |.....................| 2.291 | 1.209 | 0.03052 | 0.7568 | +#> |.....................| 0.8629 | 1.231 | 1.082 | 1.086 | +#> | X|<span style='font-weight: bold;'> 497.99655</span> | 93.13 | 0.004960 | 0.2801 | 0.8987 | +#> |.....................| 9.885 | 1.209 | 0.03052 | 0.7568 | +#> |.....................| 0.8629 | 1.231 | 1.082 | 1.086 | +#> | F| Forward Diff. | 69.16 | 2.293 | 0.1087 | 0.2725 | +#> |.....................| 0.08752 | -59.63 | -20.54 | 0.7584 | +#> |.....................| 10.86 | -11.45 | -9.094 | -10.13 | +#> |<span style='font-weight: bold;'> 12</span>| 497.16410 | 0.9907 | -1.003 | -0.9113 | -0.8947 | +#> |.....................| -0.8455 | -0.7720 | -0.8569 | -0.8784 | +#> |.....................| -0.8889 | -0.8505 | -0.8566 | -0.8551 | +#> | U| 497.1641 | 92.25 | -5.307 | -0.9441 | -0.1069 | +#> |.....................| 2.291 | 1.216 | 0.03058 | 0.7566 | +#> |.....................| 0.8612 | 1.234 | 1.084 | 1.088 | +#> | X|<span style='font-weight: bold;'> 497.1641</span> | 92.25 | 0.004958 | 0.2801 | 0.8987 | +#> |.....................| 9.885 | 1.216 | 0.03058 | 0.7566 | +#> |.....................| 0.8612 | 1.234 | 1.084 | 1.088 | +#> | F| Forward Diff. | -65.09 | 2.175 | -0.1829 | 0.1920 | +#> |.....................| -0.2233 | -56.76 | -21.02 | 0.6415 | +#> |.....................| 9.983 | -11.18 | -8.930 | -9.895 | +#> |<span style='font-weight: bold;'> 13</span>| 496.40281 | 1.000 | -1.004 | -0.9113 | -0.8948 | +#> |.....................| -0.8455 | -0.7609 | -0.8528 | -0.8785 | +#> |.....................| -0.8909 | -0.8483 | -0.8548 | -0.8532 | +#> | U| 496.40281 | 93.15 | -5.307 | -0.9441 | -0.1069 | +#> |.....................| 2.291 | 1.222 | 0.03064 | 0.7566 | +#> |.....................| 0.8594 | 1.237 | 1.086 | 1.091 | +#> | X|<span style='font-weight: bold;'> 496.40281</span> | 93.15 | 0.004955 | 0.2801 | 0.8986 | +#> |.....................| 9.885 | 1.222 | 0.03064 | 0.7566 | +#> |.....................| 0.8594 | 1.237 | 1.086 | 1.091 | +#> | F| Forward Diff. | 70.05 | 2.265 | 0.1236 | 0.2851 | +#> |.....................| 0.1152 | -55.71 | -19.12 | 0.8701 | +#> |.....................| 7.394 | -11.22 | -8.890 | -9.949 | +#> |<span style='font-weight: bold;'> 14</span>| 495.59236 | 0.9910 | -1.004 | -0.9113 | -0.8948 | +#> |.....................| -0.8455 | -0.7494 | -0.8488 | -0.8787 | +#> |.....................| -0.8926 | -0.8459 | -0.8530 | -0.8511 | +#> | U| 495.59236 | 92.28 | -5.308 | -0.9441 | -0.1070 | +#> |.....................| 2.291 | 1.229 | 0.03070 | 0.7564 | +#> |.....................| 0.8580 | 1.240 | 1.088 | 1.093 | +#> | X|<span style='font-weight: bold;'> 495.59236</span> | 92.28 | 0.004953 | 0.2801 | 0.8986 | +#> |.....................| 9.885 | 1.229 | 0.03070 | 0.7564 | +#> |.....................| 0.8580 | 1.240 | 1.088 | 1.093 | +#> | F| Forward Diff. | -61.97 | 2.150 | -0.1619 | 0.2028 | +#> |.....................| -0.2007 | -53.46 | -19.76 | 0.5341 | +#> |.....................| 9.715 | -10.96 | -8.745 | -9.729 | +#> |<span style='font-weight: bold;'> 15</span>| 494.82198 | 1.000 | -1.005 | -0.9113 | -0.8949 | +#> |.....................| -0.8455 | -0.7378 | -0.8446 | -0.8788 | +#> |.....................| -0.8946 | -0.8435 | -0.8510 | -0.8489 | +#> | U| 494.82198 | 93.11 | -5.308 | -0.9441 | -0.1070 | +#> |.....................| 2.291 | 1.235 | 0.03076 | 0.7563 | +#> |.....................| 0.8562 | 1.243 | 1.090 | 1.095 | +#> | X|<span style='font-weight: bold;'> 494.82198</span> | 93.11 | 0.004951 | 0.2801 | 0.8985 | +#> |.....................| 9.886 | 1.235 | 0.03076 | 0.7563 | +#> |.....................| 0.8562 | 1.243 | 1.090 | 1.095 | +#> | F| Forward Diff. | 62.35 | 2.229 | 0.1203 | 0.2879 | +#> |.....................| 0.1180 | -52.16 | -17.88 | 0.7550 | +#> |.....................| 8.431 | -10.99 | -8.665 | -9.736 | +#> |<span style='font-weight: bold;'> 16</span>| 494.07821 | 0.9910 | -1.005 | -0.9113 | -0.8949 | +#> |.....................| -0.8455 | -0.7261 | -0.8406 | -0.8789 | +#> |.....................| -0.8966 | -0.8410 | -0.8490 | -0.8467 | +#> | U| 494.07821 | 92.28 | -5.309 | -0.9441 | -0.1071 | +#> |.....................| 2.291 | 1.242 | 0.03082 | 0.7562 | +#> |.....................| 0.8544 | 1.246 | 1.092 | 1.098 | +#> | X|<span style='font-weight: bold;'> 494.07821</span> | 92.28 | 0.004948 | 0.2801 | 0.8985 | +#> |.....................| 9.885 | 1.242 | 0.03082 | 0.7562 | +#> |.....................| 0.8544 | 1.246 | 1.092 | 1.098 | +#> | F| Forward Diff. | -62.97 | 2.119 | -0.1628 | 0.2103 | +#> |.....................| -0.1835 | -49.97 | -18.50 | 0.4855 | +#> |.....................| 6.275 | -10.75 | -8.529 | -9.546 | +#> |<span style='font-weight: bold;'> 17</span>| 493.31030 | 0.9997 | -1.006 | -0.9113 | -0.8950 | +#> |.....................| -0.8455 | -0.7143 | -0.8363 | -0.8790 | +#> |.....................| -0.8981 | -0.8383 | -0.8469 | -0.8443 | +#> | U| 493.3103 | 93.08 | -5.309 | -0.9441 | -0.1071 | +#> |.....................| 2.291 | 1.249 | 0.03089 | 0.7561 | +#> |.....................| 0.8531 | 1.249 | 1.094 | 1.100 | +#> | X|<span style='font-weight: bold;'> 493.3103</span> | 93.08 | 0.004946 | 0.2801 | 0.8984 | +#> |.....................| 9.886 | 1.249 | 0.03089 | 0.7561 | +#> |.....................| 0.8531 | 1.249 | 1.094 | 1.100 | +#> | F| Forward Diff. | 56.08 | 2.195 | 0.1067 | 0.2931 | +#> |.....................| 0.1254 | -49.64 | -16.98 | 0.3491 | +#> |.....................| 8.549 | -10.78 | -8.455 | -9.552 | +#> |<span style='font-weight: bold;'> 18</span>| 492.59068 | 0.9914 | -1.006 | -0.9113 | -0.8951 | +#> |.....................| -0.8455 | -0.7023 | -0.8321 | -0.8791 | +#> |.....................| -0.9000 | -0.8355 | -0.8448 | -0.8419 | +#> | U| 492.59068 | 92.32 | -5.310 | -0.9441 | -0.1072 | +#> |.....................| 2.291 | 1.256 | 0.03095 | 0.7561 | +#> |.....................| 0.8514 | 1.252 | 1.096 | 1.103 | +#> | X|<span style='font-weight: bold;'> 492.59068</span> | 92.32 | 0.004943 | 0.2801 | 0.8983 | +#> |.....................| 9.885 | 1.256 | 0.03095 | 0.7561 | +#> |.....................| 0.8514 | 1.252 | 1.096 | 1.103 | +#> | F| Forward Diff. | -58.13 | 2.097 | -0.1289 | 0.2246 | +#> |.....................| -0.1582 | -47.13 | -17.33 | 0.3097 | +#> |.....................| 7.738 | -10.54 | -8.304 | -9.345 | +#> |<span style='font-weight: bold;'> 19</span>| 491.88063 | 0.9998 | -1.007 | -0.9113 | -0.8951 | +#> |.....................| -0.8455 | -0.6905 | -0.8279 | -0.8791 | +#> |.....................| -0.9022 | -0.8327 | -0.8426 | -0.8394 | +#> | U| 491.88063 | 93.10 | -5.310 | -0.9441 | -0.1073 | +#> |.....................| 2.291 | 1.263 | 0.03101 | 0.7561 | +#> |.....................| 0.8496 | 1.256 | 1.099 | 1.105 | +#> | X|<span style='font-weight: bold;'> 491.88063</span> | 93.10 | 0.004940 | 0.2801 | 0.8983 | +#> |.....................| 9.886 | 1.263 | 0.03101 | 0.7561 | +#> |.....................| 0.8496 | 1.256 | 1.099 | 1.105 | +#> | F| Forward Diff. | 56.71 | 2.166 | 0.1292 | 0.3076 | +#> |.....................| 0.1542 | -45.57 | -15.60 | 0.4873 | +#> |.....................| 6.413 | -10.51 | -8.202 | -9.332 | +#> |<span style='font-weight: bold;'> 20</span>| 491.19020 | 0.9917 | -1.008 | -0.9113 | -0.8952 | +#> |.....................| -0.8455 | -0.6785 | -0.8237 | -0.8792 | +#> |.....................| -0.9039 | -0.8296 | -0.8402 | -0.8366 | +#> | U| 491.1902 | 92.34 | -5.311 | -0.9441 | -0.1074 | +#> |.....................| 2.291 | 1.270 | 0.03107 | 0.7560 | +#> |.....................| 0.8481 | 1.259 | 1.101 | 1.108 | +#> | X|<span style='font-weight: bold;'> 491.1902</span> | 92.34 | 0.004937 | 0.2801 | 0.8982 | +#> |.....................| 9.885 | 1.270 | 0.03107 | 0.7560 | +#> |.....................| 0.8481 | 1.259 | 1.101 | 1.108 | +#> | F| Forward Diff. | -55.56 | 2.070 | -0.1130 | 0.2359 | +#> |.....................| -0.1346 | -44.07 | -16.23 | 0.1008 | +#> |.....................| 7.464 | -10.26 | -8.060 | -9.125 | +#> |<span style='font-weight: bold;'> 21</span>| 490.47868 | 0.9993 | -1.008 | -0.9113 | -0.8953 | +#> |.....................| -0.8455 | -0.6665 | -0.8194 | -0.8791 | +#> |.....................| -0.9059 | -0.8264 | -0.8377 | -0.8337 | +#> | U| 490.47868 | 93.05 | -5.312 | -0.9441 | -0.1075 | +#> |.....................| 2.291 | 1.277 | 0.03114 | 0.7561 | +#> |.....................| 0.8463 | 1.263 | 1.104 | 1.111 | +#> | X|<span style='font-weight: bold;'> 490.47868</span> | 93.05 | 0.004934 | 0.2801 | 0.8981 | +#> |.....................| 9.885 | 1.277 | 0.03114 | 0.7561 | +#> |.....................| 0.8463 | 1.263 | 1.104 | 1.111 | +#> | F| Forward Diff. | 47.93 | 2.132 | 0.1269 | 0.3117 | +#> |.....................| 0.1562 | -43.27 | -14.78 | 0.06906 | +#> |.....................| 9.295 | -10.26 | -7.955 | -9.092 | +#> |<span style='font-weight: bold;'> 22</span>| 489.84765 | 0.9918 | -1.009 | -0.9114 | -0.8954 | +#> |.....................| -0.8456 | -0.6545 | -0.8153 | -0.8790 | +#> |.....................| -0.9090 | -0.8231 | -0.8352 | -0.8308 | +#> | U| 489.84765 | 92.35 | -5.312 | -0.9441 | -0.1076 | +#> |.....................| 2.291 | 1.284 | 0.03120 | 0.7562 | +#> |.....................| 0.8436 | 1.267 | 1.107 | 1.115 | +#> | X|<span style='font-weight: bold;'> 489.84765</span> | 92.35 | 0.004930 | 0.2801 | 0.8980 | +#> |.....................| 9.885 | 1.284 | 0.03120 | 0.7562 | +#> |.....................| 0.8436 | 1.267 | 1.107 | 1.115 | +#> | F| Forward Diff. | -55.71 | 2.038 | -0.1283 | 0.2328 | +#> |.....................| -0.1164 | -41.15 | -15.14 | 0.009736 | +#> |.....................| 8.505 | -10.03 | -7.805 | -8.885 | +#> |<span style='font-weight: bold;'> 23</span>| 489.17644 | 0.9994 | -1.010 | -0.9113 | -0.8955 | +#> |.....................| -0.8456 | -0.6429 | -0.8112 | -0.8788 | +#> |.....................| -0.9126 | -0.8197 | -0.8325 | -0.8278 | +#> | U| 489.17644 | 93.06 | -5.313 | -0.9441 | -0.1077 | +#> |.....................| 2.291 | 1.290 | 0.03126 | 0.7563 | +#> |.....................| 0.8405 | 1.272 | 1.109 | 1.118 | +#> | X|<span style='font-weight: bold;'> 489.17644</span> | 93.06 | 0.004927 | 0.2801 | 0.8979 | +#> |.....................| 9.885 | 1.290 | 0.03126 | 0.7563 | +#> |.....................| 0.8405 | 1.272 | 1.109 | 1.118 | +#> | F| Forward Diff. | 46.87 | 2.093 | 0.1493 | 0.3243 | +#> |.....................| 0.1838 | -40.03 | -13.57 | 0.1411 | +#> |.....................| 5.593 | -9.957 | -7.669 | -8.831 | +#> |<span style='font-weight: bold;'> 24</span>| 488.58015 | 0.9920 | -1.011 | -0.9114 | -0.8957 | +#> |.....................| -0.8457 | -0.6309 | -0.8071 | -0.8787 | +#> |.....................| -0.9147 | -0.8159 | -0.8297 | -0.8244 | +#> | U| 488.58015 | 92.37 | -5.314 | -0.9442 | -0.1078 | +#> |.....................| 2.291 | 1.297 | 0.03132 | 0.7564 | +#> |.....................| 0.8386 | 1.276 | 1.112 | 1.121 | +#> | X|<span style='font-weight: bold;'> 488.58015</span> | 92.37 | 0.004923 | 0.2801 | 0.8978 | +#> |.....................| 9.884 | 1.297 | 0.03132 | 0.7564 | +#> |.....................| 0.8386 | 1.276 | 1.112 | 1.121 | +#> | F| Forward Diff. | -53.50 | 2.005 | -0.1078 | 0.2446 | +#> |.....................| -0.09190 | -37.89 | -13.87 | 0.05672 | +#> |.....................| 4.909 | -9.713 | -7.511 | -8.606 | +#> |<span style='font-weight: bold;'> 25</span>| 487.93833 | 0.9991 | -1.011 | -0.9114 | -0.8958 | +#> |.....................| -0.8457 | -0.6190 | -0.8030 | -0.8785 | +#> |.....................| -0.9153 | -0.8117 | -0.8266 | -0.8207 | +#> | U| 487.93833 | 93.04 | -5.315 | -0.9442 | -0.1080 | +#> |.....................| 2.291 | 1.304 | 0.03139 | 0.7566 | +#> |.....................| 0.8381 | 1.281 | 1.116 | 1.125 | +#> | X|<span style='font-weight: bold;'> 487.93833</span> | 93.04 | 0.004918 | 0.2801 | 0.8977 | +#> |.....................| 9.883 | 1.304 | 0.03139 | 0.7566 | +#> |.....................| 0.8381 | 1.281 | 1.116 | 1.125 | +#> | F| Forward Diff. | 41.92 | 2.065 | 0.1569 | 0.3320 | +#> |.....................| 0.1961 | -37.34 | -12.63 | 0.01172 | +#> |.....................| 5.301 | -9.646 | -7.360 | -8.530 | +#> |<span style='font-weight: bold;'> 26</span>| 487.37063 | 0.9925 | -1.012 | -0.9115 | -0.8960 | +#> |.....................| -0.8458 | -0.6069 | -0.7990 | -0.8783 | +#> |.....................| -0.9161 | -0.8073 | -0.8233 | -0.8168 | +#> | U| 487.37063 | 92.42 | -5.316 | -0.9443 | -0.1081 | +#> |.....................| 2.291 | 1.311 | 0.03145 | 0.7567 | +#> |.....................| 0.8374 | 1.287 | 1.119 | 1.130 | +#> | X|<span style='font-weight: bold;'> 487.37063</span> | 92.42 | 0.004913 | 0.2800 | 0.8975 | +#> |.....................| 9.882 | 1.311 | 0.03145 | 0.7567 | +#> |.....................| 0.8374 | 1.287 | 1.119 | 1.130 | +#> | F| Forward Diff. | -47.84 | 1.989 | -0.08553 | 0.2559 | +#> |.....................| -0.06263 | -35.59 | -12.91 | -0.09336 | +#> |.....................| 8.020 | -9.356 | -7.180 | -8.291 | +#> |<span style='font-weight: bold;'> 27</span>| 486.76802 | 0.9991 | -1.014 | -0.9115 | -0.8962 | +#> |.....................| -0.8459 | -0.5954 | -0.7952 | -0.8779 | +#> |.....................| -0.9197 | -0.8027 | -0.8200 | -0.8127 | +#> | U| 486.76802 | 93.03 | -5.317 | -0.9443 | -0.1083 | +#> |.....................| 2.291 | 1.318 | 0.03150 | 0.7570 | +#> |.....................| 0.8342 | 1.292 | 1.123 | 1.134 | +#> | X|<span style='font-weight: bold;'> 486.76802</span> | 93.03 | 0.004908 | 0.2800 | 0.8973 | +#> |.....................| 9.881 | 1.318 | 0.03150 | 0.7570 | +#> |.....................| 0.8342 | 1.292 | 1.123 | 1.134 | +#> | F| Forward Diff. | 39.28 | 2.032 | 0.1697 | 0.3409 | +#> |.....................| 0.2161 | -34.26 | -11.60 | -0.04206 | +#> |.....................| 6.414 | -9.258 | -7.014 | -8.183 | +#> |<span style='font-weight: bold;'> 28</span>| 486.25961 | 0.9924 | -1.015 | -0.9116 | -0.8964 | +#> |.....................| -0.8461 | -0.5843 | -0.7916 | -0.8775 | +#> |.....................| -0.9242 | -0.7980 | -0.8166 | -0.8086 | +#> | U| 486.25961 | 92.41 | -5.318 | -0.9444 | -0.1086 | +#> |.....................| 2.290 | 1.324 | 0.03156 | 0.7573 | +#> |.....................| 0.8303 | 1.298 | 1.126 | 1.138 | +#> | X|<span style='font-weight: bold;'> 486.25961</span> | 92.41 | 0.004902 | 0.2800 | 0.8971 | +#> |.....................| 9.880 | 1.324 | 0.03156 | 0.7573 | +#> |.....................| 0.8303 | 1.298 | 1.126 | 1.138 | +#> | F| Forward Diff. | -50.63 | 1.945 | -0.07307 | 0.2626 | +#> |.....................| -0.04930 | -33.11 | -12.03 | -0.1686 | +#> |.....................| 7.510 | -8.984 | -6.802 | -7.934 | +#> |<span style='font-weight: bold;'> 29</span>| 485.66844 | 0.9985 | -1.016 | -0.9117 | -0.8967 | +#> |.....................| -0.8462 | -0.5738 | -0.7881 | -0.8769 | +#> |.....................| -0.9293 | -0.7927 | -0.8129 | -0.8039 | +#> | U| 485.66844 | 92.98 | -5.319 | -0.9445 | -0.1089 | +#> |.....................| 2.290 | 1.331 | 0.03161 | 0.7578 | +#> |.....................| 0.8259 | 1.304 | 1.130 | 1.143 | +#> | X|<span style='font-weight: bold;'> 485.66844</span> | 92.98 | 0.004895 | 0.2800 | 0.8969 | +#> |.....................| 9.878 | 1.331 | 0.03161 | 0.7578 | +#> |.....................| 0.8259 | 1.304 | 1.130 | 1.143 | +#> | F| Forward Diff. | 30.24 | 1.977 | 0.1746 | 0.3455 | +#> |.....................| 0.2218 | -32.22 | -10.87 | -0.2249 | +#> |.....................| 4.336 | -8.820 | -6.615 | -7.812 | +#> |<span style='font-weight: bold;'> 30</span>| 485.23968 | 0.9921 | -1.017 | -0.9119 | -0.8970 | +#> |.....................| -0.8465 | -0.5622 | -0.7845 | -0.8762 | +#> |.....................| -0.9314 | -0.7876 | -0.8094 | -0.7994 | +#> | U| 485.23968 | 92.38 | -5.321 | -0.9447 | -0.1091 | +#> |.....................| 2.290 | 1.337 | 0.03166 | 0.7583 | +#> |.....................| 0.8240 | 1.310 | 1.134 | 1.148 | +#> | X|<span style='font-weight: bold;'> 485.23968</span> | 92.38 | 0.004889 | 0.2800 | 0.8966 | +#> |.....................| 9.876 | 1.337 | 0.03166 | 0.7583 | +#> |.....................| 0.8240 | 1.310 | 1.134 | 1.148 | +#> | F| Forward Diff. | -56.59 | 1.902 | -0.07536 | 0.2678 | +#> |.....................| -0.04797 | -30.46 | -11.14 | -0.09043 | +#> |.....................| 3.742 | -8.533 | -6.412 | -7.541 | +#> |<span style='font-weight: bold;'> 31</span>| 484.69662 | 0.9984 | -1.019 | -0.9121 | -0.8974 | +#> |.....................| -0.8467 | -0.5517 | -0.7813 | -0.8754 | +#> |.....................| -0.9289 | -0.7816 | -0.8053 | -0.7941 | +#> | U| 484.69662 | 92.97 | -5.322 | -0.9448 | -0.1095 | +#> |.....................| 2.290 | 1.343 | 0.03171 | 0.7589 | +#> |.....................| 0.8262 | 1.318 | 1.138 | 1.154 | +#> | X|<span style='font-weight: bold;'> 484.69662</span> | 92.97 | 0.004881 | 0.2799 | 0.8963 | +#> |.....................| 9.873 | 1.343 | 0.03171 | 0.7589 | +#> |.....................| 0.8262 | 1.318 | 1.138 | 1.154 | +#> | F| Forward Diff. | 27.47 | 1.960 | 0.1737 | 0.3487 | +#> |.....................| 0.2320 | -29.84 | -10.04 | -0.2714 | +#> |.....................| 5.731 | -8.337 | -6.228 | -7.371 | +#> |<span style='font-weight: bold;'> 32</span>| 484.27605 | 0.9928 | -1.021 | -0.9123 | -0.8978 | +#> |.....................| -0.8471 | -0.5404 | -0.7779 | -0.8746 | +#> |.....................| -0.9302 | -0.7757 | -0.8014 | -0.7889 | +#> | U| 484.27605 | 92.45 | -5.324 | -0.9451 | -0.1099 | +#> |.....................| 2.289 | 1.350 | 0.03176 | 0.7595 | +#> |.....................| 0.8251 | 1.325 | 1.143 | 1.159 | +#> | X|<span style='font-weight: bold;'> 484.27605</span> | 92.45 | 0.004872 | 0.2799 | 0.8959 | +#> |.....................| 9.870 | 1.350 | 0.03176 | 0.7595 | +#> |.....................| 0.8251 | 1.325 | 1.143 | 1.159 | +#> | F| Forward Diff. | -48.28 | 1.894 | -0.05804 | 0.2769 | +#> |.....................| -0.01457 | -28.21 | -10.24 | -0.1977 | +#> |.....................| 5.253 | -8.027 | -5.998 | -7.085 | +#> |<span style='font-weight: bold;'> 33</span>| 483.77365 | 0.9986 | -1.023 | -0.9126 | -0.8983 | +#> |.....................| -0.8475 | -0.5309 | -0.7752 | -0.8734 | +#> |.....................| -0.9343 | -0.7690 | -0.7970 | -0.7831 | +#> | U| 483.77365 | 92.99 | -5.326 | -0.9453 | -0.1104 | +#> |.....................| 2.289 | 1.355 | 0.03180 | 0.7604 | +#> |.....................| 0.8215 | 1.333 | 1.147 | 1.166 | +#> | X|<span style='font-weight: bold;'> 483.77365</span> | 92.99 | 0.004861 | 0.2798 | 0.8954 | +#> |.....................| 9.866 | 1.355 | 0.03180 | 0.7604 | +#> |.....................| 0.8215 | 1.333 | 1.147 | 1.166 | +#> | F| Forward Diff. | 28.59 | 1.923 | 0.1952 | 0.3548 | +#> |.....................| 0.2608 | -27.76 | -9.333 | -0.3645 | +#> |.....................| 3.958 | -7.814 | -5.777 | -6.894 | +#> |<span style='font-weight: bold;'> 34</span>| 483.37086 | 0.9934 | -1.025 | -0.9129 | -0.8989 | +#> |.....................| -0.8480 | -0.5203 | -0.7721 | -0.8720 | +#> |.....................| -0.9349 | -0.7624 | -0.7928 | -0.7774 | +#> | U| 483.37086 | 92.51 | -5.329 | -0.9456 | -0.1110 | +#> |.....................| 2.289 | 1.362 | 0.03185 | 0.7615 | +#> |.....................| 0.8209 | 1.341 | 1.152 | 1.172 | +#> | X|<span style='font-weight: bold;'> 483.37086</span> | 92.51 | 0.004850 | 0.2798 | 0.8949 | +#> |.....................| 9.861 | 1.362 | 0.03185 | 0.7615 | +#> |.....................| 0.8209 | 1.341 | 1.152 | 1.172 | +#> | F| Forward Diff. | -41.16 | 1.862 | -0.03265 | 0.2828 | +#> |.....................| 0.01951 | -26.43 | -9.488 | -0.2833 | +#> |.....................| 3.545 | -7.469 | -5.528 | -6.584 | +#> |<span style='font-weight: bold;'> 35</span>| 482.96272 | 0.9987 | -1.028 | -0.9132 | -0.8995 | +#> |.....................| -0.8485 | -0.5103 | -0.7694 | -0.8702 | +#> |.....................| -0.9315 | -0.7558 | -0.7888 | -0.7716 | +#> | U| 482.96272 | 92.99 | -5.332 | -0.9459 | -0.1117 | +#> |.....................| 2.288 | 1.367 | 0.03189 | 0.7629 | +#> |.....................| 0.8240 | 1.349 | 1.156 | 1.178 | +#> | X|<span style='font-weight: bold;'> 482.96272</span> | 92.99 | 0.004836 | 0.2797 | 0.8943 | +#> |.....................| 9.856 | 1.367 | 0.03189 | 0.7629 | +#> |.....................| 0.8240 | 1.349 | 1.156 | 1.178 | +#> | F| Forward Diff. | 28.21 | 1.908 | 0.1917 | 0.3504 | +#> |.....................| 0.2712 | -25.82 | -8.599 | -0.3385 | +#> |.....................| 4.050 | -7.278 | -5.334 | -6.398 | +#> |<span style='font-weight: bold;'> 36</span>| 482.60011 | 0.9939 | -1.032 | -0.9136 | -0.9003 | +#> |.....................| -0.8492 | -0.4998 | -0.7669 | -0.8684 | +#> |.....................| -0.9296 | -0.7490 | -0.7849 | -0.7659 | +#> | U| 482.60011 | 92.55 | -5.335 | -0.9462 | -0.1124 | +#> |.....................| 2.287 | 1.373 | 0.03193 | 0.7642 | +#> |.....................| 0.8256 | 1.357 | 1.160 | 1.184 | +#> | X|<span style='font-weight: bold;'> 482.60011</span> | 92.55 | 0.004820 | 0.2796 | 0.8937 | +#> |.....................| 9.849 | 1.373 | 0.03193 | 0.7642 | +#> |.....................| 0.8256 | 1.357 | 1.160 | 1.184 | +#> | F| Forward Diff. | -36.31 | 1.855 | -0.03781 | 0.2769 | +#> |.....................| 0.03076 | -24.99 | -8.890 | -0.4685 | +#> |.....................| 7.176 | -6.892 | -5.117 | -6.081 | +#> |<span style='font-weight: bold;'> 37</span>| 482.21198 | 0.9982 | -1.035 | -0.9138 | -0.9009 | +#> |.....................| -0.8497 | -0.4920 | -0.7653 | -0.8661 | +#> |.....................| -0.9399 | -0.7441 | -0.7821 | -0.7617 | +#> | U| 482.21198 | 92.95 | -5.338 | -0.9465 | -0.1130 | +#> |.....................| 2.287 | 1.378 | 0.03195 | 0.7659 | +#> |.....................| 0.8166 | 1.363 | 1.163 | 1.189 | +#> | X|<span style='font-weight: bold;'> 482.21198</span> | 92.95 | 0.004805 | 0.2796 | 0.8931 | +#> |.....................| 9.844 | 1.378 | 0.03195 | 0.7659 | +#> |.....................| 0.8166 | 1.363 | 1.163 | 1.189 | +#> | F| Forward Diff. | 20.01 | 1.850 | 0.1852 | 0.3312 | +#> |.....................| 0.2616 | -23.95 | -7.997 | -0.3393 | +#> |.....................| 4.985 | -6.711 | -4.923 | -5.940 | +#> |<span style='font-weight: bold;'> 38</span>| 481.96846 | 0.9924 | -1.037 | -0.9141 | -0.9014 | +#> |.....................| -0.8503 | -0.4828 | -0.7630 | -0.8646 | +#> |.....................| -0.9490 | -0.7399 | -0.7795 | -0.7579 | +#> | U| 481.96846 | 92.41 | -5.341 | -0.9468 | -0.1136 | +#> |.....................| 2.286 | 1.383 | 0.03199 | 0.7671 | +#> |.....................| 0.8087 | 1.368 | 1.166 | 1.193 | +#> | X|<span style='font-weight: bold;'> 481.96846</span> | 92.41 | 0.004793 | 0.2795 | 0.8927 | +#> |.....................| 9.838 | 1.383 | 0.03199 | 0.7671 | +#> |.....................| 0.8087 | 1.368 | 1.166 | 1.193 | +#> | F| Forward Diff. | -59.26 | 1.761 | -0.08116 | 0.2547 | +#> |.....................| -0.02692 | -22.78 | -8.366 | -0.2344 | +#> |.....................| 4.087 | -6.524 | -4.792 | -5.748 | +#> |<span style='font-weight: bold;'> 39</span>| 481.52549 | 0.9980 | -1.042 | -0.9148 | -0.9024 | +#> |.....................| -0.8514 | -0.4755 | -0.7621 | -0.8625 | +#> |.....................| -0.9558 | -0.7333 | -0.7761 | -0.7520 | +#> | U| 481.52549 | 92.93 | -5.345 | -0.9474 | -0.1146 | +#> |.....................| 2.285 | 1.388 | 0.03200 | 0.7686 | +#> |.....................| 0.8027 | 1.376 | 1.170 | 1.199 | +#> | X|<span style='font-weight: bold;'> 481.52549</span> | 92.93 | 0.004770 | 0.2794 | 0.8917 | +#> |.....................| 9.827 | 1.388 | 0.03200 | 0.7686 | +#> |.....................| 0.8027 | 1.376 | 1.170 | 1.199 | +#> | F| Forward Diff. | 14.56 | 1.771 | 0.1903 | 0.3270 | +#> |.....................| 0.2641 | -22.44 | -7.508 | -0.4496 | +#> |.....................| 2.566 | -6.373 | -4.622 | -5.584 | +#> |<span style='font-weight: bold;'> 40</span>| 481.26396 | 0.9932 | -1.045 | -0.9155 | -0.9032 | +#> |.....................| -0.8523 | -0.4642 | -0.7593 | -0.8605 | +#> |.....................| -0.9543 | -0.7272 | -0.7727 | -0.7469 | +#> | U| 481.26396 | 92.49 | -5.349 | -0.9480 | -0.1154 | +#> |.....................| 2.284 | 1.394 | 0.03204 | 0.7702 | +#> |.....................| 0.8040 | 1.384 | 1.173 | 1.205 | +#> | X|<span style='font-weight: bold;'> 481.26396</span> | 92.49 | 0.004753 | 0.2793 | 0.8910 | +#> |.....................| 9.818 | 1.394 | 0.03204 | 0.7702 | +#> |.....................| 0.8040 | 1.384 | 1.173 | 1.205 | +#> | F| Forward Diff. | -49.84 | 1.721 | -0.06329 | 0.2500 | +#> |.....................| 0.003387 | -21.58 | -7.808 | -0.4470 | +#> |.....................| 3.805 | -6.020 | -4.412 | -5.292 | +#> |<span style='font-weight: bold;'> 41</span>| 480.91101 | 0.9981 | -1.051 | -0.9163 | -0.9044 | +#> |.....................| -0.8537 | -0.4552 | -0.7584 | -0.8559 | +#> |.....................| -0.9510 | -0.7207 | -0.7698 | -0.7416 | +#> | U| 480.91101 | 92.94 | -5.355 | -0.9488 | -0.1166 | +#> |.....................| 2.283 | 1.399 | 0.03206 | 0.7737 | +#> |.....................| 0.8069 | 1.392 | 1.176 | 1.210 | +#> | X|<span style='font-weight: bold;'> 480.91101</span> | 92.94 | 0.004727 | 0.2791 | 0.8900 | +#> |.....................| 9.804 | 1.399 | 0.03206 | 0.7737 | +#> |.....................| 0.8069 | 1.392 | 1.176 | 1.210 | +#> | F| Forward Diff. | 16.05 | 1.751 | 0.1631 | 0.3020 | +#> |.....................| 0.2540 | -20.90 | -6.928 | -0.3893 | +#> |.....................| 4.288 | -5.817 | -4.263 | -5.144 | +#> |<span style='font-weight: bold;'> 42</span>| 480.64341 | 0.9941 | -1.056 | -0.9169 | -0.9053 | +#> |.....................| -0.8549 | -0.4456 | -0.7571 | -0.8527 | +#> |.....................| -0.9585 | -0.7158 | -0.7673 | -0.7373 | +#> | U| 480.64341 | 92.57 | -5.360 | -0.9493 | -0.1175 | +#> |.....................| 2.282 | 1.405 | 0.03208 | 0.7761 | +#> |.....................| 0.8004 | 1.398 | 1.179 | 1.215 | +#> | X|<span style='font-weight: bold;'> 480.64341</span> | 92.57 | 0.004703 | 0.2790 | 0.8892 | +#> |.....................| 9.793 | 1.405 | 0.03208 | 0.7761 | +#> |.....................| 0.8004 | 1.398 | 1.179 | 1.215 | +#> | F| Forward Diff. | -40.16 | 1.680 | -0.01378 | 0.2424 | +#> |.....................| 0.03021 | -20.27 | -7.228 | -0.4675 | +#> |.....................| 4.140 | -5.523 | -4.100 | -4.903 | +#> |<span style='font-weight: bold;'> 43</span>| 480.34062 | 0.9982 | -1.062 | -0.9177 | -0.9064 | +#> |.....................| -0.8561 | -0.4387 | -0.7572 | -0.8486 | +#> |.....................| -0.9687 | -0.7122 | -0.7655 | -0.7338 | +#> | U| 480.34062 | 92.95 | -5.365 | -0.9501 | -0.1185 | +#> |.....................| 2.280 | 1.409 | 0.03207 | 0.7792 | +#> |.....................| 0.7914 | 1.402 | 1.181 | 1.219 | +#> | X|<span style='font-weight: bold;'> 480.34062</span> | 92.95 | 0.004675 | 0.2789 | 0.8883 | +#> |.....................| 9.781 | 1.409 | 0.03207 | 0.7792 | +#> |.....................| 0.7914 | 1.402 | 1.181 | 1.219 | +#> |<span style='font-weight: bold;'> 44</span>| 480.11354 | 0.9982 | -1.069 | -0.9186 | -0.9075 | +#> |.....................| -0.8576 | -0.4327 | -0.7582 | -0.8437 | +#> |.....................| -0.9807 | -0.7086 | -0.7639 | -0.7301 | +#> | U| 480.11354 | 92.95 | -5.372 | -0.9510 | -0.1197 | +#> |.....................| 2.279 | 1.412 | 0.03206 | 0.7829 | +#> |.....................| 0.7810 | 1.406 | 1.183 | 1.223 | +#> | X|<span style='font-weight: bold;'> 480.11354</span> | 92.95 | 0.004643 | 0.2787 | 0.8872 | +#> |.....................| 9.767 | 1.412 | 0.03206 | 0.7829 | +#> |.....................| 0.7810 | 1.406 | 1.183 | 1.223 | +#> |<span style='font-weight: bold;'> 45</span>| 479.24256 | 0.9982 | -1.100 | -0.9228 | -0.9129 | +#> |.....................| -0.8642 | -0.4061 | -0.7626 | -0.8221 | +#> |.....................| -1.034 | -0.6924 | -0.7565 | -0.7138 | +#> | U| 479.24256 | 92.95 | -5.404 | -0.9550 | -0.1250 | +#> |.....................| 2.272 | 1.428 | 0.03199 | 0.7993 | +#> |.....................| 0.7344 | 1.426 | 1.191 | 1.240 | +#> | X|<span style='font-weight: bold;'> 479.24256</span> | 92.95 | 0.004500 | 0.2779 | 0.8825 | +#> |.....................| 9.702 | 1.428 | 0.03199 | 0.7993 | +#> |.....................| 0.7344 | 1.426 | 1.191 | 1.240 | +#> |<span style='font-weight: bold;'> 46</span>| 477.60836 | 1.003 | -1.228 | -0.9400 | -0.9346 | +#> |.....................| -0.8912 | -0.2901 | -0.7784 | -0.7332 | +#> |.....................| -1.206 | -0.6258 | -0.7257 | -0.6466 | +#> | U| 477.60836 | 93.40 | -5.531 | -0.9712 | -0.1467 | +#> |.....................| 2.245 | 1.495 | 0.03176 | 0.8667 | +#> |.....................| 0.5843 | 1.507 | 1.224 | 1.312 | +#> | X|<span style='font-weight: bold;'> 477.60836</span> | 93.40 | 0.003961 | 0.2746 | 0.8635 | +#> |.....................| 9.444 | 1.495 | 0.03176 | 0.8667 | +#> |.....................| 0.5843 | 1.507 | 1.224 | 1.312 | +#> | F| Forward Diff. | 50.81 | 0.8332 | 0.6263 | 0.04339 | +#> |.....................| 0.5543 | -9.740 | -2.969 | 0.1978 | +#> |.....................| -10.28 | -2.761 | -1.505 | -1.849 | +#> |<span style='font-weight: bold;'> 47</span>| 476.77966 | 1.006 | -1.398 | -0.9862 | -0.9532 | +#> |.....................| -0.9413 | -0.07616 | -0.7687 | -0.6374 | +#> |.....................| -0.9573 | -0.5395 | -0.7103 | -0.5930 | +#> | U| 476.77966 | 93.71 | -5.701 | -1.015 | -0.1654 | +#> |.....................| 2.195 | 1.619 | 0.03190 | 0.9393 | +#> |.....................| 0.8014 | 1.612 | 1.240 | 1.369 | +#> | X|<span style='font-weight: bold;'> 476.77966</span> | 93.71 | 0.003342 | 0.2660 | 0.8476 | +#> |.....................| 8.982 | 1.619 | 0.03190 | 0.9393 | +#> |.....................| 0.8014 | 1.612 | 1.240 | 1.369 | +#> | F| Forward Diff. | 100.8 | 0.5681 | -2.148 | -0.2910 | +#> |.....................| -0.6169 | 0.8458 | 0.8586 | 0.3650 | +#> |.....................| 3.820 | 1.443 | -0.7364 | 0.2440 | +#> |<span style='font-weight: bold;'> 48</span>| 478.65806 | 0.9952 | -1.512 | -0.6913 | -0.9031 | +#> |.....................| -0.8317 | -0.01918 | -0.7109 | -0.6555 | +#> |.....................| -0.9083 | -0.7021 | -0.6121 | -0.6260 | +#> | U| 478.65806 | 92.67 | -5.815 | -0.7363 | -0.1152 | +#> |.....................| 2.305 | 1.652 | 0.03277 | 0.9255 | +#> |.....................| 0.8442 | 1.414 | 1.345 | 1.334 | +#> | X|<span style='font-weight: bold;'> 478.65806</span> | 92.67 | 0.002982 | 0.3238 | 0.8912 | +#> |.....................| 10.02 | 1.652 | 0.03277 | 0.9255 | +#> |.....................| 0.8442 | 1.414 | 1.345 | 1.334 | +#> |<span style='font-weight: bold;'> 49</span>| 476.83500 | 0.9931 | -1.426 | -0.9118 | -0.9406 | +#> |.....................| -0.9137 | -0.06192 | -0.7543 | -0.6420 | +#> |.....................| -0.9454 | -0.5805 | -0.6855 | -0.6013 | +#> | U| 476.835 | 92.48 | -5.730 | -0.9445 | -0.1527 | +#> |.....................| 2.223 | 1.627 | 0.03212 | 0.9358 | +#> |.....................| 0.8118 | 1.562 | 1.267 | 1.361 | +#> | X|<span style='font-weight: bold;'> 476.835</span> | 92.48 | 0.003247 | 0.2800 | 0.8584 | +#> |.....................| 9.234 | 1.627 | 0.03212 | 0.9358 | +#> |.....................| 0.8118 | 1.562 | 1.267 | 1.361 | +#> |<span style='font-weight: bold;'> 50</span>| 476.86775 | 0.9928 | -1.411 | -0.9513 | -0.9473 | +#> |.....................| -0.9284 | -0.06958 | -0.7620 | -0.6396 | +#> |.....................| -0.9520 | -0.5587 | -0.6987 | -0.5969 | +#> | U| 476.86775 | 92.44 | -5.715 | -0.9819 | -0.1595 | +#> |.....................| 2.208 | 1.623 | 0.03200 | 0.9376 | +#> |.....................| 0.8060 | 1.588 | 1.252 | 1.365 | +#> | X|<span style='font-weight: bold;'> 476.86775</span> | 92.44 | 0.003297 | 0.2725 | 0.8526 | +#> |.....................| 9.099 | 1.623 | 0.03200 | 0.9376 | +#> |.....................| 0.8060 | 1.588 | 1.252 | 1.365 | +#> |<span style='font-weight: bold;'> 51</span>| 476.94436 | 0.9926 | -1.403 | -0.9724 | -0.9509 | +#> |.....................| -0.9362 | -0.07366 | -0.7662 | -0.6383 | +#> |.....................| -0.9556 | -0.5471 | -0.7057 | -0.5945 | +#> | U| 476.94436 | 92.42 | -5.706 | -1.002 | -0.1630 | +#> |.....................| 2.200 | 1.621 | 0.03194 | 0.9386 | +#> |.....................| 0.8029 | 1.602 | 1.245 | 1.368 | +#> | X|<span style='font-weight: bold;'> 476.94436</span> | 92.42 | 0.003324 | 0.2686 | 0.8496 | +#> |.....................| 9.028 | 1.621 | 0.03194 | 0.9386 | +#> |.....................| 0.8029 | 1.602 | 1.245 | 1.368 | +#> |<span style='font-weight: bold;'> 52</span>| 476.64580 | 0.9959 | -1.398 | -0.9860 | -0.9532 | +#> |.....................| -0.9413 | -0.07625 | -0.7688 | -0.6374 | +#> |.....................| -0.9577 | -0.5396 | -0.7102 | -0.5930 | +#> | U| 476.6458 | 92.74 | -5.701 | -1.015 | -0.1653 | +#> |.....................| 2.195 | 1.619 | 0.03190 | 0.9392 | +#> |.....................| 0.8011 | 1.611 | 1.240 | 1.369 | +#> | X|<span style='font-weight: bold;'> 476.6458</span> | 92.74 | 0.003342 | 0.2661 | 0.8476 | +#> |.....................| 8.983 | 1.619 | 0.03190 | 0.9392 | +#> |.....................| 0.8011 | 1.611 | 1.240 | 1.369 | +#> | F| Forward Diff. | -76.03 | 0.4748 | -3.401 | -0.5335 | +#> |.....................| -1.858 | 1.570 | -0.1336 | 0.2990 | +#> |.....................| 3.107 | 1.921 | -0.6340 | 0.6252 | +#> |<span style='font-weight: bold;'> 53</span>| 476.45477 | 1.000 | -1.400 | -0.9787 | -0.9521 | +#> |.....................| -0.9380 | -0.07508 | -0.7683 | -0.6381 | +#> |.....................| -0.9567 | -0.5427 | -0.7079 | -0.5935 | +#> | U| 476.45477 | 93.14 | -5.704 | -1.008 | -0.1642 | +#> |.....................| 2.199 | 1.620 | 0.03191 | 0.9387 | +#> |.....................| 0.8019 | 1.608 | 1.243 | 1.369 | +#> | X|<span style='font-weight: bold;'> 476.45477</span> | 93.14 | 0.003334 | 0.2674 | 0.8486 | +#> |.....................| 9.012 | 1.620 | 0.03191 | 0.9387 | +#> |.....................| 0.8019 | 1.608 | 1.243 | 1.369 | +#> | F| Forward Diff. | 0.2803 | 0.4975 | -2.426 | -0.4122 | +#> |.....................| -1.237 | 1.245 | 0.3711 | 0.1250 | +#> |.....................| 4.601 | 1.480 | -0.5654 | 0.4236 | +#> |<span style='font-weight: bold;'> 54</span>| 476.38303 | 0.9998 | -1.401 | -0.9743 | -0.9513 | +#> |.....................| -0.9358 | -0.07732 | -0.7690 | -0.6383 | +#> |.....................| -0.9650 | -0.5454 | -0.7069 | -0.5943 | +#> | U| 476.38303 | 93.10 | -5.704 | -1.004 | -0.1635 | +#> |.....................| 2.201 | 1.618 | 0.03190 | 0.9385 | +#> |.....................| 0.7947 | 1.604 | 1.244 | 1.368 | +#> | X|<span style='font-weight: bold;'> 476.38303</span> | 93.10 | 0.003331 | 0.2682 | 0.8492 | +#> |.....................| 9.032 | 1.618 | 0.03190 | 0.9385 | +#> |.....................| 0.7947 | 1.604 | 1.244 | 1.368 | +#> |<span style='font-weight: bold;'> 55</span>| 476.22864 | 0.9983 | -1.404 | -0.9612 | -0.9491 | +#> |.....................| -0.9291 | -0.08404 | -0.7710 | -0.6390 | +#> |.....................| -0.9898 | -0.5533 | -0.7039 | -0.5966 | +#> | U| 476.22864 | 92.96 | -5.707 | -0.9912 | -0.1612 | +#> |.....................| 2.207 | 1.614 | 0.03187 | 0.9380 | +#> |.....................| 0.7730 | 1.595 | 1.247 | 1.366 | +#> | X|<span style='font-weight: bold;'> 476.22864</span> | 92.96 | 0.003322 | 0.2707 | 0.8511 | +#> |.....................| 9.093 | 1.614 | 0.03187 | 0.9380 | +#> |.....................| 0.7730 | 1.595 | 1.247 | 1.366 | +#> |<span style='font-weight: bold;'> 56</span>| 476.57199 | 0.9958 | -1.445 | -0.8532 | -0.9271 | +#> |.....................| -0.8725 | -0.06353 | -0.7679 | -0.6421 | +#> |.....................| -0.9751 | -0.5970 | -0.6712 | -0.6082 | +#> | U| 476.57199 | 92.73 | -5.749 | -0.8892 | -0.1393 | +#> |.....................| 2.264 | 1.626 | 0.03191 | 0.9357 | +#> |.....................| 0.7859 | 1.542 | 1.282 | 1.353 | +#> | X|<span style='font-weight: bold;'> 476.57199</span> | 92.73 | 0.003186 | 0.2913 | 0.8700 | +#> |.....................| 9.623 | 1.626 | 0.03191 | 0.9357 | +#> |.....................| 0.7859 | 1.542 | 1.282 | 1.353 | +#> | F| Forward Diff. | -32.75 | 0.5399 | -1.515 | -0.3941 | +#> |.....................| -1.151 | 1.245 | 0.03890 | 0.2327 | +#> |.....................| 2.518 | 0.9004 | -0.2852 | 0.3306 | +#> |<span style='font-weight: bold;'> 57</span>| 476.21990 | 0.9982 | -1.515 | -0.9538 | -0.8974 | +#> |.....................| -0.8289 | -0.1020 | -0.7526 | -0.6734 | +#> |.....................| -0.9899 | -0.5334 | -0.6863 | -0.5986 | +#> | U| 476.2199 | 92.95 | -5.819 | -0.9842 | -0.1096 | +#> |.....................| 2.308 | 1.604 | 0.03214 | 0.9120 | +#> |.....................| 0.7729 | 1.619 | 1.266 | 1.364 | +#> | X|<span style='font-weight: bold;'> 476.2199</span> | 92.95 | 0.002972 | 0.2721 | 0.8962 | +#> |.....................| 10.05 | 1.604 | 0.03214 | 0.9120 | +#> |.....................| 0.7729 | 1.619 | 1.266 | 1.364 | +#> | F| Forward Diff. | -17.29 | 0.1752 | -1.213 | 0.7541 | +#> |.....................| 1.907 | 0.8055 | -0.1948 | -0.02118 | +#> |.....................| 1.522 | 1.784 | 0.5826 | 0.3001 | +#> |<span style='font-weight: bold;'> 58</span>| 476.15328 | 0.9997 | -1.587 | -0.9380 | -0.8926 | +#> |.....................| -0.8393 | -0.1057 | -0.7294 | -0.6920 | +#> |.....................| -0.9908 | -0.5546 | -0.6943 | -0.5998 | +#> | U| 476.15328 | 93.09 | -5.890 | -0.9693 | -0.1048 | +#> |.....................| 2.297 | 1.602 | 0.03249 | 0.8979 | +#> |.....................| 0.7721 | 1.593 | 1.257 | 1.362 | +#> | X|<span style='font-weight: bold;'> 476.15328</span> | 93.09 | 0.002766 | 0.2750 | 0.9005 | +#> |.....................| 9.947 | 1.602 | 0.03249 | 0.8979 | +#> |.....................| 0.7721 | 1.593 | 1.257 | 1.362 | +#> | F| Forward Diff. | 9.478 | -0.04668 | -0.07764 | 0.8847 | +#> |.....................| 1.686 | 1.059 | 0.2200 | -0.09397 | +#> |.....................| 3.078 | 0.7416 | 0.1570 | 0.2315 | +#> |<span style='font-weight: bold;'> 59</span>| 476.01802 | 1.000 | -1.651 | -0.9570 | -0.8992 | +#> |.....................| -0.8607 | -0.1274 | -0.7088 | -0.7141 | +#> |.....................| -1.015 | -0.5543 | -0.6984 | -0.6027 | +#> | U| 476.01802 | 93.12 | -5.954 | -0.9872 | -0.1113 | +#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8811 | +#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | +#> | X|<span style='font-weight: bold;'> 476.01802</span> | 93.12 | 0.002594 | 0.2715 | 0.8947 | +#> |.....................| 9.736 | 1.589 | 0.03280 | 0.8811 | +#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | +#> |<span style='font-weight: bold;'> 60</span>| 476.22711 | 1.004 | -1.844 | -1.014 | -0.9185 | +#> |.....................| -0.9244 | -0.1921 | -0.6470 | -0.7805 | +#> |.....................| -1.085 | -0.5529 | -0.7106 | -0.6114 | +#> | U| 476.22711 | 93.52 | -6.147 | -1.041 | -0.1307 | +#> |.....................| 2.212 | 1.552 | 0.03373 | 0.8308 | +#> |.....................| 0.6895 | 1.595 | 1.240 | 1.350 | +#> | X|<span style='font-weight: bold;'> 476.22711</span> | 93.52 | 0.002140 | 0.2610 | 0.8775 | +#> |.....................| 9.136 | 1.552 | 0.03373 | 0.8308 | +#> |.....................| 0.6895 | 1.595 | 1.240 | 1.350 | +#> | F| Forward Diff. | 11.37 | -0.1053 | -1.010 | 0.7448 | +#> |.....................| 1.048 | 0.2820 | 0.2022 | -0.3140 | +#> |.....................| 0.8239 | 0.7199 | -0.08354 | 0.05077 | +#> |<span style='font-weight: bold;'> 61</span>| 477.73164 | 0.9986 | -1.783 | -0.8482 | -1.092 | +#> |.....................| -0.9355 | -0.2068 | -0.7199 | -0.6608 | +#> |.....................| -1.022 | -0.4554 | -0.5612 | -0.5707 | +#> | U| 477.73164 | 92.99 | -6.086 | -0.8845 | -0.3044 | +#> |.....................| 2.201 | 1.543 | 0.03264 | 0.9215 | +#> |.....................| 0.7445 | 1.714 | 1.399 | 1.393 | +#> | X|<span style='font-weight: bold;'> 477.73164</span> | 92.99 | 0.002274 | 0.2922 | 0.7376 | +#> |.....................| 9.035 | 1.543 | 0.03264 | 0.9215 | +#> |.....................| 0.7445 | 1.714 | 1.399 | 1.393 | +#> |<span style='font-weight: bold;'> 62</span>| 476.07192 | 0.9962 | -1.664 | -0.9459 | -0.9184 | +#> |.....................| -0.8684 | -0.1353 | -0.7100 | -0.7087 | +#> |.....................| -1.016 | -0.5448 | -0.6848 | -0.5995 | +#> | U| 476.07192 | 92.76 | -5.967 | -0.9768 | -0.1306 | +#> |.....................| 2.268 | 1.585 | 0.03278 | 0.8852 | +#> |.....................| 0.7503 | 1.605 | 1.267 | 1.362 | +#> | X|<span style='font-weight: bold;'> 476.07192</span> | 92.76 | 0.002561 | 0.2735 | 0.8776 | +#> |.....................| 9.662 | 1.585 | 0.03278 | 0.8852 | +#> |.....................| 0.7503 | 1.605 | 1.267 | 1.362 | +#> |<span style='font-weight: bold;'> 63</span>| 476.10587 | 0.9957 | -1.654 | -0.9539 | -0.9043 | +#> |.....................| -0.8630 | -0.1295 | -0.7092 | -0.7126 | +#> |.....................| -1.015 | -0.5521 | -0.6949 | -0.6019 | +#> | U| 476.10587 | 92.72 | -5.958 | -0.9843 | -0.1164 | +#> |.....................| 2.274 | 1.588 | 0.03280 | 0.8822 | +#> |.....................| 0.7508 | 1.596 | 1.257 | 1.360 | +#> | X|<span style='font-weight: bold;'> 476.10587</span> | 92.72 | 0.002586 | 0.2720 | 0.8901 | +#> |.....................| 9.714 | 1.588 | 0.03280 | 0.8822 | +#> |.....................| 0.7508 | 1.596 | 1.257 | 1.360 | +#> |<span style='font-weight: bold;'> 64</span>| 476.02413 | 0.9981 | -1.651 | -0.9568 | -0.8993 | +#> |.....................| -0.8609 | -0.1274 | -0.7088 | -0.7140 | +#> |.....................| -1.015 | -0.5544 | -0.6984 | -0.6027 | +#> | U| 476.02413 | 92.94 | -5.954 | -0.9870 | -0.1114 | +#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8812 | +#> |.....................| 0.7511 | 1.593 | 1.253 | 1.359 | +#> | X|<span style='font-weight: bold;'> 476.02413</span> | 92.94 | 0.002594 | 0.2715 | 0.8946 | +#> |.....................| 9.735 | 1.589 | 0.03280 | 0.8812 | +#> |.....................| 0.7511 | 1.593 | 1.253 | 1.359 | +#> |<span style='font-weight: bold;'> 65</span>| 476.01367 | 0.9993 | -1.651 | -0.9569 | -0.8992 | +#> |.....................| -0.8608 | -0.1274 | -0.7088 | -0.7141 | +#> |.....................| -1.015 | -0.5543 | -0.6984 | -0.6027 | +#> | U| 476.01367 | 93.05 | -5.954 | -0.9871 | -0.1114 | +#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8812 | +#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | +#> | X|<span style='font-weight: bold;'> 476.01367</span> | 93.05 | 0.002594 | 0.2715 | 0.8946 | +#> |.....................| 9.736 | 1.589 | 0.03280 | 0.8812 | +#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | +#> | F| Forward Diff. | -0.2880 | -0.1104 | -1.088 | 0.7255 | +#> |.....................| 0.9655 | -0.09765 | 0.02713 | -0.4308 | +#> |.....................| 1.898 | 0.6709 | -0.08067 | 0.06084 | +#> |<span style='font-weight: bold;'> 66</span>| 476.01068 | 0.9993 | -1.651 | -0.9566 | -0.8994 | +#> |.....................| -0.8610 | -0.1274 | -0.7088 | -0.7139 | +#> |.....................| -1.015 | -0.5545 | -0.6983 | -0.6027 | +#> | U| 476.01068 | 93.06 | -5.954 | -0.9868 | -0.1116 | +#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8813 | +#> |.....................| 0.7507 | 1.593 | 1.253 | 1.359 | +#> | X|<span style='font-weight: bold;'> 476.01068</span> | 93.06 | 0.002595 | 0.2715 | 0.8944 | +#> |.....................| 9.733 | 1.589 | 0.03280 | 0.8813 | +#> |.....................| 0.7507 | 1.593 | 1.253 | 1.359 | +#> |<span style='font-weight: bold;'> 67</span>| 476.00249 | 0.9996 | -1.651 | -0.9556 | -0.9000 | +#> |.....................| -0.8619 | -0.1273 | -0.7089 | -0.7136 | +#> |.....................| -1.017 | -0.5551 | -0.6983 | -0.6027 | +#> | U| 476.00249 | 93.08 | -5.954 | -0.9860 | -0.1122 | +#> |.....................| 2.275 | 1.589 | 0.03280 | 0.8815 | +#> |.....................| 0.7493 | 1.593 | 1.253 | 1.359 | +#> | X|<span style='font-weight: bold;'> 476.00249</span> | 93.08 | 0.002595 | 0.2717 | 0.8939 | +#> |.....................| 9.725 | 1.589 | 0.03280 | 0.8815 | +#> |.....................| 0.7493 | 1.593 | 1.253 | 1.359 | +#> |<span style='font-weight: bold;'> 68</span>| 475.98648 | 0.9997 | -1.654 | -0.9518 | -0.9062 | +#> |.....................| -0.8643 | -0.1288 | -0.7101 | -0.7095 | +#> |.....................| -1.019 | -0.5521 | -0.6956 | -0.6031 | +#> | U| 475.98648 | 93.09 | -5.957 | -0.9823 | -0.1183 | +#> |.....................| 2.272 | 1.589 | 0.03278 | 0.8846 | +#> |.....................| 0.7477 | 1.596 | 1.256 | 1.359 | +#> | X|<span style='font-weight: bold;'> 475.98648</span> | 93.09 | 0.002587 | 0.2724 | 0.8884 | +#> |.....................| 9.702 | 1.589 | 0.03278 | 0.8846 | +#> |.....................| 0.7477 | 1.596 | 1.256 | 1.359 | +#> |<span style='font-weight: bold;'> 69</span>| 475.97179 | 0.9994 | -1.666 | -0.9399 | -0.9282 | +#> |.....................| -0.8710 | -0.1347 | -0.7147 | -0.6948 | +#> |.....................| -1.020 | -0.5387 | -0.6854 | -0.6045 | +#> | U| 475.97179 | 93.06 | -5.969 | -0.9711 | -0.1404 | +#> |.....................| 2.266 | 1.585 | 0.03271 | 0.8957 | +#> |.....................| 0.7463 | 1.612 | 1.267 | 1.357 | +#> | X|<span style='font-weight: bold;'> 475.97179</span> | 93.06 | 0.002557 | 0.2747 | 0.8690 | +#> |.....................| 9.637 | 1.585 | 0.03271 | 0.8957 | +#> |.....................| 0.7463 | 1.612 | 1.267 | 1.357 | +#> | F| Forward Diff. | 1.543 | -0.1187 | -0.09427 | 0.04746 | +#> |.....................| 0.7019 | 0.1743 | 0.004057 | -0.1664 | +#> |.....................| 1.824 | 1.487 | 0.8060 | -0.1087 | +#> |<span style='font-weight: bold;'> 70</span>| 475.93640 | 0.9984 | -1.664 | -0.9398 | -0.9470 | +#> |.....................| -0.8662 | -0.1315 | -0.7271 | -0.6595 | +#> |.....................| -1.030 | -0.5499 | -0.6986 | -0.5913 | +#> | U| 475.9364 | 92.96 | -5.967 | -0.9710 | -0.1592 | +#> |.....................| 2.270 | 1.587 | 0.03253 | 0.9225 | +#> |.....................| 0.7382 | 1.599 | 1.253 | 1.371 | +#> | X|<span style='font-weight: bold;'> 475.9364</span> | 92.96 | 0.002561 | 0.2747 | 0.8529 | +#> |.....................| 9.682 | 1.587 | 0.03253 | 0.9225 | +#> |.....................| 0.7382 | 1.599 | 1.253 | 1.371 | +#> | F| Forward Diff. | -18.02 | -0.07507 | -0.1675 | -0.4306 | +#> |.....................| 0.8222 | -0.4249 | -0.3576 | -0.06909 | +#> |.....................| -0.1553 | 0.7789 | -0.06902 | 0.4423 | +#> |<span style='font-weight: bold;'> 71</span>| 475.93449 | 0.9995 | -1.655 | -0.9484 | -0.9330 | +#> |.....................| -0.8784 | -0.1258 | -0.7357 | -0.6330 | +#> |.....................| -1.033 | -0.5716 | -0.6758 | -0.5988 | +#> | U| 475.93449 | 93.07 | -5.959 | -0.9791 | -0.1451 | +#> |.....................| 2.258 | 1.590 | 0.03240 | 0.9426 | +#> |.....................| 0.7351 | 1.573 | 1.277 | 1.363 | +#> | X|<span style='font-weight: bold;'> 475.93449</span> | 93.07 | 0.002583 | 0.2731 | 0.8649 | +#> |.....................| 9.566 | 1.590 | 0.03240 | 0.9426 | +#> |.....................| 0.7351 | 1.573 | 1.277 | 1.363 | +#> | F| Forward Diff. | -1.432 | -0.03245 | -0.4539 | -0.04331 | +#> |.....................| 0.5695 | -0.03993 | -0.2223 | 0.1396 | +#> |.....................| -0.3709 | -0.08203 | 1.409 | 0.03273 | +#> |<span style='font-weight: bold;'> 72</span>| 475.92305 | 1.001 | -1.648 | -0.9418 | -0.9189 | +#> |.....................| -0.8867 | -0.1240 | -0.7358 | -0.6284 | +#> |.....................| -1.035 | -0.5652 | -0.6857 | -0.6066 | +#> | U| 475.92305 | 93.18 | -5.952 | -0.9729 | -0.1311 | +#> |.....................| 2.250 | 1.591 | 0.03240 | 0.9461 | +#> |.....................| 0.7335 | 1.580 | 1.266 | 1.355 | +#> | X|<span style='font-weight: bold;'> 475.92305</span> | 93.18 | 0.002602 | 0.2743 | 0.8772 | +#> |.....................| 9.486 | 1.591 | 0.03240 | 0.9461 | +#> |.....................| 0.7335 | 1.580 | 1.266 | 1.355 | +#> | F| Forward Diff. | 18.31 | 0.001701 | 0.03033 | 0.3531 | +#> |.....................| 0.4204 | 0.05655 | -0.08057 | 0.1734 | +#> |.....................| -0.4632 | 0.1099 | 0.8178 | -0.3689 | +#> |<span style='font-weight: bold;'> 73</span>| 475.91938 | 0.9986 | -1.638 | -0.9366 | -0.9070 | +#> |.....................| -0.8945 | -0.1236 | -0.7244 | -0.6267 | +#> |.....................| -1.037 | -0.5623 | -0.6914 | -0.6147 | +#> | U| 475.91938 | 92.99 | -5.941 | -0.9680 | -0.1192 | +#> |.....................| 2.242 | 1.592 | 0.03257 | 0.9474 | +#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | +#> | X|<span style='font-weight: bold;'> 475.91938</span> | 92.99 | 0.002629 | 0.2753 | 0.8877 | +#> |.....................| 9.412 | 1.592 | 0.03257 | 0.9474 | +#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | +#> | F| Forward Diff. | -15.99 | 0.01876 | 0.07238 | 0.5908 | +#> |.....................| -0.09055 | 0.2914 | -0.2119 | 0.1409 | +#> |.....................| 0.4365 | 0.1061 | 0.4376 | -0.5157 | +#> |<span style='font-weight: bold;'> 74</span>| 475.91938 | 0.9986 | -1.638 | -0.9366 | -0.9070 | +#> |.....................| -0.8945 | -0.1236 | -0.7244 | -0.6267 | +#> |.....................| -1.037 | -0.5623 | -0.6914 | -0.6147 | +#> | U| 475.91938 | 92.99 | -5.941 | -0.9680 | -0.1192 | +#> |.....................| 2.242 | 1.592 | 0.03257 | 0.9474 | +#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | +#> | X|<span style='font-weight: bold;'> 475.91938</span> | 92.99 | 0.002629 | 0.2753 | 0.8877 | +#> |.....................| 9.412 | 1.592 | 0.03257 | 0.9474 | +#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | +#> 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: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</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='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'>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_A1 |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>| 495.80376 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 495.80376 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 495.80376</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | G| Gill Diff. | 40.10 | 2.344 | -0.09792 | 0.01304 | +#> |.....................| -0.4854 | 0.6353 | -29.93 | -20.00 | +#> |.....................| 1.261 | 9.993 | -12.68 | -0.7774 | +#> <span style='text-decoration: underline;'>|.....................| 8.106 | -12.55 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 2936.2793 | 0.3119 | -1.040 | -0.9093 | -0.9382 | +#> |.....................| -0.9801 | -0.8941 | -0.3619 | -0.5483 | +#> |.....................| -0.8992 | -1.046 | -0.6506 | -0.8594 | +#> <span style='text-decoration: underline;'>|.....................| -1.014 | -0.6521 |...........|...........|</span> +#> | U| 2936.2793 | 28.54 | -5.229 | -0.8860 | -2.190 | +#> |.....................| -4.622 | 0.4539 | 1.041 | 0.06759 | +#> |.....................| 0.7138 | 0.7431 | 1.443 | 0.9756 | +#> <span style='text-decoration: underline;'>|.....................| 0.7388 | 1.478 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 2936.2793</span> | 28.54 | 0.005360 | 0.2919 | 0.1119 | +#> |.....................| 0.009832 | 0.6116 | 1.041 | 0.06759 | +#> |.....................| 0.7138 | 0.7431 | 1.443 | 0.9756 | +#> <span style='text-decoration: underline;'>|.....................| 0.7388 | 1.478 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 515.54714 | 0.9312 | -1.004 | -0.9108 | -0.9380 | +#> |.....................| -0.9876 | -0.8843 | -0.8242 | -0.8571 | +#> |.....................| -0.8797 | -0.8912 | -0.8464 | -0.8714 | +#> <span style='text-decoration: underline;'>|.....................| -0.8888 | -0.8460 |...........|...........|</span> +#> | U| 515.54714 | 85.19 | -5.193 | -0.8873 | -2.190 | +#> |.....................| -4.630 | 0.4584 | 0.8493 | 0.05868 | +#> |.....................| 0.7280 | 0.8815 | 1.211 | 0.9641 | +#> <span style='text-decoration: underline;'>|.....................| 0.8462 | 1.242 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 515.54714</span> | 85.19 | 0.005557 | 0.2917 | 0.1119 | +#> |.....................| 0.009758 | 0.6126 | 0.8493 | 0.05868 | +#> |.....................| 0.7280 | 0.8815 | 1.211 | 0.9641 | +#> <span style='text-decoration: underline;'>|.....................| 0.8462 | 1.242 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 501.46574 | 0.9922 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9884 | -0.8833 | -0.8697 | -0.8876 | +#> |.....................| -0.8778 | -0.8761 | -0.8657 | -0.8726 | +#> <span style='text-decoration: underline;'>|.....................| -0.8765 | -0.8650 |...........|...........|</span> +#> | U| 501.46574 | 90.77 | -5.189 | -0.8874 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8304 | 0.05781 | +#> |.....................| 0.7294 | 0.8952 | 1.188 | 0.9629 | +#> <span style='text-decoration: underline;'>|.....................| 0.8568 | 1.219 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.46574</span> | 90.77 | 0.005577 | 0.2916 | 0.1119 | +#> |.....................| 0.009751 | 0.6127 | 0.8304 | 0.05781 | +#> |.....................| 0.7294 | 0.8952 | 1.188 | 0.9629 | +#> <span style='text-decoration: underline;'>|.....................| 0.8568 | 1.219 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 501.84206 | 0.9992 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9884 | -0.8832 | -0.8749 | -0.8911 | +#> |.....................| -0.8776 | -0.8743 | -0.8679 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8751 | -0.8673 |...........|...........|</span> +#> | U| 501.84206 | 91.41 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8283 | 0.05771 | +#> |.....................| 0.7296 | 0.8967 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8580 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.84206</span> | 91.41 | 0.005579 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8283 | 0.05771 | +#> |.....................| 0.7296 | 0.8967 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8580 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 501.90183 | 0.9999 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8914 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90183 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05770 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90183</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05770 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 501.90808 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90808 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90808</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 501.90873 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90873 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90873</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 501.90880 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.9088 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.9088</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 501.90881 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90881 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90881</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90882</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90882</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90882</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90882</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90882</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 501.90883 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90883 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90883</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 501.90883 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | +#> <span style='text-decoration: underline;'>|.....................| -0.8749 | -0.8675 |...........|...........|</span> +#> | U| 501.90883 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.90883</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | +#> <span style='text-decoration: underline;'>|.....................| 0.8582 | 1.216 |...........|...........|</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='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: 0.775 0.024 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'>#> 1: 92.2740 -5.2361 0.2113 1.9393 -2.0029 2.8805 1.6298 0.7279 0.7192 0.4382 6.7264 0.4769 7.2363 0.6178 +#> 2: 93.1532 -5.3060 0.0602 2.0735 -2.0177 2.7365 1.5483 0.6915 0.8577 0.4163 7.5229 0.0003 8.5494 0.0006 +#> 3: 9.3232e+01 -5.5491e+00 5.1555e-02 2.4627e+00 -1.4981e+00 2.5997e+00 1.4709e+00 6.5697e-01 8.1480e-01 3.9549e-01 4.6581e+00 4.3492e-05 5.3112e+00 1.7818e-04 +#> 4: 9.3109e+01 -5.6749e+00 3.7928e-02 2.4274e+00 -1.3355e+00 2.4697e+00 1.3973e+00 6.2412e-01 7.7406e-01 3.7572e-01 3.5252e+00 9.5643e-05 4.0990e+00 4.6584e-05 +#> 5: 9.3327e+01 -5.8341e+00 -1.6798e-02 2.4024e+00 -1.2129e+00 2.3462e+00 1.3274e+00 5.9292e-01 7.3536e-01 3.5693e-01 3.3259e+00 1.6901e-05 3.5218e+00 4.0075e-05 +#> 6: 9.3449e+01 -6.0745e+00 -6.1031e-02 2.3458e+00 -1.2034e+00 2.2289e+00 1.8700e+00 5.6327e-01 6.9859e-01 3.3908e-01 2.9533e+00 6.5587e-07 3.1056e+00 2.1346e-02 +#> 7: 93.2519 -6.0564 -0.0590 2.3588 -1.1293 2.1174 1.8910 0.5351 0.6637 0.3221 2.8211 0.0082 2.8507 0.0251 +#> 8: 93.0343 -5.9362 -0.0851 2.2949 -1.0760 2.0116 1.7964 0.5084 0.6305 0.3060 2.5340 0.0181 2.6368 0.0243 +#> 9: 93.1444 -6.1910 -0.1199 2.2709 -1.1077 1.9110 1.8664 0.4829 0.5990 0.2907 2.3768 0.0191 2.3601 0.0284 +#> 10: 93.2748 -6.4970 -0.1598 2.2235 -1.1034 2.1024 3.1968 0.4588 0.5690 0.2762 2.1991 0.0255 2.2790 0.0316 +#> 11: 93.4141 -6.4463 -0.1698 2.1876 -1.0890 1.9973 3.0370 0.4358 0.5406 0.2624 2.1469 0.0266 2.1681 0.0325 +#> 12: 93.4935 -6.5467 -0.1715 2.1666 -1.0952 1.8974 3.7848 0.4141 0.5135 0.2493 1.9137 0.0292 2.0701 0.0331 +#> 13: 93.6730 -6.4173 -0.1752 2.1387 -1.0753 1.8026 3.7278 0.3934 0.4879 0.2368 1.9084 0.0272 2.0289 0.0369 +#> 14: 93.5721 -6.2146 -0.1738 2.1854 -1.0740 2.0902 3.5415 0.3737 0.4635 0.2250 1.9861 0.0239 2.0052 0.0347 +#> 15: 93.6638 -6.3103 -0.1693 2.1828 -1.0327 2.0702 3.3644 0.3720 0.4403 0.2137 1.8947 0.0247 1.9865 0.0375 +#> 16: 93.4156 -6.0957 -0.1666 2.1755 -1.0737 2.6391 3.1962 0.3691 0.4183 0.2030 1.9089 0.0241 2.0159 0.0360 +#> 17: 93.4257 -6.1494 -0.1705 2.1664 -1.0589 2.5072 3.0714 0.3697 0.3974 0.1929 1.8253 0.0268 2.0391 0.0301 +#> 18: 93.5593 -6.1696 -0.1780 2.1670 -1.0129 2.3818 3.7604 0.3725 0.3775 0.1832 1.8529 0.0304 1.8784 0.0298 +#> 19: 93.5027 -6.2960 -0.1791 2.1543 -1.0325 2.6052 4.5501 0.3942 0.3586 0.1741 1.8082 0.0328 1.8654 0.0335 +#> 20: 93.4480 -6.4389 -0.1776 2.1772 -1.0485 2.6607 5.1881 0.3894 0.3554 0.1654 1.8032 0.0322 1.9018 0.0312 +#> 21: 93.6411 -6.2893 -0.1750 2.1759 -1.0350 2.5276 4.9287 0.3817 0.3386 0.1605 1.8533 0.0264 1.9317 0.0301 +#> 22: 93.9320 -6.1469 -0.1750 2.1910 -1.0527 2.4013 4.6823 0.3720 0.3642 0.1525 1.8949 0.0273 1.8977 0.0310 +#> 23: 93.6074 -6.3097 -0.1502 2.2111 -1.0155 2.2812 4.6643 0.3832 0.4236 0.1449 1.7075 0.0340 1.7367 0.0337 +#> 24: 93.7425 -6.4598 -0.1446 2.2249 -1.0011 2.7056 6.0597 0.3949 0.4075 0.1479 1.7180 0.0360 1.7786 0.0302 +#> 25: 94.1822 -6.3674 -0.1496 2.1917 -1.0011 3.4724 5.7567 0.3897 0.4355 0.1465 1.6977 0.0356 1.8373 0.0328 +#> 26: 94.0446 -6.3235 -0.1496 2.2004 -1.0414 3.5912 5.4688 0.3897 0.4438 0.1405 1.6765 0.0344 1.8262 0.0355 +#> 27: 94.4454 -6.2148 -0.1370 2.2360 -1.0220 4.6238 5.1954 0.3702 0.4216 0.1335 1.7209 0.0349 1.7702 0.0336 +#> 28: 94.1837 -6.1301 -0.1376 2.2253 -1.0261 4.3926 4.9356 0.3644 0.4005 0.1345 1.6968 0.0290 1.8540 0.0316 +#> 29: 94.0681 -5.8726 -0.1440 2.2237 -1.0400 4.1730 4.6889 0.3750 0.4055 0.1464 1.7084 0.0329 1.7379 0.0407 +#> 30: 94.5866 -5.9141 -0.1416 2.2045 -1.0350 3.9896 4.4544 0.3770 0.3852 0.1769 1.6009 0.0326 1.8718 0.0350 +#> 31: 94.1640 -6.0370 -0.1382 2.2140 -1.0189 5.4942 4.2317 0.3759 0.3809 0.1680 1.5887 0.0386 1.8918 0.0286 +#> 32: 94.5952 -5.8349 -0.1373 2.2374 -1.0283 5.2195 4.0201 0.3745 0.3835 0.1636 1.6451 0.0375 1.7459 0.0382 +#> 33: 95.0936 -5.8145 -0.1356 2.2325 -1.0037 4.9634 3.8191 0.3614 0.3644 0.1677 1.6313 0.0414 1.6809 0.0399 +#> 34: 94.7033 -5.8916 -0.1208 2.2687 -0.9896 5.4935 3.6281 0.3741 0.3536 0.1701 1.5923 0.0376 1.2962 0.0644 +#> 35: 94.8127 -5.9839 -0.1122 2.2615 -0.9983 5.2188 3.7348 0.3817 0.3661 0.1712 1.5848 0.0313 1.1651 0.0752 +#> 36: 94.6798 -5.8938 -0.1203 2.2441 -1.0009 4.9578 3.5480 0.3835 0.3478 0.1708 1.5525 0.0313 1.1527 0.0712 +#> 37: 93.9759 -5.8017 -0.1274 2.2346 -1.0021 4.7100 3.3706 0.3868 0.3350 0.1622 1.6278 0.0256 1.7263 0.0372 +#> 38: 94.2013 -5.8617 -0.1206 2.2570 -1.0125 4.4745 3.2021 0.3754 0.3520 0.1574 1.5396 0.0290 1.0653 0.0746 +#> 39: 94.1314 -5.7645 -0.1261 2.2381 -1.0361 4.2507 3.0420 0.3804 0.3521 0.1543 1.6280 0.0267 1.1461 0.0755 +#> 40: 93.7934 -5.8654 -0.1206 2.2417 -1.0503 4.0382 2.8899 0.3624 0.3413 0.1747 1.6231 0.0239 1.5698 0.0513 +#> 41: 93.8756 -6.0150 -0.1171 2.2581 -1.0313 3.8363 3.3629 0.3809 0.3369 0.1944 1.6461 0.0217 1.7762 0.0345 +#> 42: 94.0644 -5.9723 -0.1136 2.2769 -1.0295 3.6445 3.2171 0.3702 0.3394 0.1920 1.5035 0.0416 1.5148 0.0475 +#> 43: 93.7394 -5.9927 -0.1233 2.2650 -1.0374 3.4622 3.0562 0.3735 0.3370 0.1824 1.6022 0.0379 1.5080 0.0468 +#> 44: 93.5428 -5.9784 -0.1187 2.2780 -1.0279 3.2891 2.9495 0.3732 0.3289 0.1742 1.5456 0.0471 1.4361 0.0517 +#> 45: 93.2885 -5.9836 -0.1273 2.2650 -1.0100 3.1247 3.2884 0.3768 0.3719 0.1655 1.6579 0.0336 1.4031 0.0585 +#> 46: 93.4080 -5.9261 -0.1371 2.2513 -1.0159 3.4180 3.1630 0.3709 0.3762 0.1711 1.7365 0.0269 1.4612 0.0530 +#> 47: 93.4548 -5.8101 -0.1372 2.2650 -1.0058 3.2471 3.0049 0.3703 0.3921 0.1797 1.7161 0.0300 1.4813 0.0524 +#> 48: 93.1829 -5.6877 -0.1391 2.2594 -1.0035 3.0848 2.8546 0.3690 0.3901 0.1707 1.7558 0.0292 1.5856 0.0487 +#> 49: 93.1860 -5.8153 -0.1349 2.2793 -0.9905 2.9305 2.7119 0.3619 0.3877 0.1690 1.7255 0.0299 1.6143 0.0465 +#> 50: 93.5597 -5.7551 -0.1334 2.2669 -0.9808 2.7840 2.5763 0.3652 0.3795 0.1716 1.6690 0.0290 1.4895 0.0536 +#> 51: 93.5952 -5.8089 -0.1358 2.2626 -1.0100 2.6448 2.4475 0.3640 0.4246 0.1630 1.5892 0.0344 1.3958 0.0604 +#> 52: 93.3111 -5.9181 -0.1323 2.2489 -0.9909 2.5126 2.8739 0.3695 0.4337 0.1549 1.5200 0.0329 1.2246 0.0685 +#> 53: 93.4921 -6.0837 -0.1307 2.2513 -1.0031 2.3869 3.6029 0.3678 0.4363 0.1682 1.4683 0.0336 1.2917 0.0665 +#> 54: 93.4808 -6.2019 -0.1488 2.2068 -1.0207 2.2676 4.1833 0.3952 0.4145 0.1598 1.6478 0.0325 1.2418 0.0659 +#> 55: 93.5453 -6.2747 -0.1411 2.2297 -1.0122 2.1542 4.5107 0.3941 0.4044 0.1556 1.5685 0.0358 1.3236 0.0654 +#> 56: 94.0212 -6.2713 -0.1355 2.2228 -1.0205 2.0465 5.1718 0.3901 0.4101 0.1516 1.5568 0.0341 1.1952 0.0736 +#> 57: 93.7155 -6.2511 -0.1574 2.1899 -1.0374 1.9442 4.9132 0.3991 0.3974 0.1442 1.5528 0.0364 1.5497 0.0485 +#> 58: 93.9064 -6.2021 -0.1543 2.1935 -1.0277 1.8470 4.6676 0.3935 0.3944 0.1458 1.5590 0.0354 1.3512 0.0613 +#> 59: 93.9059 -6.3971 -0.1550 2.1899 -1.0124 1.7546 5.8885 0.3925 0.3943 0.1446 1.5641 0.0373 1.4293 0.0550 +#> 60: 93.8600 -6.2474 -0.1552 2.1978 -0.9930 1.7661 5.5941 0.3905 0.4078 0.1532 1.5235 0.0364 1.5442 0.0477 +#> 61: 93.8936 -6.3077 -0.1568 2.2022 -1.0084 1.7122 5.3507 0.3946 0.4146 0.1455 1.5154 0.0342 1.3664 0.0587 +#> 62: 93.6133 -6.1446 -0.1473 2.2277 -1.0195 1.6266 5.0832 0.3794 0.4254 0.1383 1.5586 0.0330 1.1663 0.0705 +#> 63: 93.5549 -6.3005 -0.1437 2.2302 -1.0096 1.5452 5.0969 0.3651 0.4262 0.1349 1.5730 0.0323 1.2501 0.0668 +#> 64: 93.3212 -6.1190 -0.1428 2.2309 -1.0005 1.4826 4.8421 0.3661 0.4181 0.1443 1.6657 0.0259 1.3409 0.0627 +#> 65: 93.2534 -5.9614 -0.1492 2.2310 -0.9865 1.4084 4.6000 0.3735 0.4186 0.1695 1.6883 0.0235 1.4446 0.0563 +#> 66: 93.3429 -5.9786 -0.1401 2.2198 -0.9934 1.3380 4.3700 0.3807 0.4094 0.1610 1.6697 0.0270 1.1164 0.0778 +#> 67: 93.5657 -6.2158 -0.1405 2.2326 -0.9891 1.2711 4.4653 0.3827 0.4063 0.1530 1.5851 0.0316 1.3581 0.0590 +#> 68: 93.4898 -5.9763 -0.1375 2.2431 -0.9837 1.2076 4.2420 0.3771 0.4127 0.1453 1.6134 0.0325 1.1459 0.0744 +#> 69: 93.4995 -6.1375 -0.1412 2.2423 -1.0003 1.3178 4.3907 0.3746 0.4202 0.1403 1.6223 0.0304 1.3354 0.0608 +#> 70: 93.4369 -6.1690 -0.1395 2.2472 -1.0047 1.6239 4.5654 0.3793 0.4087 0.1400 1.6317 0.0349 1.4812 0.0494 +#> 71: 93.4041 -6.3637 -0.1489 2.2348 -1.0125 1.5427 5.3897 0.3603 0.3883 0.1330 1.5954 0.0303 1.3502 0.0612 +#> 72: 93.1755 -6.4067 -0.1441 2.2492 -0.9859 1.4656 6.3554 0.3423 0.3688 0.1388 1.6135 0.0287 1.6402 0.0435 +#> 73: 93.0023 -6.7319 -0.1526 2.2550 -0.9800 1.3923 7.6438 0.3341 0.3504 0.1462 1.5491 0.0312 1.3997 0.0554 +#> 74: 92.8952 -6.7189 -0.1530 2.2393 -0.9936 1.5478 7.2616 0.3344 0.3329 0.1503 1.5626 0.0326 1.3340 0.0634 +#> 75: 93.0812 -6.8015 -0.1546 2.2265 -0.9751 1.4704 8.9537 0.3501 0.3162 0.1438 1.6019 0.0268 1.1663 0.0715 +#> 76: 93.1080 -6.1728 -0.1515 2.2259 -1.0010 1.3969 8.5060 0.3407 0.3015 0.1398 1.6484 0.0279 1.3118 0.0637 +#> 77: 92.9248 -6.3432 -0.1573 2.2221 -0.9819 1.4456 8.0807 0.3506 0.3002 0.1442 1.5947 0.0294 1.6368 0.0407 +#> 78: 93.0194 -6.1448 -0.1611 2.2228 -0.9831 1.3733 7.6767 0.3487 0.3046 0.1369 1.6471 0.0254 1.4261 0.0529 +#> 79: 92.9378 -6.6970 -0.1593 2.2313 -0.9910 1.3046 10.0158 0.3460 0.2999 0.1386 1.6108 0.0267 1.5818 0.0420 +#> 80: 93.0293 -6.3275 -0.1579 2.2290 -0.9753 1.3191 9.5150 0.3543 0.2960 0.1490 1.6570 0.0259 1.5435 0.0431 +#> 81: 93.1417 -6.2258 -0.1607 2.2285 -0.9399 1.4131 9.0393 0.3514 0.3020 0.1415 1.6990 0.0236 1.6875 0.0364 +#> 82: 92.9115 -6.1764 -0.1555 2.2204 -0.9471 1.3424 8.5873 0.3502 0.2954 0.1540 1.6780 0.0216 1.2280 0.0687 +#> 83: 93.0528 -6.3505 -0.1559 2.2391 -0.9651 1.2753 8.1579 0.3499 0.2903 0.1706 1.6924 0.0242 1.6807 0.0465 +#> 84: 93.0032 -6.2300 -0.1596 2.2300 -0.9232 1.2115 7.9391 0.3470 0.2995 0.1858 1.7153 0.0259 1.7160 0.0406 +#> 85: 93.0518 -6.3704 -0.1434 2.2696 -0.9330 1.1510 8.3071 0.3504 0.2916 0.1765 1.7072 0.0275 1.5494 0.0490 +#> 86: 93.1344 -6.3566 -0.1424 2.2595 -0.9512 1.0934 9.2972 0.3520 0.2869 0.1677 1.6609 0.0253 1.5022 0.0508 +#> 87: 93.2468 -6.3860 -0.1449 2.2505 -0.9601 1.0387 8.8323 0.3474 0.3046 0.1593 1.6326 0.0262 1.3048 0.0626 +#> 88: 93.2286 -6.3886 -0.1466 2.2452 -0.9870 0.9868 8.3907 0.3474 0.2894 0.1513 1.6554 0.0245 1.6330 0.0376 +#> 89: 93.2892 -6.0277 -0.1469 2.2403 -0.9694 0.9375 7.9712 0.3451 0.2904 0.1438 1.6795 0.0251 1.6691 0.0365 +#> 90: 93.1766 -6.1076 -0.1460 2.2502 -0.9729 0.8906 7.5726 0.3458 0.2932 0.1481 1.6182 0.0331 1.5854 0.0401 +#> 91: 93.3300 -6.0932 -0.1559 2.2356 -0.9551 0.8461 7.1940 0.3771 0.2883 0.1512 1.6728 0.0272 1.6098 0.0401 +#> 92: 93.2470 -6.4839 -0.1592 2.2265 -1.0016 0.8038 6.8343 0.3813 0.2923 0.1597 1.7017 0.0300 1.6084 0.0423 +#> 93: 93.2272 -6.2819 -0.1612 2.2356 -1.0073 0.7636 6.4926 0.3849 0.2816 0.1722 1.5422 0.0420 1.4772 0.0493 +#> 94: 93.1441 -6.1805 -0.1571 2.2274 -1.0106 0.7254 6.1680 0.3878 0.2811 0.1636 1.5998 0.0403 1.4386 0.0535 +#> 95: 92.7747 -6.2274 -0.1709 2.2191 -1.0042 0.6891 5.8596 0.3909 0.2905 0.1591 1.7184 0.0282 1.6086 0.0519 +#> 96: 92.9830 -6.3291 -0.1603 2.2297 -1.0053 0.6547 5.5666 0.3774 0.2850 0.1512 1.7427 0.0284 1.7548 0.0384 +#> 97: 92.9302 -6.3943 -0.1608 2.2211 -0.9643 0.6219 5.2882 0.3817 0.2828 0.1589 1.7080 0.0295 1.7102 0.0398 +#> 98: 92.7704 -6.3554 -0.1679 2.1894 -0.9736 0.5908 5.4196 0.3864 0.2813 0.1560 1.7234 0.0240 1.2269 0.0685 +#> 99: 92.7596 -6.2138 -0.1687 2.2088 -0.9744 0.5613 5.1486 0.3939 0.2983 0.1482 1.6732 0.0250 1.5718 0.0497 +#> 100: 92.6608 -6.2662 -0.1687 2.2180 -1.0107 0.5332 5.1471 0.3939 0.2927 0.1408 1.8434 0.0232 1.7316 0.0413 +#> 101: 92.7024 -6.1288 -0.1643 2.2096 -1.0032 0.5066 4.8898 0.3934 0.2807 0.1349 1.7055 0.0253 1.5883 0.0439 +#> 102: 92.8885 -6.3175 -0.1697 2.2208 -0.9967 0.4812 4.9699 0.3888 0.2912 0.1371 1.7311 0.0284 1.6455 0.0402 +#> 103: 92.9487 -6.2493 -0.1677 2.1861 -0.9874 0.4572 4.9605 0.3907 0.2844 0.1626 1.6898 0.0279 1.6252 0.0409 +#> 104: 92.9633 -6.2534 -0.1731 2.1797 -0.9790 0.4343 4.8675 0.4015 0.2784 0.1758 1.6516 0.0268 1.6901 0.0360 +#> 105: 93.0513 -6.0656 -0.1748 2.1802 -0.9876 0.4126 4.6241 0.4041 0.2801 0.1670 1.6863 0.0269 1.6208 0.0366 +#> 106: 93.0600 -6.2162 -0.1860 2.1783 -0.9702 0.4570 4.5504 0.4451 0.2761 0.1586 1.6859 0.0274 1.5273 0.0437 +#> 107: 93.1856 -6.1826 -0.1801 2.1796 -0.9813 0.4341 4.7286 0.4517 0.2807 0.1575 1.6268 0.0341 1.2548 0.0630 +#> 108: 93.2401 -6.2943 -0.1783 2.1808 -0.9806 0.4124 5.3114 0.4502 0.2786 0.1496 1.6676 0.0291 1.4627 0.0484 +#> 109: 93.0988 -6.1669 -0.1655 2.2018 -0.9682 0.4036 5.0458 0.4302 0.3195 0.1435 1.6524 0.0295 1.5759 0.0447 +#> 110: 93.2129 -6.3104 -0.1748 2.1876 -0.9837 0.4825 5.6408 0.4430 0.3306 0.1595 1.6068 0.0326 1.6295 0.0388 +#> 111: 93.1292 -5.9096 -0.1740 2.1932 -0.9674 0.5262 5.3587 0.4444 0.3233 0.1646 1.5777 0.0334 1.6590 0.0374 +#> 112: 93.2723 -5.8153 -0.1706 2.1920 -0.9761 0.5109 5.0908 0.4486 0.3180 0.1634 1.6128 0.0321 1.6551 0.0396 +#> 113: 93.3171 -6.0458 -0.1666 2.1879 -0.9740 0.5530 4.8362 0.4508 0.3303 0.1607 1.5862 0.0325 1.2705 0.0643 +#> 114: 93.1717 -5.9615 -0.1655 2.1638 -0.9773 0.5254 4.5944 0.4472 0.3283 0.1657 1.6307 0.0287 1.2995 0.0677 +#> 115: 93.1917 -6.0856 -0.1592 2.1576 -1.0269 0.4991 4.3647 0.4349 0.3464 0.1574 1.6430 0.0354 1.2812 0.0714 +#> 116: 93.1287 -5.9635 -0.1609 2.1640 -0.9985 0.4741 4.1465 0.4237 0.3408 0.1495 1.6910 0.0269 1.2338 0.0738 +#> 117: 93.1184 -5.8768 -0.1603 2.1842 -0.9557 0.4504 3.9392 0.4211 0.3293 0.1420 1.6447 0.0257 1.2680 0.0705 +#> 118: 93.2207 -5.7436 -0.1654 2.1709 -0.9816 0.4279 3.7422 0.4158 0.3298 0.1349 1.6860 0.0238 1.1436 0.0780 +#> 119: 93.3064 -5.8397 -0.1713 2.1722 -1.0093 0.4065 3.5551 0.4100 0.3429 0.1384 1.6612 0.0262 1.6491 0.0458 +#> 120: 93.2749 -5.8221 -0.1737 2.1643 -1.0166 0.3862 3.3773 0.4044 0.3305 0.1527 1.6516 0.0232 1.7832 0.0410 +#> 121: 93.1620 -5.9756 -0.1579 2.2018 -1.0007 0.3818 3.2992 0.3841 0.3433 0.1620 1.6648 0.0251 1.3408 0.0665 +#> 122: 93.2070 -6.0164 -0.1540 2.2154 -1.0196 0.4217 3.5598 0.3649 0.3436 0.1539 1.6757 0.0287 1.3019 0.0652 +#> 123: 93.1588 -5.7424 -0.1581 2.2142 -0.9985 0.5270 3.3818 0.3491 0.3584 0.1655 1.6321 0.0237 1.3494 0.0644 +#> 124: 93.1496 -5.6257 -0.1463 2.2264 -0.9767 0.5914 3.2127 0.3347 0.3738 0.1573 1.6553 0.0226 1.5964 0.0544 +#> 125: 93.0224 -5.8536 -0.1742 2.1859 -0.9939 0.6381 3.0521 0.3840 0.3692 0.1664 1.6009 0.0246 1.4169 0.0652 +#> 126: 93.0788 -5.6973 -0.1778 2.1772 -0.9574 0.6062 2.8995 0.3710 0.3630 0.1839 1.5256 0.0312 1.5566 0.0518 +#> 127: 93.1613 -5.5833 -0.1729 2.1806 -0.9588 0.5759 2.7545 0.3532 0.3464 0.1878 1.5708 0.0307 1.6405 0.0476 +#> 128: 93.2043 -5.6742 -0.1746 2.1919 -0.9814 0.7099 2.6168 0.3569 0.3422 0.1848 1.6236 0.0312 1.5066 0.0517 +#> 129: 93.1963 -5.7026 -0.1770 2.1853 -0.9814 0.6744 2.4859 0.3544 0.3390 0.1774 1.6150 0.0293 1.5712 0.0479 +#> 130: 93.1669 -5.7260 -0.1826 2.1565 -0.9959 0.6407 2.3616 0.3750 0.3249 0.1685 1.6347 0.0215 1.5556 0.0535 +#> 131: 93.0792 -5.7201 -0.1971 2.1339 -1.0057 0.7376 2.2436 0.3901 0.3086 0.1616 1.7653 0.0206 1.6640 0.0458 +#> 132: 92.8580 -5.8266 -0.1877 2.1512 -0.9940 0.7008 2.3272 0.3895 0.3161 0.1863 1.6050 0.0231 1.5123 0.0558 +#> 133: 92.8479 -5.8397 -0.1834 2.1637 -0.9815 0.7195 2.4732 0.3875 0.3060 0.1877 1.6197 0.0217 1.4131 0.0617 +#> 134: 92.9218 -5.8317 -0.1903 2.1709 -0.9903 0.6835 2.5070 0.3808 0.3147 0.1857 1.7298 0.0225 1.5493 0.0521 +#> 135: 92.7533 -5.7287 -0.1909 2.1670 -0.9674 0.6493 2.3817 0.3792 0.3156 0.1981 1.7074 0.0222 1.2776 0.0718 +#> 136: 92.7255 -5.9071 -0.1787 2.1826 -0.9826 0.6169 2.8147 0.3603 0.3172 0.1882 1.6242 0.0288 1.2313 0.0682 +#> 137: 92.7882 -5.9574 -0.1847 2.1549 -0.9848 0.5860 3.0538 0.3651 0.3206 0.1787 1.5640 0.0277 1.1609 0.0716 +#> 138: 92.8155 -5.9445 -0.1719 2.1750 -0.9838 0.5567 3.3525 0.3568 0.3390 0.1698 1.5507 0.0259 1.0634 0.0816 +#> 139: 92.9393 -6.0638 -0.1726 2.1840 -0.9888 0.5289 4.1627 0.3562 0.3453 0.1613 1.5792 0.0259 1.5189 0.0533 +#> 140: 93.0330 -6.1823 -0.1726 2.1984 -0.9850 0.5024 4.3153 0.3562 0.3506 0.1533 1.6467 0.0248 1.5734 0.0459 +#> 141: 93.0651 -6.1847 -0.1702 2.2183 -0.9749 0.4773 4.1656 0.3604 0.3626 0.1527 1.5887 0.0272 1.5613 0.0433 +#> 142: 93.0350 -5.9581 -0.1641 2.2133 -0.9707 0.4535 3.9574 0.3642 0.3541 0.1662 1.5904 0.0246 1.4665 0.0556 +#> 143: 92.9215 -5.7798 -0.1642 2.2269 -0.9665 0.5015 3.7595 0.3665 0.3626 0.1667 1.6019 0.0275 1.3379 0.0563 +#> 144: 93.0132 -5.6752 -0.1629 2.2273 -0.9468 0.4764 3.5715 0.3648 0.3555 0.1648 1.5218 0.0320 1.1736 0.0695 +#> 145: 92.9596 -5.8104 -0.1449 2.2498 -0.9730 0.4526 3.3929 0.3465 0.3524 0.1670 1.5918 0.0284 1.3067 0.0630 +#> 146: 92.7925 -5.7223 -0.1458 2.2463 -0.9569 0.5591 3.2233 0.3443 0.3492 0.1587 1.6175 0.0260 1.0691 0.0729 +#> 147: 92.8399 -5.8322 -0.1478 2.2485 -0.9474 0.5312 3.2015 0.3422 0.3536 0.1507 1.6257 0.0255 1.2184 0.0622 +#> 148: 92.8390 -5.9554 -0.1498 2.2490 -0.9550 0.5046 3.6305 0.3387 0.3597 0.1615 1.5994 0.0263 1.2274 0.0638 +#> 149: 92.8158 -5.9697 -0.1511 2.2337 -0.9812 0.4794 3.8244 0.3386 0.3894 0.1559 1.5723 0.0255 1.0661 0.0760 +#> 150: 92.8379 -6.0841 -0.1532 2.2323 -0.9832 0.4554 4.3416 0.3340 0.3840 0.1575 1.5375 0.0272 1.1589 0.0677 +#> 151: 92.6741 -6.3268 -0.1572 2.2252 -0.9782 0.4327 5.9395 0.3389 0.3859 0.1584 1.5384 0.0252 1.2809 0.0638 +#> 152: 92.7165 -6.3594 -0.1527 2.2233 -1.0007 0.4210 5.8433 0.3384 0.3915 0.1324 1.5861 0.0254 1.0728 0.0756 +#> 153: 92.6823 -6.2114 -0.1640 2.2160 -0.9861 0.5285 5.4117 0.3473 0.3878 0.1376 1.6150 0.0255 1.2105 0.0659 +#> 154: 92.4787 -6.1829 -0.1622 2.2055 -0.9571 0.5031 5.7087 0.3490 0.3748 0.1345 1.5749 0.0250 1.0579 0.0741 +#> 155: 92.4780 -6.4925 -0.1675 2.2190 -0.9301 0.4020 7.4764 0.3587 0.3785 0.1287 1.5959 0.0258 1.1342 0.0709 +#> 156: 92.5151 -6.2825 -0.1673 2.2194 -0.9174 0.3603 5.6463 0.3589 0.3848 0.1202 1.5413 0.0301 1.1866 0.0674 +#> 157: 92.5140 -6.0058 -0.1644 2.2312 -0.9298 0.3857 4.2481 0.3610 0.3706 0.1281 1.5944 0.0292 1.2712 0.0631 +#> 158: 92.5669 -5.8692 -0.1673 2.2493 -0.9413 0.4751 3.7632 0.3600 0.3572 0.1383 1.6202 0.0323 1.4797 0.0499 +#> 159: 92.4844 -6.0078 -0.1540 2.2464 -0.9423 0.4626 4.6774 0.3587 0.3603 0.1450 1.6404 0.0280 1.3577 0.0587 +#> 160: 92.5182 -6.1231 -0.1504 2.2518 -0.9274 0.4153 5.0466 0.3616 0.3633 0.1373 1.5891 0.0297 1.2392 0.0653 +#> 161: 92.5665 -5.9062 -0.1569 2.2563 -0.9412 0.3989 4.3594 0.3541 0.3719 0.1433 1.6242 0.0314 1.2822 0.0627 +#> 162: 92.5749 -6.0936 -0.1507 2.2752 -0.9474 0.3140 4.4065 0.3438 0.3921 0.1320 1.5013 0.0378 1.1647 0.0662 +#> 163: 92.6248 -6.1392 -0.1565 2.2499 -0.9499 0.2129 4.6022 0.3512 0.3890 0.1425 1.4936 0.0336 1.4339 0.0494 +#> 164: 92.6486 -6.3898 -0.1590 2.2519 -0.9574 0.1948 5.7817 0.3564 0.3925 0.1308 1.5218 0.0326 1.2197 0.0630 +#> 165: 92.6600 -6.3261 -0.1606 2.2464 -0.9815 0.3054 5.9162 0.3611 0.3979 0.1433 1.5747 0.0316 1.2062 0.0632 +#> 166: 92.7951 -6.3068 -0.1630 2.2428 -0.9542 0.3144 5.7041 0.3597 0.3766 0.1612 1.5464 0.0317 1.2649 0.0617 +#> 167: 92.8541 -6.4919 -0.1642 2.2275 -0.9505 0.3509 6.3858 0.3639 0.3713 0.1581 1.5543 0.0315 1.3546 0.0574 +#> 168: 92.6848 -6.3299 -0.1618 2.2329 -0.9494 0.4645 5.7127 0.3700 0.3698 0.1544 1.5058 0.0340 1.1747 0.0685 +#> 169: 92.5817 -6.0236 -0.1572 2.2583 -0.9510 0.6725 3.9864 0.3672 0.3812 0.1763 1.4445 0.0386 1.3230 0.0583 +#> 170: 92.7223 -5.9170 -0.1609 2.2456 -0.9485 0.5137 3.7991 0.3712 0.3714 0.1601 1.5502 0.0385 1.3393 0.0547 +#> 171: 92.6532 -5.9417 -0.1544 2.2294 -0.9448 0.6206 3.9052 0.3789 0.3634 0.1487 1.5809 0.0314 1.1226 0.0711 +#> 172: 92.4803 -5.7302 -0.1414 2.2679 -0.9255 0.7853 2.7901 0.3598 0.3666 0.1508 1.5531 0.0341 1.1785 0.0667 +#> 173: 92.3172 -5.7462 -0.1405 2.2823 -0.9193 1.2505 2.9155 0.3579 0.3678 0.1480 1.4894 0.0434 1.2288 0.0618 +#> 174: 92.4674 -5.6638 -0.1415 2.2775 -0.9054 1.0653 2.8138 0.3623 0.3740 0.1371 1.5301 0.0393 1.0790 0.0669 +#> 175: 92.5581 -5.6388 -0.1338 2.2878 -0.9154 0.6617 2.5216 0.3471 0.3719 0.1546 1.5231 0.0361 1.0672 0.0723 +#> 176: 92.7218 -5.7548 -0.1249 2.3099 -0.9203 0.4464 2.8226 0.3570 0.3978 0.1570 1.4938 0.0354 1.1125 0.0655 +#> 177: 92.7655 -5.6769 -0.1232 2.3114 -0.9257 0.5291 2.5249 0.3571 0.4023 0.1657 1.4392 0.0386 1.1149 0.0663 +#> 178: 92.7966 -5.6766 -0.1219 2.3202 -0.9142 0.4897 2.3359 0.3605 0.3944 0.1720 1.4792 0.0401 1.1665 0.0637 +#> 179: 92.8304 -5.7678 -0.1133 2.3352 -0.9262 0.5428 2.8512 0.3552 0.4191 0.1716 1.4994 0.0410 1.0651 0.0701 +#> 180: 92.8413 -5.7485 -0.1124 2.3452 -0.9494 0.5179 2.6552 0.3555 0.4025 0.1778 1.5102 0.0383 1.1541 0.0670 +#> 181: 92.7078 -5.7437 -0.1145 2.3257 -0.9482 0.6237 2.5673 0.3564 0.3851 0.1897 1.5373 0.0335 1.1413 0.0698 +#> 182: 92.6278 -5.7965 -0.1115 2.3341 -0.9763 0.7558 2.7421 0.3541 0.3850 0.1625 1.5720 0.0309 1.1164 0.0758 +#> 183: 92.4359 -5.7826 -0.1211 2.3204 -0.9481 1.2089 3.0954 0.3598 0.3813 0.1384 1.6391 0.0333 1.2142 0.0646 +#> 184: 92.4840 -5.9143 -0.1218 2.2965 -0.9330 1.2610 4.0248 0.3752 0.3549 0.1597 1.6019 0.0292 1.0945 0.0767 +#> 185: 92.5659 -5.8333 -0.1223 2.2914 -0.9090 1.0578 3.9752 0.3706 0.3640 0.1769 1.5858 0.0287 1.7070 0.0404 +#> 186: 92.5157 -5.9540 -0.1274 2.2967 -0.9678 1.0199 3.7413 0.3625 0.3766 0.1354 1.5905 0.0321 1.2521 0.0660 +#> 187: 92.6988 -5.8607 -0.1193 2.2922 -0.9685 1.1721 2.9764 0.3511 0.3823 0.1347 1.5790 0.0352 1.1477 0.0746 +#> 188: 92.7427 -5.9073 -0.1166 2.3166 -0.9529 1.3606 2.9747 0.3487 0.3981 0.1322 1.5315 0.0344 1.3014 0.0594 +#> 189: 92.6288 -5.8326 -0.1075 2.3268 -0.9543 1.3459 3.2341 0.3388 0.3983 0.1622 1.5374 0.0334 1.5390 0.0504 +#> 190: 92.8047 -5.6198 -0.1064 2.3212 -0.9148 1.6280 2.5774 0.3319 0.4086 0.1656 1.5159 0.0321 1.5423 0.0515 +#> 191: 92.7642 -5.5780 -0.1105 2.3041 -0.9414 1.5723 2.6038 0.3402 0.4111 0.1612 1.5254 0.0321 1.1206 0.0792 +#> 192: 92.7137 -5.5650 -0.1087 2.3014 -0.9399 1.1968 2.0552 0.3412 0.4267 0.1418 1.4910 0.0332 0.9683 0.0834 +#> 193: 93.0503 -5.6414 -0.1060 2.3050 -0.9563 1.0067 2.2362 0.3434 0.4179 0.1371 1.5947 0.0279 1.0349 0.0813 +#> 194: 93.1071 -5.6349 -0.1048 2.3170 -0.9613 1.1495 2.6224 0.3451 0.4086 0.1419 1.6235 0.0276 1.0558 0.0792 +#> 195: 93.0741 -5.7863 -0.1052 2.3293 -0.9605 1.1597 3.0814 0.3440 0.4342 0.1394 1.5248 0.0348 1.0554 0.0771 +#> 196: 93.0768 -5.6986 -0.0911 2.3395 -0.9537 1.1388 2.7165 0.3463 0.4303 0.1467 1.5960 0.0324 1.1195 0.0755 +#> 197: 92.8638 -5.7840 -0.1009 2.3420 -0.9699 1.0231 2.8293 0.3625 0.4272 0.1849 1.5366 0.0360 1.3691 0.0602 +#> 198: 92.8979 -5.8328 -0.0905 2.3497 -0.9668 0.8847 2.7469 0.3509 0.4357 0.1842 1.5501 0.0361 1.1744 0.0715 +#> 199: 92.7817 -6.0173 -0.0946 2.3477 -0.9729 0.8131 3.4886 0.3517 0.4471 0.1906 1.4350 0.0393 1.2311 0.0693 +#> 200: 92.6353 -6.0362 -0.0924 2.3396 -0.9621 0.8259 3.3916 0.3556 0.4569 0.1867 1.4397 0.0350 1.0910 0.0793 +#> 201: 92.6908 -6.0423 -0.0917 2.3400 -0.9564 0.6766 3.6159 0.3552 0.4565 0.1735 1.4506 0.0362 1.0646 0.0794 +#> 202: 92.6302 -6.0238 -0.0919 2.3443 -0.9546 0.5824 3.6723 0.3555 0.4576 0.1716 1.4800 0.0363 1.0519 0.0791 +#> 203: 92.6040 -6.0387 -0.0944 2.3405 -0.9579 0.5710 3.9080 0.3583 0.4476 0.1752 1.4934 0.0373 1.0842 0.0762 +#> 204: 92.6042 -6.0088 -0.0965 2.3351 -0.9580 0.6145 3.8412 0.3608 0.4413 0.1720 1.5047 0.0374 1.0694 0.0760 +#> 205: 92.5887 -6.0107 -0.0968 2.3362 -0.9576 0.6432 3.8854 0.3606 0.4405 0.1711 1.4896 0.0380 1.0615 0.0750 +#> 206: 92.6452 -5.9990 -0.0992 2.3311 -0.9581 0.6728 3.8231 0.3636 0.4339 0.1683 1.4904 0.0379 1.0630 0.0747 +#> 207: 92.6867 -5.9760 -0.1012 2.3283 -0.9606 0.6907 3.6867 0.3665 0.4303 0.1665 1.4908 0.0376 1.0656 0.0739 +#> 208: 92.6867 -5.9652 -0.1033 2.3252 -0.9611 0.6656 3.6185 0.3680 0.4271 0.1656 1.4972 0.0369 1.0944 0.0724 +#> 209: 92.6807 -5.9535 -0.1051 2.3225 -0.9621 0.6532 3.5653 0.3669 0.4249 0.1641 1.4992 0.0366 1.1029 0.0721 +#> 210: 92.6772 -5.9392 -0.1067 2.3185 -0.9611 0.6492 3.4774 0.3661 0.4220 0.1620 1.5034 0.0360 1.0982 0.0723 +#> 211: 92.6803 -5.9099 -0.1089 2.3129 -0.9619 0.6462 3.3783 0.3656 0.4218 0.1622 1.5094 0.0354 1.1060 0.0725 +#> 212: 92.7033 -5.9046 -0.1110 2.3085 -0.9606 0.6467 3.3879 0.3653 0.4222 0.1602 1.5099 0.0350 1.1004 0.0726 +#> 213: 92.7143 -5.9026 -0.1135 2.3046 -0.9594 0.6326 3.3887 0.3646 0.4214 0.1585 1.5139 0.0347 1.1050 0.0722 +#> 214: 92.7156 -5.9151 -0.1157 2.3011 -0.9590 0.6186 3.4587 0.3637 0.4205 0.1571 1.5149 0.0344 1.1060 0.0720 +#> 215: 92.7185 -5.9240 -0.1177 2.2984 -0.9585 0.6226 3.5192 0.3630 0.4190 0.1564 1.5155 0.0342 1.1159 0.0713 +#> 216: 92.7133 -5.9331 -0.1197 2.2953 -0.9575 0.6253 3.5505 0.3630 0.4179 0.1552 1.5199 0.0338 1.1276 0.0708 +#> 217: 92.7111 -5.9341 -0.1215 2.2924 -0.9579 0.6200 3.5565 0.3627 0.4170 0.1542 1.5238 0.0337 1.1409 0.0702 +#> 218: 92.7142 -5.9390 -0.1226 2.2901 -0.9588 0.6110 3.5792 0.3623 0.4162 0.1541 1.5236 0.0335 1.1378 0.0704 +#> 219: 92.7121 -5.9351 -0.1233 2.2891 -0.9587 0.6083 3.5562 0.3617 0.4154 0.1535 1.5280 0.0335 1.1518 0.0697 +#> 220: 92.7133 -5.9467 -0.1244 2.2876 -0.9591 0.6158 3.6036 0.3614 0.4147 0.1542 1.5273 0.0334 1.1572 0.0693 +#> 221: 92.7206 -5.9543 -0.1253 2.2856 -0.9602 0.6252 3.6357 0.3610 0.4131 0.1540 1.5272 0.0335 1.1591 0.0692 +#> 222: 92.7267 -5.9436 -0.1262 2.2840 -0.9608 0.6377 3.5725 0.3608 0.4118 0.1540 1.5302 0.0334 1.1735 0.0683 +#> 223: 92.7364 -5.9346 -0.1268 2.2825 -0.9619 0.6430 3.5288 0.3606 0.4117 0.1542 1.5327 0.0332 1.1883 0.0676 +#> 224: 92.7464 -5.9269 -0.1274 2.2822 -0.9621 0.6394 3.4906 0.3604 0.4107 0.1541 1.5342 0.0334 1.2022 0.0667 +#> 225: 92.7572 -5.9244 -0.1278 2.2813 -0.9616 0.6340 3.4677 0.3603 0.4100 0.1535 1.5345 0.0334 1.2129 0.0661 +#> 226: 92.7662 -5.9237 -0.1282 2.2803 -0.9615 0.6336 3.4532 0.3603 0.4101 0.1532 1.5326 0.0334 1.2151 0.0661 +#> 227: 92.7778 -5.9193 -0.1286 2.2792 -0.9628 0.6280 3.4339 0.3604 0.4096 0.1527 1.5323 0.0334 1.2217 0.0658 +#> 228: 92.7824 -5.9112 -0.1289 2.2782 -0.9636 0.6217 3.3964 0.3607 0.4091 0.1525 1.5316 0.0335 1.2255 0.0658 +#> 229: 92.7895 -5.9077 -0.1291 2.2770 -0.9646 0.6178 3.3717 0.3607 0.4096 0.1521 1.5326 0.0334 1.2247 0.0660 +#> 230: 92.7987 -5.9153 -0.1297 2.2758 -0.9648 0.6177 3.4004 0.3603 0.4098 0.1517 1.5333 0.0334 1.2321 0.0656 +#> 231: 92.8081 -5.9176 -0.1308 2.2735 -0.9654 0.6185 3.4195 0.3596 0.4086 0.1513 1.5361 0.0331 1.2359 0.0656 +#> 232: 92.8119 -5.9161 -0.1318 2.2715 -0.9658 0.6140 3.4221 0.3590 0.4075 0.1513 1.5387 0.0330 1.2434 0.0653 +#> 233: 92.8117 -5.9111 -0.1329 2.2694 -0.9662 0.6096 3.4008 0.3586 0.4065 0.1511 1.5410 0.0328 1.2426 0.0654 +#> 234: 92.8132 -5.9040 -0.1339 2.2672 -0.9660 0.6097 3.3787 0.3583 0.4059 0.1506 1.5425 0.0325 1.2463 0.0654 +#> 235: 92.8117 -5.8978 -0.1347 2.2653 -0.9661 0.6020 3.3558 0.3579 0.4051 0.1502 1.5443 0.0324 1.2439 0.0657 +#> 236: 92.8050 -5.8967 -0.1355 2.2638 -0.9663 0.5963 3.3466 0.3575 0.4046 0.1495 1.5453 0.0322 1.2377 0.0661 +#> 237: 92.7975 -5.9004 -0.1362 2.2625 -0.9668 0.5891 3.3624 0.3571 0.4043 0.1491 1.5460 0.0321 1.2334 0.0664 +#> 238: 92.7965 -5.9036 -0.1371 2.2613 -0.9670 0.5828 3.3683 0.3569 0.4037 0.1488 1.5486 0.0320 1.2405 0.0662 +#> 239: 92.8006 -5.9067 -0.1376 2.2607 -0.9677 0.5767 3.3801 0.3568 0.4027 0.1490 1.5487 0.0319 1.2478 0.0658 +#> 240: 92.8061 -5.9102 -0.1382 2.2597 -0.9678 0.5697 3.3876 0.3566 0.4014 0.1489 1.5499 0.0319 1.2545 0.0654 +#> 241: 92.8111 -5.9132 -0.1388 2.2589 -0.9684 0.5647 3.3986 0.3567 0.4004 0.1489 1.5507 0.0319 1.2607 0.0651 +#> 242: 92.8157 -5.9119 -0.1395 2.2577 -0.9686 0.5610 3.3902 0.3568 0.3995 0.1490 1.5524 0.0319 1.2673 0.0647 +#> 243: 92.8204 -5.9142 -0.1401 2.2567 -0.9689 0.5597 3.3991 0.3570 0.3983 0.1492 1.5526 0.0319 1.2728 0.0646 +#> 244: 92.8272 -5.9129 -0.1408 2.2558 -0.9689 0.5598 3.3989 0.3574 0.3972 0.1493 1.5542 0.0319 1.2805 0.0642 +#> 245: 92.8361 -5.9152 -0.1414 2.2548 -0.9693 0.5617 3.4133 0.3580 0.3959 0.1500 1.5541 0.0318 1.2876 0.0638 +#> 246: 92.8432 -5.9122 -0.1420 2.2536 -0.9695 0.5627 3.4039 0.3584 0.3946 0.1507 1.5546 0.0318 1.2944 0.0633 +#> 247: 92.8481 -5.9125 -0.1426 2.2524 -0.9695 0.5574 3.4087 0.3588 0.3931 0.1515 1.5556 0.0318 1.3003 0.0629 +#> 248: 92.8486 -5.9123 -0.1433 2.2515 -0.9693 0.5545 3.4095 0.3594 0.3916 0.1519 1.5583 0.0317 1.3043 0.0626 +#> 249: 92.8515 -5.9123 -0.1439 2.2505 -0.9694 0.5547 3.4088 0.3600 0.3904 0.1523 1.5605 0.0316 1.3087 0.0623 +#> 250: 92.8521 -5.9139 -0.1443 2.2493 -0.9691 0.5589 3.4212 0.3604 0.3894 0.1525 1.5617 0.0316 1.3081 0.0624 +#> 251: 92.8530 -5.9118 -0.1450 2.2484 -0.9683 0.5562 3.4138 0.3612 0.3884 0.1528 1.5615 0.0316 1.3066 0.0625 +#> 252: 92.8568 -5.9075 -0.1457 2.2474 -0.9681 0.5506 3.3889 0.3619 0.3875 0.1531 1.5620 0.0315 1.3067 0.0625 +#> 253: 92.8603 -5.9070 -0.1464 2.2467 -0.9682 0.5476 3.3746 0.3622 0.3867 0.1539 1.5640 0.0314 1.3122 0.0622 +#> 254: 92.8653 -5.9077 -0.1470 2.2457 -0.9688 0.5448 3.3656 0.3626 0.3858 0.1546 1.5641 0.0314 1.3147 0.0620 +#> 255: 92.8686 -5.9059 -0.1477 2.2445 -0.9688 0.5406 3.3533 0.3630 0.3850 0.1549 1.5637 0.0314 1.3155 0.0619 +#> 256: 92.8706 -5.9011 -0.1483 2.2435 -0.9685 0.5384 3.3300 0.3634 0.3841 0.1550 1.5644 0.0313 1.3161 0.0617 +#> 257: 92.8721 -5.8957 -0.1488 2.2426 -0.9683 0.5398 3.3084 0.3638 0.3833 0.1552 1.5647 0.0313 1.3158 0.0617 +#> 258: 92.8725 -5.8928 -0.1493 2.2419 -0.9680 0.5392 3.2921 0.3641 0.3822 0.1552 1.5665 0.0312 1.3184 0.0614 +#> 259: 92.8718 -5.8915 -0.1498 2.2411 -0.9680 0.5367 3.2850 0.3644 0.3815 0.1553 1.5668 0.0312 1.3202 0.0613 +#> 260: 92.8701 -5.8928 -0.1499 2.2409 -0.9679 0.5339 3.2888 0.3652 0.3802 0.1552 1.5675 0.0312 1.3215 0.0612 +#> 261: 92.8700 -5.8961 -0.1499 2.2407 -0.9679 0.5302 3.2976 0.3659 0.3789 0.1551 1.5677 0.0312 1.3197 0.0613 +#> 262: 92.8683 -5.9013 -0.1500 2.2407 -0.9678 0.5282 3.3236 0.3666 0.3778 0.1549 1.5684 0.0312 1.3184 0.0613 +#> 263: 92.8662 -5.9021 -0.1498 2.2407 -0.9677 0.5271 3.3285 0.3670 0.3767 0.1547 1.5682 0.0313 1.3156 0.0615 +#> 264: 92.8631 -5.9059 -0.1495 2.2409 -0.9675 0.5244 3.3527 0.3673 0.3755 0.1547 1.5677 0.0313 1.3139 0.0616 +#> 265: 92.8635 -5.9042 -0.1492 2.2411 -0.9675 0.5220 3.3541 0.3675 0.3745 0.1545 1.5676 0.0313 1.3098 0.0618 +#> 266: 92.8636 -5.9033 -0.1490 2.2411 -0.9673 0.5208 3.3523 0.3680 0.3735 0.1546 1.5679 0.0312 1.3087 0.0619 +#> 267: 92.8639 -5.9035 -0.1489 2.2413 -0.9673 0.5208 3.3566 0.3685 0.3726 0.1546 1.5676 0.0312 1.3072 0.0621 +#> 268: 92.8620 -5.9065 -0.1487 2.2413 -0.9674 0.5191 3.3797 0.3689 0.3717 0.1545 1.5676 0.0312 1.3103 0.0620 +#> 269: 92.8593 -5.9073 -0.1486 2.2416 -0.9672 0.5192 3.3885 0.3693 0.3710 0.1545 1.5685 0.0312 1.3136 0.0618 +#> 270: 92.8549 -5.9087 -0.1487 2.2418 -0.9672 0.5209 3.4007 0.3695 0.3703 0.1544 1.5703 0.0312 1.3177 0.0615 +#> 271: 92.8519 -5.9089 -0.1487 2.2416 -0.9671 0.5227 3.4043 0.3696 0.3697 0.1545 1.5705 0.0312 1.3216 0.0613 +#> 272: 92.8493 -5.9084 -0.1488 2.2416 -0.9669 0.5223 3.3999 0.3698 0.3693 0.1543 1.5707 0.0311 1.3206 0.0614 +#> 273: 92.8479 -5.9090 -0.1486 2.2416 -0.9667 0.5230 3.3980 0.3701 0.3689 0.1544 1.5699 0.0311 1.3192 0.0615 +#> 274: 92.8456 -5.9108 -0.1485 2.2417 -0.9667 0.5249 3.4024 0.3705 0.3684 0.1544 1.5688 0.0311 1.3169 0.0617 +#> 275: 92.8440 -5.9131 -0.1483 2.2422 -0.9666 0.5253 3.4117 0.3707 0.3677 0.1542 1.5690 0.0311 1.3166 0.0616 +#> 276: 92.8425 -5.9132 -0.1482 2.2426 -0.9662 0.5241 3.4171 0.3709 0.3670 0.1540 1.5689 0.0311 1.3142 0.0617 +#> 277: 92.8412 -5.9139 -0.1481 2.2430 -0.9660 0.5214 3.4228 0.3711 0.3663 0.1540 1.5687 0.0311 1.3173 0.0615 +#> 278: 92.8398 -5.9139 -0.1479 2.2432 -0.9659 0.5184 3.4254 0.3712 0.3654 0.1540 1.5684 0.0311 1.3148 0.0617 +#> 279: 92.8386 -5.9156 -0.1478 2.2433 -0.9661 0.5157 3.4338 0.3713 0.3649 0.1539 1.5682 0.0311 1.3136 0.0618 +#> 280: 92.8378 -5.9173 -0.1478 2.2428 -0.9663 0.5127 3.4381 0.3714 0.3643 0.1537 1.5679 0.0311 1.3104 0.0621 +#> 281: 92.8364 -5.9188 -0.1479 2.2423 -0.9666 0.5089 3.4418 0.3716 0.3634 0.1533 1.5674 0.0311 1.3071 0.0623 +#> 282: 92.8377 -5.9179 -0.1481 2.2418 -0.9668 0.5045 3.4355 0.3717 0.3626 0.1530 1.5686 0.0311 1.3055 0.0624 +#> 283: 92.8385 -5.9157 -0.1485 2.2410 -0.9667 0.5014 3.4260 0.3720 0.3616 0.1527 1.5699 0.0311 1.3072 0.0622 +#> 284: 92.8388 -5.9156 -0.1489 2.2403 -0.9666 0.4977 3.4274 0.3723 0.3605 0.1525 1.5705 0.0310 1.3081 0.0621 +#> 285: 92.8374 -5.9156 -0.1492 2.2395 -0.9668 0.4944 3.4215 0.3727 0.3594 0.1525 1.5716 0.0310 1.3103 0.0619 +#> 286: 92.8376 -5.9168 -0.1496 2.2388 -0.9672 0.4915 3.4197 0.3731 0.3583 0.1526 1.5724 0.0310 1.3141 0.0617 +#> 287: 92.8393 -5.9176 -0.1498 2.2380 -0.9673 0.4886 3.4177 0.3735 0.3572 0.1523 1.5737 0.0309 1.3155 0.0615 +#> 288: 92.8400 -5.9206 -0.1502 2.2372 -0.9675 0.4873 3.4259 0.3739 0.3562 0.1523 1.5739 0.0309 1.3160 0.0614 +#> 289: 92.8404 -5.9217 -0.1506 2.2362 -0.9678 0.4845 3.4269 0.3744 0.3552 0.1524 1.5735 0.0309 1.3165 0.0614 +#> 290: 92.8395 -5.9255 -0.1510 2.2354 -0.9680 0.4830 3.4395 0.3748 0.3543 0.1521 1.5737 0.0308 1.3159 0.0615 +#> 291: 92.8384 -5.9274 -0.1513 2.2345 -0.9680 0.4841 3.4460 0.3752 0.3533 0.1518 1.5742 0.0309 1.3173 0.0613 +#> 292: 92.8384 -5.9276 -0.1515 2.2342 -0.9681 0.4865 3.4437 0.3755 0.3525 0.1516 1.5738 0.0309 1.3163 0.0614 +#> 293: 92.8385 -5.9281 -0.1517 2.2338 -0.9681 0.4882 3.4446 0.3757 0.3516 0.1513 1.5738 0.0308 1.3143 0.0614 +#> 294: 92.8400 -5.9277 -0.1519 2.2335 -0.9680 0.4871 3.4449 0.3758 0.3508 0.1512 1.5736 0.0308 1.3149 0.0614 +#> 295: 92.8414 -5.9279 -0.1520 2.2331 -0.9680 0.4842 3.4523 0.3760 0.3502 0.1510 1.5740 0.0308 1.3153 0.0614 +#> 296: 92.8424 -5.9282 -0.1521 2.2329 -0.9681 0.4835 3.4589 0.3760 0.3496 0.1509 1.5743 0.0307 1.3180 0.0613 +#> 297: 92.8409 -5.9281 -0.1522 2.2325 -0.9683 0.4827 3.4636 0.3760 0.3491 0.1509 1.5745 0.0307 1.3216 0.0611 +#> 298: 92.8395 -5.9276 -0.1522 2.2322 -0.9684 0.4819 3.4641 0.3761 0.3486 0.1508 1.5744 0.0307 1.3226 0.0612 +#> 299: 92.8388 -5.9305 -0.1524 2.2321 -0.9686 0.4800 3.4829 0.3761 0.3481 0.1507 1.5745 0.0307 1.3218 0.0612 +#> 300: 92.8375 -5.9329 -0.1524 2.2321 -0.9683 0.4792 3.4982 0.3761 0.3477 0.1505 1.5745 0.0307 1.3205 0.0613 +#> 301: 92.8359 -5.9337 -0.1524 2.2321 -0.9680 0.4788 3.5056 0.3762 0.3473 0.1503 1.5746 0.0306 1.3182 0.0614 +#> 302: 92.8346 -5.9360 -0.1524 2.2322 -0.9678 0.4800 3.5237 0.3763 0.3470 0.1500 1.5744 0.0306 1.3174 0.0614 +#> 303: 92.8338 -5.9387 -0.1524 2.2324 -0.9674 0.4795 3.5444 0.3764 0.3467 0.1501 1.5738 0.0307 1.3181 0.0613 +#> 304: 92.8318 -5.9436 -0.1524 2.2327 -0.9673 0.4787 3.5819 0.3766 0.3464 0.1502 1.5735 0.0307 1.3191 0.0612 +#> 305: 92.8300 -5.9486 -0.1524 2.2327 -0.9673 0.4794 3.6200 0.3766 0.3460 0.1502 1.5726 0.0308 1.3198 0.0611 +#> 306: 92.8294 -5.9540 -0.1524 2.2328 -0.9673 0.4788 3.6681 0.3766 0.3456 0.1502 1.5723 0.0309 1.3214 0.0610 +#> 307: 92.8287 -5.9579 -0.1525 2.2330 -0.9669 0.4779 3.7052 0.3766 0.3452 0.1498 1.5735 0.0309 1.3235 0.0609 +#> 308: 92.8290 -5.9624 -0.1524 2.2332 -0.9669 0.4775 3.7470 0.3766 0.3448 0.1500 1.5737 0.0309 1.3265 0.0607 +#> 309: 92.8293 -5.9653 -0.1524 2.2333 -0.9668 0.4774 3.7756 0.3766 0.3443 0.1499 1.5736 0.0309 1.3290 0.0605 +#> 310: 92.8289 -5.9672 -0.1523 2.2335 -0.9669 0.4762 3.7957 0.3767 0.3438 0.1499 1.5736 0.0309 1.3316 0.0603 +#> 311: 92.8301 -5.9702 -0.1521 2.2337 -0.9670 0.4755 3.8172 0.3767 0.3432 0.1498 1.5737 0.0309 1.3324 0.0603 +#> 312: 92.8322 -5.9715 -0.1520 2.2341 -0.9670 0.4742 3.8229 0.3767 0.3427 0.1496 1.5734 0.0309 1.3309 0.0603 +#> 313: 92.8338 -5.9713 -0.1517 2.2342 -0.9672 0.4737 3.8202 0.3766 0.3422 0.1494 1.5733 0.0309 1.3306 0.0604 +#> 314: 92.8360 -5.9711 -0.1515 2.2343 -0.9675 0.4725 3.8154 0.3767 0.3417 0.1493 1.5733 0.0309 1.3322 0.0603 +#> 315: 92.8378 -5.9694 -0.1514 2.2343 -0.9680 0.4714 3.8051 0.3767 0.3414 0.1494 1.5734 0.0309 1.3352 0.0601 +#> 316: 92.8400 -5.9683 -0.1514 2.2343 -0.9682 0.4705 3.7984 0.3767 0.3410 0.1495 1.5735 0.0309 1.3354 0.0602 +#> 317: 92.8422 -5.9689 -0.1513 2.2344 -0.9686 0.4695 3.7961 0.3768 0.3406 0.1497 1.5735 0.0309 1.3362 0.0602 +#> 318: 92.8440 -5.9696 -0.1510 2.2347 -0.9689 0.4681 3.7934 0.3769 0.3403 0.1499 1.5731 0.0309 1.3381 0.0601 +#> 319: 92.8458 -5.9710 -0.1508 2.2350 -0.9692 0.4668 3.7913 0.3769 0.3401 0.1500 1.5723 0.0309 1.3403 0.0599 +#> 320: 92.8474 -5.9719 -0.1506 2.2353 -0.9695 0.4667 3.7876 0.3769 0.3400 0.1502 1.5714 0.0309 1.3423 0.0598 +#> 321: 92.8494 -5.9710 -0.1503 2.2355 -0.9696 0.4673 3.7790 0.3769 0.3397 0.1503 1.5709 0.0309 1.3439 0.0597 +#> 322: 92.8511 -5.9693 -0.1501 2.2359 -0.9698 0.4690 3.7674 0.3769 0.3395 0.1503 1.5708 0.0309 1.3451 0.0596 +#> 323: 92.8528 -5.9700 -0.1498 2.2364 -0.9699 0.4696 3.7641 0.3768 0.3394 0.1504 1.5701 0.0310 1.3470 0.0594 +#> 324: 92.8547 -5.9695 -0.1495 2.2369 -0.9699 0.4703 3.7567 0.3767 0.3392 0.1505 1.5698 0.0310 1.3485 0.0593 +#> 325: 92.8563 -5.9678 -0.1490 2.2376 -0.9702 0.4701 3.7473 0.3769 0.3395 0.1505 1.5702 0.0311 1.3494 0.0592 +#> 326: 92.8582 -5.9676 -0.1486 2.2382 -0.9703 0.4709 3.7434 0.3771 0.3397 0.1506 1.5700 0.0311 1.3479 0.0593 +#> 327: 92.8603 -5.9665 -0.1481 2.2389 -0.9704 0.4716 3.7361 0.3769 0.3399 0.1507 1.5699 0.0311 1.3471 0.0594 +#> 328: 92.8622 -5.9671 -0.1477 2.2397 -0.9704 0.4726 3.7379 0.3767 0.3398 0.1507 1.5698 0.0311 1.3481 0.0593 +#> 329: 92.8639 -5.9667 -0.1473 2.2405 -0.9707 0.4735 3.7366 0.3766 0.3398 0.1506 1.5696 0.0311 1.3482 0.0593 +#> 330: 92.8663 -5.9673 -0.1469 2.2413 -0.9708 0.4736 3.7382 0.3765 0.3397 0.1506 1.5691 0.0312 1.3492 0.0592 +#> 331: 92.8674 -5.9670 -0.1464 2.2420 -0.9710 0.4740 3.7350 0.3763 0.3397 0.1507 1.5689 0.0312 1.3512 0.0591 +#> 332: 92.8681 -5.9664 -0.1460 2.2428 -0.9710 0.4737 3.7311 0.3762 0.3396 0.1509 1.5687 0.0312 1.3527 0.0590 +#> 333: 92.8683 -5.9649 -0.1456 2.2436 -0.9708 0.4727 3.7232 0.3760 0.3397 0.1509 1.5686 0.0312 1.3505 0.0591 +#> 334: 92.8690 -5.9642 -0.1452 2.2444 -0.9707 0.4723 3.7194 0.3758 0.3399 0.1511 1.5682 0.0312 1.3490 0.0592 +#> 335: 92.8698 -5.9656 -0.1447 2.2454 -0.9707 0.4722 3.7289 0.3756 0.3400 0.1512 1.5674 0.0313 1.3476 0.0592 +#> 336: 92.8691 -5.9664 -0.1443 2.2463 -0.9706 0.4724 3.7333 0.3753 0.3401 0.1511 1.5669 0.0313 1.3455 0.0593 +#> 337: 92.8687 -5.9670 -0.1440 2.2471 -0.9705 0.4742 3.7378 0.3749 0.3402 0.1510 1.5665 0.0314 1.3433 0.0594 +#> 338: 92.8683 -5.9663 -0.1435 2.2480 -0.9703 0.4747 3.7370 0.3746 0.3405 0.1510 1.5663 0.0313 1.3402 0.0595 +#> 339: 92.8682 -5.9650 -0.1431 2.2488 -0.9701 0.4760 3.7332 0.3743 0.3408 0.1509 1.5661 0.0313 1.3374 0.0597 +#> 340: 92.8684 -5.9639 -0.1427 2.2496 -0.9699 0.4774 3.7283 0.3739 0.3411 0.1510 1.5658 0.0313 1.3358 0.0597 +#> 341: 92.8685 -5.9610 -0.1423 2.2504 -0.9696 0.4782 3.7169 0.3735 0.3413 0.1510 1.5661 0.0313 1.3338 0.0598 +#> 342: 92.8681 -5.9581 -0.1419 2.2512 -0.9696 0.4802 3.7060 0.3731 0.3416 0.1511 1.5661 0.0313 1.3316 0.0599 +#> 343: 92.8671 -5.9557 -0.1414 2.2521 -0.9697 0.4821 3.6971 0.3726 0.3419 0.1510 1.5667 0.0313 1.3292 0.0601 +#> 344: 92.8662 -5.9550 -0.1409 2.2531 -0.9696 0.4825 3.6931 0.3722 0.3424 0.1509 1.5660 0.0314 1.3269 0.0602 +#> 345: 92.8651 -5.9542 -0.1405 2.2542 -0.9696 0.4825 3.6886 0.3717 0.3429 0.1511 1.5645 0.0315 1.3252 0.0602 +#> 346: 92.8636 -5.9534 -0.1401 2.2549 -0.9696 0.4822 3.6821 0.3714 0.3432 0.1510 1.5638 0.0315 1.3231 0.0603 +#> 347: 92.8622 -5.9532 -0.1397 2.2557 -0.9696 0.4815 3.6782 0.3712 0.3435 0.1509 1.5636 0.0315 1.3220 0.0604 +#> 348: 92.8593 -5.9538 -0.1394 2.2566 -0.9697 0.4813 3.6787 0.3709 0.3438 0.1508 1.5634 0.0315 1.3202 0.0605 +#> 349: 92.8574 -5.9532 -0.1389 2.2574 -0.9697 0.4808 3.6739 0.3706 0.3440 0.1506 1.5630 0.0316 1.3179 0.0606 +#> 350: 92.8561 -5.9528 -0.1385 2.2583 -0.9697 0.4801 3.6705 0.3703 0.3443 0.1505 1.5625 0.0316 1.3161 0.0607 +#> 351: 92.8541 -5.9518 -0.1381 2.2591 -0.9697 0.4804 3.6650 0.3700 0.3446 0.1505 1.5619 0.0316 1.3141 0.0608 +#> 352: 92.8528 -5.9516 -0.1377 2.2599 -0.9700 0.4818 3.6626 0.3698 0.3449 0.1504 1.5614 0.0316 1.3122 0.0609 +#> 353: 92.8506 -5.9518 -0.1373 2.2607 -0.9700 0.4836 3.6601 0.3697 0.3451 0.1506 1.5604 0.0317 1.3116 0.0610 +#> 354: 92.8482 -5.9507 -0.1369 2.2615 -0.9700 0.4852 3.6520 0.3696 0.3451 0.1506 1.5595 0.0317 1.3099 0.0611 +#> 355: 92.8459 -5.9500 -0.1365 2.2624 -0.9699 0.4873 3.6467 0.3695 0.3454 0.1505 1.5589 0.0318 1.3090 0.0611 +#> 356: 92.8441 -5.9494 -0.1361 2.2632 -0.9700 0.4893 3.6407 0.3696 0.3456 0.1505 1.5581 0.0319 1.3083 0.0612 +#> 357: 92.8425 -5.9492 -0.1356 2.2641 -0.9700 0.4906 3.6359 0.3696 0.3459 0.1506 1.5568 0.0320 1.3082 0.0612 +#> 358: 92.8414 -5.9487 -0.1351 2.2649 -0.9700 0.4914 3.6300 0.3697 0.3460 0.1506 1.5559 0.0321 1.3064 0.0613 +#> 359: 92.8395 -5.9487 -0.1346 2.2657 -0.9700 0.4923 3.6262 0.3699 0.3462 0.1507 1.5558 0.0321 1.3050 0.0614 +#> 360: 92.8373 -5.9478 -0.1341 2.2666 -0.9700 0.4922 3.6206 0.3700 0.3465 0.1509 1.5553 0.0322 1.3061 0.0614 +#> 361: 92.8353 -5.9475 -0.1337 2.2673 -0.9699 0.4912 3.6183 0.3700 0.3469 0.1510 1.5549 0.0322 1.3051 0.0614 +#> 362: 92.8339 -5.9474 -0.1333 2.2681 -0.9699 0.4896 3.6164 0.3700 0.3472 0.1510 1.5549 0.0322 1.3041 0.0616 +#> 363: 92.8318 -5.9470 -0.1328 2.2690 -0.9696 0.4882 3.6136 0.3700 0.3476 0.1510 1.5541 0.0323 1.3035 0.0616 +#> 364: 92.8305 -5.9460 -0.1325 2.2697 -0.9695 0.4863 3.6099 0.3701 0.3477 0.1510 1.5533 0.0324 1.3028 0.0616 +#> 365: 92.8300 -5.9451 -0.1320 2.2705 -0.9693 0.4851 3.6083 0.3703 0.3479 0.1511 1.5535 0.0324 1.3017 0.0617 +#> 366: 92.8290 -5.9444 -0.1317 2.2710 -0.9691 0.4841 3.6062 0.3707 0.3476 0.1512 1.5534 0.0325 1.3013 0.0617 +#> 367: 92.8279 -5.9438 -0.1313 2.2715 -0.9688 0.4829 3.6026 0.3711 0.3473 0.1513 1.5537 0.0325 1.2996 0.0618 +#> 368: 92.8270 -5.9437 -0.1310 2.2721 -0.9687 0.4824 3.6015 0.3715 0.3471 0.1513 1.5535 0.0325 1.2984 0.0619 +#> 369: 92.8268 -5.9444 -0.1306 2.2726 -0.9686 0.4829 3.6042 0.3718 0.3469 0.1514 1.5530 0.0325 1.2983 0.0619 +#> 370: 92.8268 -5.9455 -0.1303 2.2732 -0.9686 0.4833 3.6099 0.3721 0.3466 0.1513 1.5526 0.0326 1.2971 0.0619 +#> 371: 92.8269 -5.9462 -0.1300 2.2737 -0.9686 0.4842 3.6169 0.3723 0.3465 0.1512 1.5516 0.0326 1.2961 0.0619 +#> 372: 92.8272 -5.9465 -0.1297 2.2741 -0.9685 0.4852 3.6242 0.3726 0.3463 0.1512 1.5507 0.0327 1.2950 0.0620 +#> 373: 92.8275 -5.9456 -0.1294 2.2746 -0.9686 0.4861 3.6219 0.3729 0.3461 0.1511 1.5501 0.0328 1.2946 0.0620 +#> 374: 92.8278 -5.9445 -0.1291 2.2750 -0.9687 0.4867 3.6175 0.3730 0.3461 0.1509 1.5496 0.0328 1.2942 0.0620 +#> 375: 92.8285 -5.9438 -0.1289 2.2753 -0.9689 0.4874 3.6118 0.3731 0.3459 0.1509 1.5491 0.0329 1.2938 0.0620 +#> 376: 92.8286 -5.9439 -0.1287 2.2755 -0.9689 0.4876 3.6100 0.3733 0.3458 0.1508 1.5488 0.0329 1.2930 0.0621 +#> 377: 92.8289 -5.9431 -0.1285 2.2758 -0.9690 0.4870 3.6054 0.3735 0.3456 0.1508 1.5487 0.0329 1.2921 0.0621 +#> 378: 92.8293 -5.9428 -0.1284 2.2760 -0.9689 0.4865 3.6019 0.3737 0.3454 0.1508 1.5484 0.0329 1.2910 0.0622 +#> 379: 92.8294 -5.9441 -0.1282 2.2763 -0.9688 0.4857 3.6077 0.3739 0.3451 0.1507 1.5480 0.0329 1.2907 0.0622 +#> 380: 92.8296 -5.9448 -0.1281 2.2766 -0.9688 0.4844 3.6104 0.3741 0.3448 0.1506 1.5475 0.0329 1.2901 0.0622 +#> 381: 92.8301 -5.9461 -0.1280 2.2767 -0.9689 0.4833 3.6194 0.3743 0.3444 0.1505 1.5476 0.0329 1.2893 0.0622 +#> 382: 92.8312 -5.9464 -0.1278 2.2768 -0.9689 0.4823 3.6237 0.3745 0.3441 0.1505 1.5476 0.0329 1.2881 0.0622 +#> 383: 92.8317 -5.9459 -0.1277 2.2770 -0.9687 0.4817 3.6282 0.3747 0.3438 0.1504 1.5479 0.0329 1.2875 0.0622 +#> 384: 92.8325 -5.9458 -0.1276 2.2772 -0.9686 0.4818 3.6293 0.3749 0.3434 0.1503 1.5481 0.0329 1.2863 0.0623 +#> 385: 92.8337 -5.9449 -0.1275 2.2773 -0.9685 0.4832 3.6263 0.3751 0.3431 0.1503 1.5481 0.0330 1.2860 0.0622 +#> 386: 92.8346 -5.9455 -0.1274 2.2773 -0.9682 0.4834 3.6283 0.3754 0.3427 0.1501 1.5483 0.0330 1.2851 0.0623 +#> 387: 92.8353 -5.9460 -0.1273 2.2775 -0.9681 0.4831 3.6303 0.3756 0.3424 0.1499 1.5486 0.0330 1.2836 0.0623 +#> 388: 92.8365 -5.9462 -0.1272 2.2777 -0.9680 0.4831 3.6294 0.3759 0.3420 0.1498 1.5486 0.0330 1.2830 0.0624 +#> 389: 92.8378 -5.9456 -0.1271 2.2779 -0.9678 0.4830 3.6260 0.3762 0.3416 0.1497 1.5486 0.0330 1.2816 0.0624 +#> 390: 92.8397 -5.9454 -0.1270 2.2779 -0.9678 0.4835 3.6245 0.3765 0.3413 0.1496 1.5488 0.0330 1.2805 0.0625 +#> 391: 92.8416 -5.9461 -0.1269 2.2780 -0.9679 0.4841 3.6273 0.3768 0.3409 0.1497 1.5486 0.0330 1.2816 0.0624 +#> 392: 92.8430 -5.9471 -0.1269 2.2779 -0.9679 0.4844 3.6293 0.3771 0.3408 0.1498 1.5483 0.0330 1.2830 0.0623 +#> 393: 92.8444 -5.9478 -0.1269 2.2779 -0.9680 0.4841 3.6310 0.3774 0.3407 0.1500 1.5485 0.0330 1.2842 0.0623 +#> 394: 92.8458 -5.9492 -0.1268 2.2779 -0.9680 0.4839 3.6370 0.3775 0.3407 0.1502 1.5484 0.0330 1.2847 0.0622 +#> 395: 92.8474 -5.9501 -0.1268 2.2780 -0.9681 0.4830 3.6391 0.3777 0.3406 0.1503 1.5485 0.0330 1.2849 0.0622 +#> 396: 92.8484 -5.9500 -0.1267 2.2781 -0.9682 0.4820 3.6369 0.3778 0.3406 0.1504 1.5490 0.0330 1.2850 0.0622 +#> 397: 92.8497 -5.9490 -0.1267 2.2782 -0.9680 0.4813 3.6308 0.3779 0.3407 0.1504 1.5494 0.0330 1.2848 0.0622 +#> 398: 92.8511 -5.9478 -0.1267 2.2782 -0.9679 0.4811 3.6256 0.3780 0.3407 0.1505 1.5498 0.0330 1.2844 0.0622 +#> 399: 92.8531 -5.9467 -0.1266 2.2782 -0.9680 0.4804 3.6208 0.3781 0.3407 0.1505 1.5505 0.0330 1.2842 0.0623 +#> 400: 92.8545 -5.9465 -0.1266 2.2782 -0.9679 0.4793 3.6175 0.3783 0.3406 0.1505 1.5506 0.0329 1.2833 0.0623 +#> 401: 92.8558 -5.9458 -0.1266 2.2781 -0.9679 0.4787 3.6135 0.3784 0.3406 0.1506 1.5506 0.0329 1.2836 0.0623 +#> 402: 92.8571 -5.9454 -0.1266 2.2780 -0.9678 0.4788 3.6122 0.3786 0.3405 0.1506 1.5508 0.0329 1.2841 0.0623 +#> 403: 92.8583 -5.9454 -0.1267 2.2778 -0.9679 0.4794 3.6115 0.3790 0.3402 0.1507 1.5508 0.0330 1.2859 0.0622 +#> 404: 92.8593 -5.9466 -0.1268 2.2776 -0.9681 0.4787 3.6149 0.3793 0.3401 0.1508 1.5507 0.0330 1.2875 0.0621 +#> 405: 92.8598 -5.9475 -0.1269 2.2774 -0.9681 0.4781 3.6208 0.3796 0.3399 0.1509 1.5507 0.0330 1.2888 0.0620 +#> 406: 92.8596 -5.9480 -0.1269 2.2773 -0.9680 0.4776 3.6238 0.3798 0.3397 0.1509 1.5508 0.0330 1.2895 0.0619 +#> 407: 92.8588 -5.9487 -0.1270 2.2773 -0.9679 0.4773 3.6289 0.3801 0.3395 0.1508 1.5510 0.0331 1.2887 0.0619 +#> 408: 92.8587 -5.9489 -0.1271 2.2771 -0.9677 0.4777 3.6323 0.3804 0.3391 0.1508 1.5513 0.0331 1.2878 0.0620 +#> 409: 92.8585 -5.9498 -0.1272 2.2770 -0.9677 0.4791 3.6383 0.3806 0.3389 0.1506 1.5512 0.0331 1.2865 0.0621 +#> 410: 92.8574 -5.9522 -0.1272 2.2769 -0.9676 0.4810 3.6538 0.3809 0.3387 0.1507 1.5509 0.0331 1.2855 0.0621 +#> 411: 92.8568 -5.9532 -0.1272 2.2767 -0.9675 0.4817 3.6651 0.3811 0.3385 0.1507 1.5508 0.0332 1.2842 0.0622 +#> 412: 92.8562 -5.9535 -0.1273 2.2767 -0.9674 0.4819 3.6756 0.3812 0.3383 0.1507 1.5509 0.0332 1.2851 0.0621 +#> 413: 92.8559 -5.9542 -0.1274 2.2766 -0.9672 0.4824 3.6881 0.3814 0.3381 0.1507 1.5514 0.0332 1.2848 0.0621 +#> 414: 92.8556 -5.9550 -0.1274 2.2765 -0.9670 0.4835 3.6990 0.3815 0.3379 0.1507 1.5519 0.0332 1.2838 0.0622 +#> 415: 92.8551 -5.9566 -0.1274 2.2764 -0.9669 0.4838 3.7133 0.3816 0.3377 0.1506 1.5522 0.0332 1.2828 0.0623 +#> 416: 92.8547 -5.9581 -0.1275 2.2764 -0.9668 0.4848 3.7276 0.3818 0.3374 0.1504 1.5526 0.0332 1.2814 0.0623 +#> 417: 92.8538 -5.9581 -0.1274 2.2764 -0.9667 0.4856 3.7321 0.3818 0.3372 0.1503 1.5532 0.0332 1.2800 0.0624 +#> 418: 92.8527 -5.9590 -0.1273 2.2766 -0.9665 0.4869 3.7398 0.3817 0.3372 0.1502 1.5532 0.0332 1.2787 0.0625 +#> 419: 92.8524 -5.9596 -0.1272 2.2768 -0.9663 0.4869 3.7467 0.3817 0.3372 0.1501 1.5531 0.0332 1.2779 0.0625 +#> 420: 92.8520 -5.9598 -0.1271 2.2771 -0.9662 0.4863 3.7494 0.3817 0.3372 0.1501 1.5528 0.0332 1.2774 0.0625 +#> 421: 92.8516 -5.9601 -0.1270 2.2772 -0.9661 0.4855 3.7541 0.3817 0.3372 0.1500 1.5527 0.0333 1.2763 0.0625 +#> 422: 92.8509 -5.9602 -0.1270 2.2775 -0.9659 0.4855 3.7554 0.3818 0.3371 0.1499 1.5525 0.0333 1.2753 0.0626 +#> 423: 92.8497 -5.9608 -0.1269 2.2777 -0.9658 0.4855 3.7590 0.3819 0.3371 0.1499 1.5524 0.0334 1.2746 0.0626 +#> 424: 92.8490 -5.9620 -0.1269 2.2779 -0.9658 0.4852 3.7657 0.3820 0.3370 0.1498 1.5521 0.0334 1.2740 0.0626 +#> 425: 92.8481 -5.9615 -0.1268 2.2780 -0.9657 0.4852 3.7639 0.3819 0.3369 0.1497 1.5520 0.0334 1.2741 0.0625 +#> 426: 92.8471 -5.9611 -0.1267 2.2783 -0.9656 0.4859 3.7632 0.3819 0.3369 0.1495 1.5520 0.0335 1.2744 0.0625 +#> 427: 92.8470 -5.9605 -0.1266 2.2784 -0.9655 0.4856 3.7616 0.3819 0.3368 0.1494 1.5522 0.0335 1.2739 0.0625 +#> 428: 92.8464 -5.9602 -0.1266 2.2786 -0.9653 0.4851 3.7603 0.3820 0.3367 0.1493 1.5522 0.0335 1.2731 0.0625 +#> 429: 92.8450 -5.9593 -0.1265 2.2788 -0.9652 0.4852 3.7573 0.3820 0.3366 0.1493 1.5525 0.0335 1.2720 0.0626 +#> 430: 92.8440 -5.9590 -0.1264 2.2789 -0.9651 0.4862 3.7586 0.3821 0.3365 0.1493 1.5524 0.0335 1.2710 0.0627 +#> 431: 92.8428 -5.9583 -0.1263 2.2791 -0.9649 0.4868 3.7575 0.3821 0.3365 0.1493 1.5522 0.0335 1.2698 0.0627 +#> 432: 92.8417 -5.9583 -0.1262 2.2793 -0.9649 0.4881 3.7580 0.3821 0.3365 0.1493 1.5518 0.0335 1.2683 0.0628 +#> 433: 92.8404 -5.9589 -0.1261 2.2796 -0.9648 0.4888 3.7614 0.3821 0.3364 0.1494 1.5513 0.0335 1.2681 0.0628 +#> 434: 92.8392 -5.9585 -0.1260 2.2798 -0.9646 0.4900 3.7602 0.3821 0.3363 0.1494 1.5509 0.0336 1.2686 0.0627 +#> 435: 92.8376 -5.9587 -0.1260 2.2801 -0.9645 0.4913 3.7622 0.3822 0.3362 0.1494 1.5506 0.0336 1.2677 0.0627 +#> 436: 92.8367 -5.9581 -0.1259 2.2802 -0.9646 0.4912 3.7594 0.3821 0.3361 0.1494 1.5504 0.0336 1.2684 0.0627 +#> 437: 92.8352 -5.9588 -0.1259 2.2803 -0.9647 0.4910 3.7634 0.3821 0.3360 0.1494 1.5501 0.0337 1.2695 0.0626 +#> 438: 92.8332 -5.9592 -0.1259 2.2804 -0.9648 0.4913 3.7649 0.3821 0.3358 0.1494 1.5498 0.0337 1.2705 0.0625 +#> 439: 92.8310 -5.9589 -0.1258 2.2805 -0.9648 0.4916 3.7630 0.3821 0.3357 0.1494 1.5497 0.0337 1.2713 0.0625 +#> 440: 92.8292 -5.9590 -0.1258 2.2806 -0.9649 0.4915 3.7620 0.3821 0.3355 0.1493 1.5494 0.0338 1.2712 0.0625 +#> 441: 92.8276 -5.9590 -0.1258 2.2808 -0.9650 0.4915 3.7619 0.3822 0.3353 0.1493 1.5493 0.0338 1.2712 0.0625 +#> 442: 92.8258 -5.9587 -0.1257 2.2809 -0.9650 0.4927 3.7592 0.3822 0.3351 0.1493 1.5493 0.0338 1.2707 0.0625 +#> 443: 92.8241 -5.9586 -0.1256 2.2811 -0.9651 0.4941 3.7563 0.3822 0.3350 0.1493 1.5491 0.0338 1.2704 0.0625 +#> 444: 92.8228 -5.9591 -0.1256 2.2812 -0.9651 0.4954 3.7566 0.3822 0.3349 0.1493 1.5488 0.0339 1.2703 0.0625 +#> 445: 92.8210 -5.9596 -0.1256 2.2813 -0.9652 0.4972 3.7573 0.3821 0.3348 0.1493 1.5484 0.0339 1.2702 0.0625 +#> 446: 92.8193 -5.9595 -0.1255 2.2815 -0.9652 0.4989 3.7551 0.3821 0.3348 0.1494 1.5482 0.0339 1.2708 0.0624 +#> 447: 92.8183 -5.9598 -0.1255 2.2817 -0.9652 0.5002 3.7548 0.3820 0.3347 0.1494 1.5478 0.0339 1.2710 0.0624 +#> 448: 92.8177 -5.9607 -0.1255 2.2818 -0.9653 0.5019 3.7585 0.3819 0.3347 0.1495 1.5475 0.0340 1.2711 0.0624 +#> 449: 92.8171 -5.9613 -0.1254 2.2819 -0.9654 0.5040 3.7592 0.3819 0.3347 0.1495 1.5474 0.0340 1.2711 0.0624 +#> 450: 92.8164 -5.9621 -0.1253 2.2821 -0.9655 0.5060 3.7632 0.3818 0.3346 0.1495 1.5470 0.0340 1.2704 0.0624 +#> 451: 92.8157 -5.9628 -0.1253 2.2822 -0.9655 0.5082 3.7655 0.3816 0.3346 0.1495 1.5469 0.0340 1.2699 0.0625 +#> 452: 92.8157 -5.9633 -0.1252 2.2824 -0.9656 0.5092 3.7657 0.3815 0.3346 0.1495 1.5468 0.0340 1.2691 0.0625 +#> 453: 92.8155 -5.9631 -0.1252 2.2823 -0.9657 0.5099 3.7646 0.3815 0.3347 0.1494 1.5470 0.0340 1.2684 0.0625 +#> 454: 92.8149 -5.9627 -0.1252 2.2823 -0.9656 0.5110 3.7623 0.3815 0.3347 0.1495 1.5470 0.0340 1.2678 0.0626 +#> 455: 92.8147 -5.9626 -0.1253 2.2822 -0.9656 0.5118 3.7610 0.3816 0.3347 0.1495 1.5471 0.0340 1.2675 0.0626 +#> 456: 92.8146 -5.9631 -0.1253 2.2821 -0.9657 0.5124 3.7612 0.3817 0.3348 0.1495 1.5473 0.0340 1.2684 0.0625 +#> 457: 92.8146 -5.9639 -0.1253 2.2820 -0.9658 0.5131 3.7636 0.3817 0.3347 0.1494 1.5471 0.0340 1.2683 0.0625 +#> 458: 92.8142 -5.9641 -0.1254 2.2818 -0.9658 0.5143 3.7637 0.3817 0.3347 0.1493 1.5472 0.0340 1.2679 0.0626 +#> 459: 92.8129 -5.9636 -0.1254 2.2818 -0.9660 0.5155 3.7609 0.3817 0.3347 0.1493 1.5474 0.0340 1.2692 0.0625 +#> 460: 92.8118 -5.9630 -0.1254 2.2817 -0.9660 0.5155 3.7563 0.3818 0.3347 0.1493 1.5476 0.0340 1.2703 0.0624 +#> 461: 92.8102 -5.9625 -0.1255 2.2816 -0.9661 0.5159 3.7525 0.3818 0.3347 0.1493 1.5478 0.0340 1.2711 0.0624 +#> 462: 92.8090 -5.9628 -0.1255 2.2814 -0.9661 0.5163 3.7520 0.3819 0.3347 0.1492 1.5481 0.0340 1.2708 0.0624 +#> 463: 92.8075 -5.9633 -0.1256 2.2813 -0.9660 0.5180 3.7534 0.3819 0.3347 0.1491 1.5484 0.0340 1.2705 0.0624 +#> 464: 92.8066 -5.9628 -0.1256 2.2812 -0.9659 0.5194 3.7507 0.3820 0.3347 0.1490 1.5485 0.0340 1.2702 0.0624 +#> 465: 92.8058 -5.9627 -0.1257 2.2811 -0.9658 0.5212 3.7506 0.3820 0.3347 0.1490 1.5484 0.0340 1.2696 0.0625 +#> 466: 92.8055 -5.9624 -0.1258 2.2808 -0.9656 0.5227 3.7510 0.3821 0.3347 0.1489 1.5487 0.0340 1.2704 0.0624 +#> 467: 92.8052 -5.9624 -0.1260 2.2805 -0.9656 0.5242 3.7518 0.3822 0.3346 0.1488 1.5488 0.0340 1.2715 0.0623 +#> 468: 92.8054 -5.9623 -0.1261 2.2803 -0.9654 0.5260 3.7545 0.3823 0.3346 0.1487 1.5493 0.0340 1.2730 0.0623 +#> 469: 92.8052 -5.9629 -0.1262 2.2803 -0.9654 0.5278 3.7617 0.3824 0.3346 0.1486 1.5495 0.0340 1.2737 0.0622 +#> 470: 92.8055 -5.9638 -0.1263 2.2802 -0.9653 0.5290 3.7667 0.3825 0.3347 0.1486 1.5494 0.0341 1.2729 0.0623 +#> 471: 92.8061 -5.9645 -0.1263 2.2801 -0.9653 0.5293 3.7702 0.3825 0.3347 0.1485 1.5494 0.0341 1.2724 0.0623 +#> 472: 92.8057 -5.9645 -0.1264 2.2800 -0.9653 0.5288 3.7699 0.3826 0.3347 0.1484 1.5495 0.0341 1.2728 0.0623 +#> 473: 92.8053 -5.9643 -0.1265 2.2799 -0.9652 0.5282 3.7701 0.3827 0.3347 0.1483 1.5494 0.0341 1.2721 0.0623 +#> 474: 92.8049 -5.9638 -0.1266 2.2798 -0.9653 0.5273 3.7676 0.3828 0.3347 0.1483 1.5495 0.0341 1.2722 0.0623 +#> 475: 92.8041 -5.9639 -0.1267 2.2796 -0.9654 0.5269 3.7668 0.3829 0.3347 0.1482 1.5495 0.0341 1.2721 0.0623 +#> 476: 92.8032 -5.9641 -0.1269 2.2794 -0.9653 0.5260 3.7681 0.3830 0.3347 0.1481 1.5496 0.0341 1.2716 0.0623 +#> 477: 92.8026 -5.9634 -0.1270 2.2792 -0.9653 0.5249 3.7647 0.3831 0.3347 0.1480 1.5500 0.0341 1.2716 0.0623 +#> 478: 92.8021 -5.9627 -0.1271 2.2789 -0.9653 0.5241 3.7606 0.3832 0.3346 0.1480 1.5500 0.0341 1.2718 0.0623 +#> 479: 92.8019 -5.9623 -0.1272 2.2787 -0.9654 0.5241 3.7581 0.3833 0.3345 0.1480 1.5502 0.0342 1.2714 0.0624 +#> 480: 92.8017 -5.9631 -0.1274 2.2784 -0.9654 0.5241 3.7606 0.3835 0.3344 0.1479 1.5503 0.0342 1.2711 0.0624 +#> 481: 92.8020 -5.9638 -0.1275 2.2781 -0.9654 0.5237 3.7659 0.3837 0.3343 0.1478 1.5508 0.0342 1.2720 0.0624 +#> 482: 92.8024 -5.9640 -0.1278 2.2777 -0.9654 0.5228 3.7668 0.3838 0.3342 0.1478 1.5512 0.0342 1.2729 0.0623 +#> 483: 92.8017 -5.9645 -0.1280 2.2773 -0.9654 0.5224 3.7676 0.3840 0.3341 0.1478 1.5515 0.0342 1.2741 0.0622 +#> 484: 92.8012 -5.9642 -0.1281 2.2771 -0.9653 0.5221 3.7649 0.3841 0.3340 0.1478 1.5521 0.0341 1.2747 0.0622 +#> 485: 92.8009 -5.9642 -0.1283 2.2769 -0.9653 0.5214 3.7635 0.3842 0.3339 0.1479 1.5523 0.0341 1.2752 0.0622 +#> 486: 92.8002 -5.9639 -0.1284 2.2767 -0.9652 0.5213 3.7609 0.3842 0.3339 0.1480 1.5523 0.0341 1.2760 0.0621 +#> 487: 92.7998 -5.9636 -0.1285 2.2767 -0.9652 0.5212 3.7603 0.3842 0.3339 0.1480 1.5525 0.0341 1.2762 0.0621 +#> 488: 92.7995 -5.9634 -0.1285 2.2766 -0.9652 0.5218 3.7592 0.3841 0.3339 0.1480 1.5530 0.0341 1.2773 0.0621 +#> 489: 92.7996 -5.9630 -0.1286 2.2765 -0.9653 0.5220 3.7578 0.3841 0.3339 0.1480 1.5532 0.0341 1.2778 0.0621 +#> 490: 92.8001 -5.9629 -0.1287 2.2764 -0.9652 0.5226 3.7573 0.3841 0.3339 0.1479 1.5533 0.0341 1.2788 0.0620 +#> 491: 92.8001 -5.9629 -0.1287 2.2762 -0.9651 0.5225 3.7568 0.3841 0.3338 0.1479 1.5533 0.0341 1.2790 0.0620 +#> 492: 92.8005 -5.9625 -0.1288 2.2761 -0.9651 0.5228 3.7544 0.3840 0.3339 0.1479 1.5536 0.0341 1.2797 0.0619 +#> 493: 92.8010 -5.9626 -0.1289 2.2759 -0.9651 0.5228 3.7544 0.3840 0.3339 0.1479 1.5537 0.0340 1.2795 0.0620 +#> 494: 92.8014 -5.9623 -0.1290 2.2757 -0.9651 0.5239 3.7523 0.3839 0.3340 0.1479 1.5540 0.0340 1.2790 0.0620 +#> 495: 92.8017 -5.9617 -0.1291 2.2755 -0.9652 0.5244 3.7491 0.3838 0.3341 0.1480 1.5540 0.0340 1.2787 0.0621 +#> 496: 92.8019 -5.9613 -0.1291 2.2754 -0.9652 0.5246 3.7459 0.3837 0.3341 0.1481 1.5539 0.0340 1.2802 0.0620 +#> 497: 92.8023 -5.9611 -0.1292 2.2753 -0.9653 0.5252 3.7447 0.3836 0.3340 0.1482 1.5539 0.0340 1.2814 0.0620 +#> 498: 92.8025 -5.9615 -0.1292 2.2752 -0.9653 0.5254 3.7446 0.3836 0.3339 0.1483 1.5539 0.0340 1.2825 0.0619 +#> 499: 92.8033 -5.9616 -0.1292 2.2751 -0.9654 0.5254 3.7447 0.3836 0.3338 0.1483 1.5538 0.0340 1.2834 0.0619 +#> 500: 92.8041 -5.9630 -0.1292 2.2752 -0.9655 0.5248 3.7529 0.3836 0.3337 0.1484 1.5538 0.0340 1.2841 0.0619</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='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> -#> <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[5]+THETA[5];</span> -#> <span class='message'>rx_expr_12~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_14~t*rx_expr_12;</span> -#> <span class='message'>rx_expr_15~1+rx_expr_14;</span> -#> <span class='message'>rx_expr_17~rx_expr_7-(rx_expr_8);</span> -#> <span class='message'>rx_expr_19~exp(rx_expr_17);</span> -#> <span class='message'>d/dt(parent)=-rx_expr_19*parent/(rx_expr_15);</span> -#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> -#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> -#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_19*parent*f_parent_to_A1/(rx_expr_15);</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_13~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_13+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_13+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_16~rx_expr_10*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1)),2)*Rx_pow_di(THETA[9],2)+Rx_pow_di(THETA[8],2))*(rx_expr_0)+(Rx_pow_di(THETA[7],2)*Rx_pow_di(((rx_expr_4+rx_expr_16)*(rx_expr_1)),2)+Rx_pow_di(THETA[6],2))*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=THETA[2];</span> -#> <span class='message'>f_parent_qlogis=THETA[3];</span> -#> <span class='message'>log_alpha=THETA[4];</span> -#> <span class='message'>log_beta=THETA[5];</span> -#> <span class='message'>sigma_low_parent=THETA[6];</span> -#> <span class='message'>rsd_high_parent=THETA[7];</span> -#> <span class='message'>sigma_low_A1=THETA[8];</span> -#> <span class='message'>rsd_high_A1=THETA[9];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=ETA[2];</span> -#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> -#> <span class='message'>eta.log_alpha=ETA[4];</span> -#> <span class='message'>eta.log_beta=ETA[5];</span> -#> <span class='message'>parent_0_model=rx_expr_6;</span> -#> <span class='message'>k_A1=rx_expr_11;</span> -#> <span class='message'>alpha=exp(rx_expr_7);</span> -#> <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: 8.173 0.386 8.556</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>, +</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_A1 |f_parent_qlogis | log_alpha | +#> |.....................| log_beta |sigma_low_parent |rsd_high_parent |sigma_low_A1 | +#> |.....................|rsd_high_A1 | o1 | o2 | o3 | +#> <span style='text-decoration: underline;'>|.....................| o4 | o5 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 504.82714 | 1.000 | -1.000 | -0.9114 | -0.8944 | +#> |.....................| -0.8457 | -0.8687 | -0.8916 | -0.8687 | +#> |.....................| -0.8916 | -0.8768 | -0.8745 | -0.8676 | +#> <span style='text-decoration: underline;'>|.....................| -0.8705 | -0.8704 |...........|...........|</span> +#> | U| 504.82714 | 93.12 | -5.303 | -0.9442 | -0.1065 | +#> |.....................| 2.291 | 1.160 | 0.03005 | 1.160 | +#> |.....................| 0.03005 | 0.7578 | 0.8738 | 1.213 | +#> <span style='text-decoration: underline;'>|.....................| 1.069 | 1.072 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 504.82714</span> | 93.12 | 0.004975 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.160 | 0.03005 | 1.160 | +#> |.....................| 0.03005 | 0.7578 | 0.8738 | 1.213 | +#> <span style='text-decoration: underline;'>|.....................| 1.069 | 1.072 |...........|...........|</span> +#> | G| Gill Diff. | 73.79 | 2.406 | 0.05615 | 0.2285 | +#> |.....................| 0.009051 | -73.50 | -23.10 | 0.2441 | +#> |.....................| -2.663 | 1.201 | 11.89 | -10.88 | +#> <span style='text-decoration: underline;'>|.....................| -9.982 | -10.81 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 4109.9562 | 0.3228 | -1.022 | -0.9119 | -0.8965 | +#> |.....................| -0.8458 | -0.1941 | -0.6796 | -0.8709 | +#> |.....................| -0.8672 | -0.8879 | -0.9836 | -0.7677 | +#> <span style='text-decoration: underline;'>|.....................| -0.7789 | -0.7712 |...........|...........|</span> +#> | U| 4109.9562 | 30.05 | -5.326 | -0.9447 | -0.1086 | +#> |.....................| 2.291 | 1.551 | 0.03324 | 1.158 | +#> |.....................| 0.03042 | 0.7495 | 0.7784 | 1.335 | +#> <span style='text-decoration: underline;'>|.....................| 1.167 | 1.178 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 4109.9562</span> | 30.05 | 0.004866 | 0.2800 | 0.8971 | +#> |.....................| 9.883 | 1.551 | 0.03324 | 1.158 | +#> |.....................| 0.03042 | 0.7495 | 0.7784 | 1.335 | +#> <span style='text-decoration: underline;'>|.....................| 1.167 | 1.178 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 527.72868 | 0.9323 | -1.002 | -0.9115 | -0.8946 | +#> |.....................| -0.8457 | -0.8012 | -0.8704 | -0.8689 | +#> |.....................| -0.8892 | -0.8779 | -0.8854 | -0.8576 | +#> <span style='text-decoration: underline;'>|.....................| -0.8613 | -0.8605 |...........|...........|</span> +#> | U| 527.72868 | 86.81 | -5.306 | -0.9442 | -0.1067 | +#> |.....................| 2.291 | 1.199 | 0.03037 | 1.159 | +#> |.....................| 0.03009 | 0.7570 | 0.8642 | 1.226 | +#> <span style='text-decoration: underline;'>|.....................| 1.079 | 1.083 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 527.72868</span> | 86.81 | 0.004964 | 0.2800 | 0.8988 | +#> |.....................| 9.884 | 1.199 | 0.03037 | 1.159 | +#> |.....................| 0.03009 | 0.7570 | 0.8642 | 1.226 | +#> <span style='text-decoration: underline;'>|.....................| 1.079 | 1.083 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 503.94655 | 0.9891 | -1.000 | -0.9114 | -0.8944 | +#> |.....................| -0.8457 | -0.8578 | -0.8882 | -0.8687 | +#> |.....................| -0.8912 | -0.8770 | -0.8762 | -0.8660 | +#> <span style='text-decoration: underline;'>|.....................| -0.8690 | -0.8688 |...........|...........|</span> +#> | U| 503.94655 | 92.10 | -5.304 | -0.9442 | -0.1066 | +#> |.....................| 2.291 | 1.166 | 0.03011 | 1.160 | +#> |.....................| 0.03006 | 0.7577 | 0.8722 | 1.215 | +#> <span style='text-decoration: underline;'>|.....................| 1.070 | 1.074 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 503.94655</span> | 92.10 | 0.004973 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.166 | 0.03011 | 1.160 | +#> |.....................| 0.03006 | 0.7577 | 0.8722 | 1.215 | +#> <span style='text-decoration: underline;'>|.....................| 1.070 | 1.074 |...........|...........|</span> +#> | F| Forward Diff. | -83.20 | 2.270 | -0.2572 | 0.1460 | +#> |.....................| -0.3233 | -71.29 | -24.25 | 0.7297 | +#> |.....................| -2.130 | 1.329 | 9.332 | -11.82 | +#> <span style='text-decoration: underline;'>|.....................| -9.604 | -10.42 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 503.03407 | 1.000 | -1.001 | -0.9114 | -0.8944 | +#> |.....................| -0.8456 | -0.8473 | -0.8847 | -0.8688 | +#> |.....................| -0.8909 | -0.8772 | -0.8776 | -0.8642 | +#> <span style='text-decoration: underline;'>|.....................| -0.8676 | -0.8673 |...........|...........|</span> +#> | U| 503.03407 | 93.15 | -5.304 | -0.9442 | -0.1066 | +#> |.....................| 2.291 | 1.172 | 0.03016 | 1.159 | +#> |.....................| 0.03007 | 0.7575 | 0.8710 | 1.217 | +#> <span style='text-decoration: underline;'>|.....................| 1.072 | 1.075 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 503.03407</span> | 93.15 | 0.004971 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.172 | 0.03016 | 1.159 | +#> |.....................| 0.03007 | 0.7575 | 0.8710 | 1.217 | +#> <span style='text-decoration: underline;'>|.....................| 1.072 | 1.075 |...........|...........|</span> +#> | F| Forward Diff. | 79.23 | 2.386 | 0.06830 | 0.2424 | +#> |.....................| 0.02121 | -70.84 | -22.28 | -0.5289 | +#> |.....................| -2.713 | 1.149 | 11.82 | -11.86 | +#> <span style='text-decoration: underline;'>|.....................| -9.567 | -10.47 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 502.12413 | 0.9895 | -1.001 | -0.9114 | -0.8945 | +#> |.....................| -0.8456 | -0.8365 | -0.8812 | -0.8687 | +#> |.....................| -0.8905 | -0.8774 | -0.8794 | -0.8624 | +#> <span style='text-decoration: underline;'>|.....................| -0.8662 | -0.8657 |...........|...........|</span> +#> | U| 502.12413 | 92.14 | -5.304 | -0.9442 | -0.1066 | +#> |.....................| 2.291 | 1.178 | 0.03021 | 1.160 | +#> |.....................| 0.03007 | 0.7574 | 0.8695 | 1.220 | +#> <span style='text-decoration: underline;'>|.....................| 1.073 | 1.077 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 502.12413</span> | 92.14 | 0.004969 | 0.2801 | 0.8989 | +#> |.....................| 9.884 | 1.178 | 0.03021 | 1.160 | +#> |.....................| 0.03007 | 0.7574 | 0.8695 | 1.220 | +#> <span style='text-decoration: underline;'>|.....................| 1.073 | 1.077 |...........|...........|</span> +#> | F| Forward Diff. | -77.28 | 2.252 | -0.2503 | 0.1427 | +#> |.....................| -0.3238 | -69.21 | -23.25 | 0.3943 | +#> |.....................| -2.493 | 1.092 | 10.79 | -11.67 | +#> <span style='text-decoration: underline;'>|.....................| -9.485 | -10.25 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 501.24651 | 1.000 | -1.001 | -0.9114 | -0.8945 | +#> |.....................| -0.8456 | -0.8257 | -0.8776 | -0.8688 | +#> |.....................| -0.8901 | -0.8775 | -0.8811 | -0.8606 | +#> <span style='text-decoration: underline;'>|.....................| -0.8647 | -0.8641 |...........|...........|</span> +#> | U| 501.24651 | 93.15 | -5.305 | -0.9441 | -0.1067 | +#> |.....................| 2.291 | 1.184 | 0.03026 | 1.160 | +#> |.....................| 0.03008 | 0.7573 | 0.8680 | 1.222 | +#> <span style='text-decoration: underline;'>|.....................| 1.075 | 1.079 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 501.24651</span> | 93.15 | 0.004968 | 0.2801 | 0.8988 | +#> |.....................| 9.885 | 1.184 | 0.03026 | 1.160 | +#> |.....................| 0.03008 | 0.7573 | 0.8680 | 1.222 | +#> <span style='text-decoration: underline;'>|.....................| 1.075 | 1.079 |...........|...........|</span> +#> | F| Forward Diff. | 78.96 | 2.363 | 0.07229 | 0.2390 | +#> |.....................| 0.02239 | -67.81 | -20.97 | 0.1381 | +#> |.....................| -2.125 | 1.379 | 9.797 | -11.70 | +#> <span style='text-decoration: underline;'>|.....................| -9.438 | -10.29 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 500.35160 | 0.9896 | -1.002 | -0.9114 | -0.8945 | +#> |.....................| -0.8456 | -0.8148 | -0.8742 | -0.8688 | +#> |.....................| -0.8898 | -0.8778 | -0.8827 | -0.8587 | +#> <span style='text-decoration: underline;'>|.....................| -0.8632 | -0.8625 |...........|...........|</span> +#> | U| 500.3516 | 92.15 | -5.305 | -0.9441 | -0.1067 | +#> |.....................| 2.291 | 1.191 | 0.03032 | 1.159 | +#> |.....................| 0.03008 | 0.7571 | 0.8666 | 1.224 | +#> <span style='text-decoration: underline;'>|.....................| 1.077 | 1.081 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 500.3516</span> | 92.15 | 0.004966 | 0.2801 | 0.8988 | +#> |.....................| 9.885 | 1.191 | 0.03032 | 1.159 | +#> |.....................| 0.03008 | 0.7571 | 0.8666 | 1.224 | +#> <span style='text-decoration: underline;'>|.....................| 1.077 | 1.081 |...........|...........|</span> +#> | F| Forward Diff. | -75.23 | 2.232 | -0.2459 | 0.1501 | +#> |.....................| -0.3253 | -66.87 | -22.19 | 0.4436 | +#> |.....................| -2.150 | 0.9434 | 9.182 | -11.49 | +#> <span style='text-decoration: underline;'>|.....................| -9.350 | -10.07 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 499.45361 | 1.000 | -1.002 | -0.9113 | -0.8946 | +#> |.....................| -0.8455 | -0.8036 | -0.8705 | -0.8689 | +#> |.....................| -0.8894 | -0.8779 | -0.8842 | -0.8568 | +#> <span style='text-decoration: underline;'>|.....................| -0.8616 | -0.8608 |...........|...........|</span> +#> | U| 499.45361 | 93.12 | -5.306 | -0.9441 | -0.1067 | +#> |.....................| 2.291 | 1.197 | 0.03037 | 1.159 | +#> |.....................| 0.03009 | 0.7570 | 0.8653 | 1.226 | +#> <span style='text-decoration: underline;'>|.....................| 1.078 | 1.082 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 499.45361</span> | 93.12 | 0.004964 | 0.2801 | 0.8988 | +#> |.....................| 9.885 | 1.197 | 0.03037 | 1.159 | +#> |.....................| 0.03009 | 0.7570 | 0.8653 | 1.226 | +#> <span style='text-decoration: underline;'>|.....................| 1.078 | 1.082 |...........|...........|</span> +#> | F| Forward Diff. | 73.21 | 2.337 | 0.06584 | 0.2472 | +#> |.....................| 0.008903 | -65.96 | -20.21 | -0.3457 | +#> |.....................| -2.677 | 1.048 | 11.29 | -11.53 | +#> <span style='text-decoration: underline;'>|.....................| -9.311 | -10.11 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 498.59105 | 0.9896 | -1.003 | -0.9113 | -0.8946 | +#> |.....................| -0.8455 | -0.7924 | -0.8671 | -0.8688 | +#> |.....................| -0.8890 | -0.8781 | -0.8861 | -0.8548 | +#> <span style='text-decoration: underline;'>|.....................| -0.8600 | -0.8591 |...........|...........|</span> +#> | U| 498.59105 | 92.15 | -5.306 | -0.9441 | -0.1068 | +#> |.....................| 2.291 | 1.204 | 0.03042 | 1.159 | +#> |.....................| 0.03009 | 0.7568 | 0.8636 | 1.229 | +#> <span style='text-decoration: underline;'>|.....................| 1.080 | 1.084 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 498.59105</span> | 92.15 | 0.004962 | 0.2801 | 0.8987 | +#> |.....................| 9.885 | 1.204 | 0.03042 | 1.159 | +#> |.....................| 0.03009 | 0.7568 | 0.8636 | 1.229 | +#> <span style='text-decoration: underline;'>|.....................| 1.080 | 1.084 |...........|...........|</span> +#> | F| Forward Diff. | -74.43 | 2.211 | -0.2431 | 0.1502 | +#> |.....................| -0.3305 | -64.40 | -21.08 | 0.5329 | +#> |.....................| -2.487 | 0.9319 | 8.926 | -11.33 | +#> <span style='text-decoration: underline;'>|.....................| -9.217 | -9.888 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 497.71590 | 1.000 | -1.003 | -0.9113 | -0.8946 | +#> |.....................| -0.8455 | -0.7811 | -0.8634 | -0.8689 | +#> |.....................| -0.8885 | -0.8783 | -0.8877 | -0.8529 | +#> <span style='text-decoration: underline;'>|.....................| -0.8584 | -0.8573 |...........|...........|</span> +#> | U| 497.7159 | 93.11 | -5.306 | -0.9441 | -0.1068 | +#> |.....................| 2.291 | 1.210 | 0.03048 | 1.159 | +#> |.....................| 0.03010 | 0.7567 | 0.8622 | 1.231 | +#> <span style='text-decoration: underline;'>|.....................| 1.082 | 1.086 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 497.7159</span> | 93.11 | 0.004960 | 0.2801 | 0.8987 | +#> |.....................| 9.886 | 1.210 | 0.03048 | 1.159 | +#> |.....................| 0.03010 | 0.7567 | 0.8622 | 1.231 | +#> <span style='text-decoration: underline;'>|.....................| 1.082 | 1.086 |...........|...........|</span> +#> | F| Forward Diff. | 71.79 | 2.312 | 0.07434 | 0.2557 | +#> |.....................| 0.006614 | -63.04 | -18.95 | 0.3164 | +#> |.....................| -2.117 | 1.342 | 9.274 | -11.35 | +#> <span style='text-decoration: underline;'>|.....................| -9.172 | -9.924 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 496.86264 | 0.9898 | -1.003 | -0.9113 | -0.8947 | +#> |.....................| -0.8455 | -0.7696 | -0.8599 | -0.8690 | +#> |.....................| -0.8881 | -0.8785 | -0.8894 | -0.8508 | +#> <span style='text-decoration: underline;'>|.....................| -0.8567 | -0.8555 |...........|...........|</span> +#> | U| 496.86264 | 92.17 | -5.307 | -0.9441 | -0.1068 | +#> |.....................| 2.291 | 1.217 | 0.03053 | 1.159 | +#> |.....................| 0.03011 | 0.7565 | 0.8607 | 1.234 | +#> <span style='text-decoration: underline;'>|.....................| 1.084 | 1.088 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 496.86264</span> | 92.17 | 0.004958 | 0.2801 | 0.8987 | +#> |.....................| 9.886 | 1.217 | 0.03053 | 1.159 | +#> |.....................| 0.03011 | 0.7565 | 0.8607 | 1.234 | +#> <span style='text-decoration: underline;'>|.....................| 1.084 | 1.088 |...........|...........|</span> +#> | F| Forward Diff. | -71.54 | 2.190 | -0.2371 | 0.1482 | +#> |.....................| -0.3369 | -61.67 | -19.90 | 0.9419 | +#> |.....................| -2.139 | 1.041 | 7.036 | -11.13 | +#> <span style='text-decoration: underline;'>|.....................| -9.064 | -9.692 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 495.99097 | 0.9997 | -1.004 | -0.9113 | -0.8947 | +#> |.....................| -0.8454 | -0.7580 | -0.8562 | -0.8692 | +#> |.....................| -0.8877 | -0.8787 | -0.8907 | -0.8487 | +#> <span style='text-decoration: underline;'>|.....................| -0.8550 | -0.8537 |...........|...........|</span> +#> | U| 495.99097 | 93.09 | -5.307 | -0.9441 | -0.1069 | +#> |.....................| 2.291 | 1.224 | 0.03059 | 1.159 | +#> |.....................| 0.03011 | 0.7564 | 0.8596 | 1.236 | +#> <span style='text-decoration: underline;'>|.....................| 1.085 | 1.090 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 495.99097</span> | 93.09 | 0.004956 | 0.2801 | 0.8987 | +#> |.....................| 9.886 | 1.224 | 0.03059 | 1.159 | +#> |.....................| 0.03011 | 0.7564 | 0.8596 | 1.236 | +#> <span style='text-decoration: underline;'>|.....................| 1.085 | 1.090 |...........|...........|</span> +#> | F| Forward Diff. | 67.48 | 2.282 | 0.05510 | 0.2442 | +#> |.....................| -0.01700 | -60.62 | -17.93 | 0.4372 | +#> |.....................| -2.100 | 1.212 | 9.042 | -11.17 | +#> <span style='text-decoration: underline;'>|.....................| -9.025 | -9.723 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 495.15472 | 0.9899 | -1.004 | -0.9113 | -0.8948 | +#> |.....................| -0.8454 | -0.7463 | -0.8527 | -0.8693 | +#> |.....................| -0.8873 | -0.8789 | -0.8924 | -0.8465 | +#> <span style='text-decoration: underline;'>|.....................| -0.8533 | -0.8518 |...........|...........|</span> +#> | U| 495.15472 | 92.18 | -5.308 | -0.9441 | -0.1069 | +#> |.....................| 2.291 | 1.231 | 0.03064 | 1.159 | +#> |.....................| 0.03012 | 0.7562 | 0.8581 | 1.239 | +#> <span style='text-decoration: underline;'>|.....................| 1.087 | 1.092 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 495.15472</span> | 92.18 | 0.004954 | 0.2801 | 0.8986 | +#> |.....................| 9.886 | 1.231 | 0.03064 | 1.159 | +#> |.....................| 0.03012 | 0.7562 | 0.8581 | 1.239 | +#> <span style='text-decoration: underline;'>|.....................| 1.087 | 1.092 |...........|...........|</span> +#> | F| Forward Diff. | -68.93 | 2.171 | -0.2257 | 0.1488 | +#> |.....................| -0.3348 | -59.34 | -18.81 | 1.070 | +#> |.....................| -2.082 | 1.016 | 8.208 | -10.96 | +#> <span style='text-decoration: underline;'>|.....................| -8.930 | -9.498 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 494.30065 | 0.9995 | -1.005 | -0.9112 | -0.8948 | +#> |.....................| -0.8453 | -0.7344 | -0.8490 | -0.8695 | +#> |.....................| -0.8869 | -0.8792 | -0.8941 | -0.8443 | +#> <span style='text-decoration: underline;'>|.....................| -0.8515 | -0.8499 |...........|...........|</span> +#> | U| 494.30065 | 93.07 | -5.308 | -0.9440 | -0.1069 | +#> |.....................| 2.291 | 1.237 | 0.03069 | 1.159 | +#> |.....................| 0.03013 | 0.7561 | 0.8567 | 1.242 | +#> <span style='text-decoration: underline;'>|.....................| 1.089 | 1.094 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 494.30065</span> | 93.07 | 0.004951 | 0.2801 | 0.8986 | +#> |.....................| 9.887 | 1.237 | 0.03069 | 1.159 | +#> |.....................| 0.03013 | 0.7561 | 0.8567 | 1.242 | +#> <span style='text-decoration: underline;'>|.....................| 1.089 | 1.094 |...........|...........|</span> +#> | F| Forward Diff. | 65.20 | 2.260 | 0.06851 | 0.2416 | +#> |.....................| -0.02143 | -58.42 | -17.03 | 0.3665 | +#> |.....................| -2.202 | 1.112 | 7.377 | -10.96 | +#> <span style='text-decoration: underline;'>|.....................| -8.866 | -9.510 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 493.48608 | 0.9901 | -1.005 | -0.9112 | -0.8948 | +#> |.....................| -0.8453 | -0.7225 | -0.8455 | -0.8696 | +#> |.....................| -0.8865 | -0.8794 | -0.8956 | -0.8421 | +#> <span style='text-decoration: underline;'>|.....................| -0.8496 | -0.8479 |...........|...........|</span> +#> | U| 493.48608 | 92.19 | -5.309 | -0.9440 | -0.1070 | +#> |.....................| 2.291 | 1.244 | 0.03075 | 1.159 | +#> |.....................| 0.03013 | 0.7559 | 0.8553 | 1.244 | +#> <span style='text-decoration: underline;'>|.....................| 1.091 | 1.096 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 493.48608</span> | 92.19 | 0.004949 | 0.2801 | 0.8985 | +#> |.....................| 9.887 | 1.244 | 0.03075 | 1.159 | +#> |.....................| 0.03013 | 0.7559 | 0.8553 | 1.244 | +#> <span style='text-decoration: underline;'>|.....................| 1.091 | 1.096 |...........|...........|</span> +#> | F| Forward Diff. | -66.94 | 2.152 | -0.2367 | 0.1452 | +#> |.....................| -0.3412 | -57.13 | -17.84 | 1.057 | +#> |.....................| -2.129 | 0.9540 | 6.557 | -10.77 | +#> <span style='text-decoration: underline;'>|.....................| -8.770 | -9.285 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 492.64670 | 0.9993 | -1.006 | -0.9112 | -0.8949 | +#> |.....................| -0.8453 | -0.7105 | -0.8419 | -0.8698 | +#> |.....................| -0.8860 | -0.8796 | -0.8969 | -0.8398 | +#> <span style='text-decoration: underline;'>|.....................| -0.8478 | -0.8460 |...........|...........|</span> +#> | U| 492.6467 | 93.06 | -5.309 | -0.9440 | -0.1070 | +#> |.....................| 2.291 | 1.251 | 0.03080 | 1.159 | +#> |.....................| 0.03014 | 0.7557 | 0.8542 | 1.247 | +#> <span style='text-decoration: underline;'>|.....................| 1.093 | 1.098 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 492.6467</span> | 93.06 | 0.004947 | 0.2801 | 0.8985 | +#> |.....................| 9.888 | 1.251 | 0.03080 | 1.159 | +#> |.....................| 0.03014 | 0.7557 | 0.8542 | 1.247 | +#> <span style='text-decoration: underline;'>|.....................| 1.093 | 1.098 |...........|...........|</span> +#> | F| Forward Diff. | 62.51 | 2.244 | 0.07930 | 0.2506 | +#> |.....................| -0.02305 | -56.21 | -16.10 | 0.4420 | +#> |.....................| -2.202 | 1.071 | 7.160 | -10.75 | +#> <span style='text-decoration: underline;'>|.....................| -8.705 | -9.292 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 18</span>| 491.85024 | 0.9902 | -1.006 | -0.9112 | -0.8949 | +#> |.....................| -0.8453 | -0.6983 | -0.8384 | -0.8699 | +#> |.....................| -0.8855 | -0.8798 | -0.8984 | -0.8374 | +#> <span style='text-decoration: underline;'>|.....................| -0.8459 | -0.8439 |...........|...........|</span> +#> | U| 491.85024 | 92.21 | -5.310 | -0.9440 | -0.1071 | +#> |.....................| 2.291 | 1.258 | 0.03085 | 1.159 | +#> |.....................| 0.03015 | 0.7556 | 0.8529 | 1.250 | +#> <span style='text-decoration: underline;'>|.....................| 1.095 | 1.100 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 491.85024</span> | 92.21 | 0.004944 | 0.2801 | 0.8985 | +#> |.....................| 9.888 | 1.258 | 0.03085 | 1.159 | +#> |.....................| 0.03015 | 0.7556 | 0.8529 | 1.250 | +#> <span style='text-decoration: underline;'>|.....................| 1.095 | 1.100 |...........|...........|</span> +#> | F| Forward Diff. | -64.39 | 2.132 | -0.2231 | 0.1507 | +#> |.....................| -0.3455 | -54.91 | -16.84 | 1.107 | +#> |.....................| -2.130 | 0.9153 | 6.361 | -10.56 | +#> <span style='text-decoration: underline;'>|.....................| -8.604 | -9.065 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 19</span>| 491.03181 | 0.9992 | -1.007 | -0.9112 | -0.8950 | +#> |.....................| -0.8452 | -0.6860 | -0.8347 | -0.8702 | +#> |.....................| -0.8850 | -0.8800 | -0.8997 | -0.8350 | +#> <span style='text-decoration: underline;'>|.....................| -0.8439 | -0.8419 |...........|...........|</span> +#> | U| 491.03181 | 93.04 | -5.310 | -0.9440 | -0.1071 | +#> |.....................| 2.291 | 1.265 | 0.03091 | 1.159 | +#> |.....................| 0.03015 | 0.7554 | 0.8517 | 1.253 | +#> <span style='text-decoration: underline;'>|.....................| 1.097 | 1.103 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 491.03181</span> | 93.04 | 0.004942 | 0.2801 | 0.8984 | +#> |.....................| 9.888 | 1.265 | 0.03091 | 1.159 | +#> |.....................| 0.03015 | 0.7554 | 0.8517 | 1.253 | +#> <span style='text-decoration: underline;'>|.....................| 1.097 | 1.103 |...........|...........|</span> +#> | F| Forward Diff. | 59.97 | 2.217 | 0.06954 | 0.2512 | +#> |.....................| -0.03854 | -54.10 | -15.21 | 0.3955 | +#> |.....................| -2.336 | 1.047 | 8.162 | -10.81 | +#> <span style='text-decoration: underline;'>|.....................| -8.706 | -9.233 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 20</span>| 490.24998 | 0.9904 | -1.007 | -0.9112 | -0.8950 | +#> |.....................| -0.8452 | -0.6737 | -0.8313 | -0.8703 | +#> |.....................| -0.8845 | -0.8803 | -0.9015 | -0.8325 | +#> <span style='text-decoration: underline;'>|.....................| -0.8419 | -0.8397 |...........|...........|</span> +#> | U| 490.24998 | 92.22 | -5.311 | -0.9440 | -0.1072 | +#> |.....................| 2.291 | 1.273 | 0.03096 | 1.159 | +#> |.....................| 0.03016 | 0.7552 | 0.8502 | 1.256 | +#> <span style='text-decoration: underline;'>|.....................| 1.099 | 1.105 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 490.24998</span> | 92.22 | 0.004939 | 0.2801 | 0.8984 | +#> |.....................| 9.889 | 1.273 | 0.03096 | 1.159 | +#> |.....................| 0.03016 | 0.7552 | 0.8502 | 1.256 | +#> <span style='text-decoration: underline;'>|.....................| 1.099 | 1.105 |...........|...........|</span> +#> | F| Forward Diff. | -61.40 | 2.114 | -0.2172 | 0.1580 | +#> |.....................| -0.3477 | -53.15 | -16.02 | 0.7982 | +#> |.....................| -2.483 | 0.7215 | 9.240 | -10.34 | +#> <span style='text-decoration: underline;'>|.....................| -8.435 | -8.843 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 21</span>| 489.45580 | 0.9991 | -1.008 | -0.9111 | -0.8951 | +#> |.....................| -0.8451 | -0.6614 | -0.8278 | -0.8705 | +#> |.....................| -0.8839 | -0.8804 | -0.9038 | -0.8300 | +#> <span style='text-decoration: underline;'>|.....................| -0.8398 | -0.8376 |...........|...........|</span> +#> | U| 489.4558 | 93.03 | -5.311 | -0.9439 | -0.1072 | +#> |.....................| 2.291 | 1.280 | 0.03101 | 1.159 | +#> |.....................| 0.03017 | 0.7551 | 0.8482 | 1.259 | +#> <span style='text-decoration: underline;'>|.....................| 1.102 | 1.107 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 489.4558</span> | 93.03 | 0.004937 | 0.2801 | 0.8983 | +#> |.....................| 9.889 | 1.280 | 0.03101 | 1.159 | +#> |.....................| 0.03017 | 0.7551 | 0.8482 | 1.259 | +#> <span style='text-decoration: underline;'>|.....................| 1.102 | 1.107 |...........|...........|</span> +#> | F| Forward Diff. | 58.20 | 2.191 | 0.07193 | 0.2543 | +#> |.....................| -0.04201 | -51.69 | -14.22 | 0.6968 | +#> |.....................| -2.088 | 1.024 | 8.024 | -10.34 | +#> <span style='text-decoration: underline;'>|.....................| -8.364 | -8.845 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 22</span>| 488.71859 | 0.9903 | -1.008 | -0.9111 | -0.8951 | +#> |.....................| -0.8451 | -0.6491 | -0.8245 | -0.8707 | +#> |.....................| -0.8833 | -0.8807 | -0.9059 | -0.8275 | +#> <span style='text-decoration: underline;'>|.....................| -0.8378 | -0.8354 |...........|...........|</span> +#> | U| 488.71859 | 92.21 | -5.312 | -0.9439 | -0.1073 | +#> |.....................| 2.291 | 1.287 | 0.03106 | 1.158 | +#> |.....................| 0.03018 | 0.7549 | 0.8463 | 1.262 | +#> <span style='text-decoration: underline;'>|.....................| 1.104 | 1.110 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 488.71859</span> | 92.21 | 0.004934 | 0.2801 | 0.8983 | +#> |.....................| 9.890 | 1.287 | 0.03106 | 1.158 | +#> |.....................| 0.03018 | 0.7549 | 0.8463 | 1.262 | +#> <span style='text-decoration: underline;'>|.....................| 1.104 | 1.110 |...........|...........|</span> +#> | F| Forward Diff. | -62.72 | 2.087 | -0.2158 | 0.1536 | +#> |.....................| -0.3560 | -50.59 | -14.96 | 1.289 | +#> |.....................| -2.066 | 0.8753 | 7.259 | -10.12 | +#> <span style='text-decoration: underline;'>|.....................| -8.247 | -8.604 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 23</span>| 487.91801 | 0.9987 | -1.009 | -0.9111 | -0.8952 | +#> |.....................| -0.8450 | -0.6366 | -0.8210 | -0.8711 | +#> |.....................| -0.8828 | -0.8809 | -0.9078 | -0.8248 | +#> <span style='text-decoration: underline;'>|.....................| -0.8356 | -0.8332 |...........|...........|</span> +#> | U| 487.91801 | 93.00 | -5.312 | -0.9439 | -0.1073 | +#> |.....................| 2.292 | 1.294 | 0.03112 | 1.158 | +#> |.....................| 0.03019 | 0.7547 | 0.8446 | 1.265 | +#> <span style='text-decoration: underline;'>|.....................| 1.106 | 1.112 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 487.91801</span> | 93.00 | 0.004931 | 0.2801 | 0.8982 | +#> |.....................| 9.890 | 1.294 | 0.03112 | 1.158 | +#> |.....................| 0.03019 | 0.7547 | 0.8446 | 1.265 | +#> <span style='text-decoration: underline;'>|.....................| 1.106 | 1.112 |...........|...........|</span> +#> | F| Forward Diff. | 52.73 | 2.162 | 0.07610 | 0.2481 | +#> |.....................| -0.05835 | -50.28 | -13.63 | 0.1991 | +#> |.....................| -2.681 | 0.6961 | 9.479 | -10.12 | +#> <span style='text-decoration: underline;'>|.....................| -8.180 | -8.607 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 24</span>| 487.19380 | 0.9906 | -1.009 | -0.9111 | -0.8952 | +#> |.....................| -0.8450 | -0.6240 | -0.8177 | -0.8712 | +#> |.....................| -0.8820 | -0.8811 | -0.9103 | -0.8222 | +#> <span style='text-decoration: underline;'>|.....................| -0.8335 | -0.8310 |...........|...........|</span> +#> | U| 487.1938 | 92.24 | -5.313 | -0.9439 | -0.1074 | +#> |.....................| 2.292 | 1.301 | 0.03116 | 1.158 | +#> |.....................| 0.03020 | 0.7546 | 0.8424 | 1.269 | +#> <span style='text-decoration: underline;'>|.....................| 1.108 | 1.114 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 487.1938</span> | 92.24 | 0.004929 | 0.2801 | 0.8982 | +#> |.....................| 9.891 | 1.301 | 0.03116 | 1.158 | +#> |.....................| 0.03020 | 0.7546 | 0.8424 | 1.269 | +#> <span style='text-decoration: underline;'>|.....................| 1.108 | 1.114 |...........|...........|</span> +#> | F| Forward Diff. | -58.70 | 2.065 | -0.2024 | 0.1592 | +#> |.....................| -0.3563 | -48.58 | -14.05 | 1.280 | +#> |.....................| -2.114 | 0.8980 | 5.535 | -9.882 | +#> <span style='text-decoration: underline;'>|.....................| -8.046 | -8.364 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 25</span>| 486.45861 | 0.9990 | -1.010 | -0.9111 | -0.8953 | +#> |.....................| -0.8449 | -0.6115 | -0.8144 | -0.8715 | +#> |.....................| -0.8813 | -0.8813 | -0.9121 | -0.8195 | +#> <span style='text-decoration: underline;'>|.....................| -0.8313 | -0.8287 |...........|...........|</span> +#> | U| 486.45861 | 93.03 | -5.313 | -0.9439 | -0.1074 | +#> |.....................| 2.292 | 1.309 | 0.03121 | 1.158 | +#> |.....................| 0.03021 | 0.7545 | 0.8409 | 1.272 | +#> <span style='text-decoration: underline;'>|.....................| 1.111 | 1.117 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 486.45861</span> | 93.03 | 0.004926 | 0.2801 | 0.8981 | +#> |.....................| 9.892 | 1.309 | 0.03121 | 1.158 | +#> |.....................| 0.03021 | 0.7545 | 0.8409 | 1.272 | +#> <span style='text-decoration: underline;'>|.....................| 1.111 | 1.117 |...........|...........|</span> +#> | F| Forward Diff. | 56.64 | 2.141 | 0.09518 | 0.2574 | +#> |.....................| -0.04938 | -48.45 | -12.81 | 0.1110 | +#> |.....................| -2.819 | 0.7463 | 7.804 | -9.858 | +#> <span style='text-decoration: underline;'>|.....................| -7.976 | -8.366 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 26</span>| 485.70463 | 0.9912 | -1.011 | -0.9111 | -0.8954 | +#> |.....................| -0.8448 | -0.5987 | -0.8113 | -0.8717 | +#> |.....................| -0.8805 | -0.8815 | -0.9139 | -0.8166 | +#> <span style='text-decoration: underline;'>|.....................| -0.8290 | -0.8264 |...........|...........|</span> +#> | U| 485.70463 | 92.30 | -5.314 | -0.9439 | -0.1075 | +#> |.....................| 2.292 | 1.316 | 0.03126 | 1.158 | +#> |.....................| 0.03022 | 0.7543 | 0.8393 | 1.275 | +#> <span style='text-decoration: underline;'>|.....................| 1.113 | 1.119 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 485.70463</span> | 92.30 | 0.004923 | 0.2801 | 0.8981 | +#> |.....................| 9.892 | 1.316 | 0.03126 | 1.158 | +#> |.....................| 0.03022 | 0.7543 | 0.8393 | 1.275 | +#> <span style='text-decoration: underline;'>|.....................| 1.113 | 1.119 |...........|...........|</span> +#> | F| Forward Diff. | -49.75 | 2.049 | -0.1896 | 0.1657 | +#> |.....................| -0.3394 | -47.06 | -13.27 | 0.8968 | +#> |.....................| -2.558 | 0.5259 | 7.006 | -9.655 | +#> <span style='text-decoration: underline;'>|.....................| -7.860 | -8.128 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 27</span>| 485.03383 | 0.9993 | -1.011 | -0.9111 | -0.8954 | +#> |.....................| -0.8447 | -0.5860 | -0.8081 | -0.8719 | +#> |.....................| -0.8796 | -0.8816 | -0.9160 | -0.8138 | +#> <span style='text-decoration: underline;'>|.....................| -0.8267 | -0.8240 |...........|...........|</span> +#> | U| 485.03383 | 93.05 | -5.315 | -0.9439 | -0.1076 | +#> |.....................| 2.292 | 1.323 | 0.03131 | 1.158 | +#> |.....................| 0.03024 | 0.7542 | 0.8375 | 1.279 | +#> <span style='text-decoration: underline;'>|.....................| 1.116 | 1.122 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 485.03383</span> | 93.05 | 0.004920 | 0.2801 | 0.8980 | +#> |.....................| 9.893 | 1.323 | 0.03131 | 1.158 | +#> |.....................| 0.03024 | 0.7542 | 0.8375 | 1.279 | +#> <span style='text-decoration: underline;'>|.....................| 1.116 | 1.122 |...........|...........|</span> +#> | F| Forward Diff. | 59.36 | 2.117 | 0.1128 | 0.2587 | +#> |.....................| -0.03694 | -45.49 | -11.65 | 0.8714 | +#> |.....................| -2.196 | 0.9711 | 7.208 | -9.629 | +#> <span style='text-decoration: underline;'>|.....................| -7.785 | -8.123 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 28</span>| 484.30050 | 0.9913 | -1.012 | -0.9111 | -0.8955 | +#> |.....................| -0.8447 | -0.5733 | -0.8052 | -0.8723 | +#> |.....................| -0.8788 | -0.8818 | -0.9181 | -0.8109 | +#> <span style='text-decoration: underline;'>|.....................| -0.8243 | -0.8216 |...........|...........|</span> +#> | U| 484.3005 | 92.30 | -5.315 | -0.9439 | -0.1077 | +#> |.....................| 2.292 | 1.331 | 0.03135 | 1.157 | +#> |.....................| 0.03025 | 0.7541 | 0.8357 | 1.282 | +#> <span style='text-decoration: underline;'>|.....................| 1.118 | 1.124 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 484.3005</span> | 92.30 | 0.004916 | 0.2801 | 0.8979 | +#> |.....................| 9.894 | 1.331 | 0.03135 | 1.157 | +#> |.....................| 0.03025 | 0.7541 | 0.8357 | 1.282 | +#> <span style='text-decoration: underline;'>|.....................| 1.118 | 1.124 |...........|...........|</span> +#> | F| Forward Diff. | -49.13 | 2.024 | -0.1788 | 0.1668 | +#> |.....................| -0.3408 | -44.74 | -12.30 | 1.348 | +#> |.....................| -2.137 | 0.7757 | 5.010 | -9.393 | +#> <span style='text-decoration: underline;'>|.....................| -7.651 | -7.866 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 29</span>| 483.61888 | 0.9988 | -1.013 | -0.9110 | -0.8956 | +#> |.....................| -0.8446 | -0.5603 | -0.8022 | -0.8729 | +#> |.....................| -0.8781 | -0.8821 | -0.9194 | -0.8078 | +#> <span style='text-decoration: underline;'>|.....................| -0.8218 | -0.8191 |...........|...........|</span> +#> | U| 483.61888 | 93.00 | -5.316 | -0.9438 | -0.1077 | +#> |.....................| 2.292 | 1.338 | 0.03140 | 1.157 | +#> |.....................| 0.03026 | 0.7539 | 0.8345 | 1.286 | +#> <span style='text-decoration: underline;'>|.....................| 1.121 | 1.127 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 483.61888</span> | 93.00 | 0.004913 | 0.2801 | 0.8979 | +#> |.....................| 9.895 | 1.338 | 0.03140 | 1.157 | +#> |.....................| 0.03026 | 0.7539 | 0.8345 | 1.286 | +#> <span style='text-decoration: underline;'>|.....................| 1.121 | 1.127 |...........|...........|</span> +#> | F| Forward Diff. | 51.77 | 2.082 | 0.08733 | 0.2462 | +#> |.....................| -0.07383 | -44.60 | -11.22 | 0.3023 | +#> |.....................| -2.722 | 0.5489 | 8.672 | -9.371 | +#> <span style='text-decoration: underline;'>|.....................| -7.562 | -7.848 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 30</span>| 482.91165 | 0.9915 | -1.013 | -0.9110 | -0.8957 | +#> |.....................| -0.8445 | -0.5473 | -0.7995 | -0.8732 | +#> |.....................| -0.8770 | -0.8822 | -0.9219 | -0.8047 | +#> <span style='text-decoration: underline;'>|.....................| -0.8192 | -0.8165 |...........|...........|</span> +#> | U| 482.91165 | 92.33 | -5.317 | -0.9438 | -0.1078 | +#> |.....................| 2.292 | 1.346 | 0.03144 | 1.157 | +#> |.....................| 0.03027 | 0.7538 | 0.8323 | 1.290 | +#> <span style='text-decoration: underline;'>|.....................| 1.124 | 1.130 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 482.91165</span> | 92.33 | 0.004909 | 0.2801 | 0.8978 | +#> |.....................| 9.895 | 1.346 | 0.03144 | 1.157 | +#> |.....................| 0.03027 | 0.7538 | 0.8323 | 1.290 | +#> <span style='text-decoration: underline;'>|.....................| 1.124 | 1.130 |...........|...........|</span> +#> | F| Forward Diff. | -45.50 | 2.003 | -0.1660 | 0.1702 | +#> |.....................| -0.3374 | -43.33 | -11.63 | 0.9930 | +#> |.....................| -2.511 | 0.4656 | 7.949 | -9.128 | +#> <span style='text-decoration: underline;'>|.....................| -7.427 | -7.608 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 31</span>| 482.28997 | 0.9991 | -1.014 | -0.9110 | -0.8957 | +#> |.....................| -0.8444 | -0.5346 | -0.7968 | -0.8735 | +#> |.....................| -0.8759 | -0.8822 | -0.9253 | -0.8017 | +#> <span style='text-decoration: underline;'>|.....................| -0.8168 | -0.8141 |...........|...........|</span> +#> | U| 482.28997 | 93.03 | -5.317 | -0.9438 | -0.1079 | +#> |.....................| 2.292 | 1.353 | 0.03148 | 1.157 | +#> |.....................| 0.03029 | 0.7538 | 0.8294 | 1.293 | +#> <span style='text-decoration: underline;'>|.....................| 1.126 | 1.132 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 482.28997</span> | 93.03 | 0.004906 | 0.2801 | 0.8977 | +#> |.....................| 9.896 | 1.353 | 0.03148 | 1.157 | +#> |.....................| 0.03029 | 0.7538 | 0.8294 | 1.293 | +#> <span style='text-decoration: underline;'>|.....................| 1.126 | 1.132 |...........|...........|</span> +#> | F| Forward Diff. | 55.95 | 2.054 | 0.1106 | 0.2465 | +#> |.....................| -0.05340 | -42.18 | -10.21 | 0.8261 | +#> |.....................| -2.234 | 0.9104 | 5.096 | -9.114 | +#> <span style='text-decoration: underline;'>|.....................| -7.334 | -7.590 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 32</span>| 481.60550 | 0.9915 | -1.015 | -0.9110 | -0.8958 | +#> |.....................| -0.8443 | -0.5217 | -0.7945 | -0.8740 | +#> |.....................| -0.8749 | -0.8824 | -0.9274 | -0.7984 | +#> <span style='text-decoration: underline;'>|.....................| -0.8142 | -0.8115 |...........|...........|</span> +#> | U| 481.6055 | 92.33 | -5.318 | -0.9438 | -0.1080 | +#> |.....................| 2.292 | 1.361 | 0.03151 | 1.156 | +#> |.....................| 0.03031 | 0.7536 | 0.8276 | 1.297 | +#> <span style='text-decoration: underline;'>|.....................| 1.129 | 1.135 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 481.6055</span> | 92.33 | 0.004902 | 0.2801 | 0.8977 | +#> |.....................| 9.897 | 1.361 | 0.03151 | 1.156 | +#> |.....................| 0.03031 | 0.7536 | 0.8276 | 1.297 | +#> <span style='text-decoration: underline;'>|.....................| 1.129 | 1.135 |...........|...........|</span> +#> | F| Forward Diff. | -45.82 | 1.973 | -0.1624 | 0.1674 | +#> |.....................| -0.3387 | -41.15 | -10.74 | 1.410 | +#> |.....................| -2.130 | 0.6088 | 4.422 | -8.852 | +#> <span style='text-decoration: underline;'>|.....................| -7.186 | -7.335 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 33</span>| 480.97343 | 0.9986 | -1.016 | -0.9110 | -0.8959 | +#> |.....................| -0.8442 | -0.5084 | -0.7922 | -0.8748 | +#> |.....................| -0.8740 | -0.8826 | -0.9278 | -0.7950 | +#> <span style='text-decoration: underline;'>|.....................| -0.8114 | -0.8088 |...........|...........|</span> +#> | U| 480.97343 | 92.98 | -5.319 | -0.9438 | -0.1081 | +#> |.....................| 2.292 | 1.368 | 0.03155 | 1.156 | +#> |.....................| 0.03032 | 0.7534 | 0.8272 | 1.301 | +#> <span style='text-decoration: underline;'>|.....................| 1.132 | 1.138 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 480.97343</span> | 92.98 | 0.004897 | 0.2801 | 0.8976 | +#> |.....................| 9.898 | 1.368 | 0.03155 | 1.156 | +#> |.....................| 0.03032 | 0.7534 | 0.8272 | 1.301 | +#> <span style='text-decoration: underline;'>|.....................| 1.132 | 1.138 |...........|...........|</span> +#> | F| Forward Diff. | 47.76 | 2.024 | 0.09167 | 0.2404 | +#> |.....................| -0.07393 | -40.22 | -9.470 | 1.031 | +#> |.....................| -2.098 | 0.8752 | 6.346 | -8.797 | +#> <span style='text-decoration: underline;'>|.....................| -7.089 | -7.296 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 34</span>| 480.33235 | 0.9916 | -1.017 | -0.9110 | -0.8960 | +#> |.....................| -0.8441 | -0.4952 | -0.7903 | -0.8757 | +#> |.....................| -0.8731 | -0.8830 | -0.9294 | -0.7914 | +#> <span style='text-decoration: underline;'>|.....................| -0.8086 | -0.8060 |...........|...........|</span> +#> | U| 480.33235 | 92.33 | -5.320 | -0.9438 | -0.1082 | +#> |.....................| 2.292 | 1.376 | 0.03158 | 1.155 | +#> |.....................| 0.03033 | 0.7532 | 0.8258 | 1.306 | +#> <span style='text-decoration: underline;'>|.....................| 1.135 | 1.141 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 480.33235</span> | 92.33 | 0.004893 | 0.2801 | 0.8975 | +#> |.....................| 9.899 | 1.376 | 0.03158 | 1.155 | +#> |.....................| 0.03033 | 0.7532 | 0.8258 | 1.306 | +#> <span style='text-decoration: underline;'>|.....................| 1.135 | 1.141 |...........|...........|</span> +#> | F| Forward Diff. | -44.82 | 1.956 | -0.1640 | 0.1653 | +#> |.....................| -0.3374 | -39.36 | -9.982 | 1.432 | +#> |.....................| -2.136 | 0.6770 | 5.747 | -8.552 | +#> <span style='text-decoration: underline;'>|.....................| -6.943 | -7.038 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 35</span>| 479.71253 | 0.9984 | -1.018 | -0.9110 | -0.8961 | +#> |.....................| -0.8439 | -0.4821 | -0.7885 | -0.8768 | +#> |.....................| -0.8721 | -0.8833 | -0.9319 | -0.7879 | +#> <span style='text-decoration: underline;'>|.....................| -0.8057 | -0.8033 |...........|...........|</span> +#> | U| 479.71253 | 92.97 | -5.321 | -0.9438 | -0.1083 | +#> |.....................| 2.293 | 1.384 | 0.03160 | 1.155 | +#> |.....................| 0.03035 | 0.7529 | 0.8236 | 1.310 | +#> <span style='text-decoration: underline;'>|.....................| 1.138 | 1.144 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 479.71253</span> | 92.97 | 0.004888 | 0.2801 | 0.8974 | +#> |.....................| 9.901 | 1.384 | 0.03160 | 1.155 | +#> |.....................| 0.03035 | 0.7529 | 0.8236 | 1.310 | +#> <span style='text-decoration: underline;'>|.....................| 1.138 | 1.144 |...........|...........|</span> +#> | F| Forward Diff. | 45.27 | 2.001 | 0.09802 | 0.2411 | +#> |.....................| -0.07361 | -39.48 | -9.147 | 0.2467 | +#> |.....................| -2.886 | 0.4583 | 7.836 | -8.475 | +#> <span style='text-decoration: underline;'>|.....................| -6.831 | -7.001 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 36</span>| 479.08241 | 0.9920 | -1.019 | -0.9110 | -0.8962 | +#> |.....................| -0.8438 | -0.4691 | -0.7871 | -0.8771 | +#> |.....................| -0.8704 | -0.8833 | -0.9359 | -0.7844 | +#> <span style='text-decoration: underline;'>|.....................| -0.8029 | -0.8006 |...........|...........|</span> +#> | U| 479.08241 | 92.37 | -5.322 | -0.9438 | -0.1084 | +#> |.....................| 2.293 | 1.391 | 0.03163 | 1.155 | +#> |.....................| 0.03037 | 0.7529 | 0.8201 | 1.314 | +#> <span style='text-decoration: underline;'>|.....................| 1.141 | 1.147 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 479.08241</span> | 92.37 | 0.004883 | 0.2801 | 0.8973 | +#> |.....................| 9.902 | 1.391 | 0.03163 | 1.155 | +#> |.....................| 0.03037 | 0.7529 | 0.8201 | 1.314 | +#> <span style='text-decoration: underline;'>|.....................| 1.141 | 1.147 |...........|...........|</span> +#> | F| Forward Diff. | -39.48 | 1.926 | -0.1378 | 0.1752 | +#> |.....................| -0.3206 | -38.45 | -9.498 | 0.8453 | +#> |.....................| -2.699 | 0.3871 | 5.589 | -8.242 | +#> <span style='text-decoration: underline;'>|.....................| -6.674 | -6.762 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 37</span>| 478.53604 | 0.9990 | -1.019 | -0.9110 | -0.8964 | +#> |.....................| -0.8437 | -0.4561 | -0.7854 | -0.8772 | +#> |.....................| -0.8684 | -0.8832 | -0.9392 | -0.7811 | +#> <span style='text-decoration: underline;'>|.....................| -0.8002 | -0.7981 |...........|...........|</span> +#> | U| 478.53604 | 93.02 | -5.323 | -0.9438 | -0.1085 | +#> |.....................| 2.293 | 1.399 | 0.03165 | 1.155 | +#> |.....................| 0.03040 | 0.7530 | 0.8172 | 1.318 | +#> <span style='text-decoration: underline;'>|.....................| 1.144 | 1.150 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 478.53604</span> | 93.02 | 0.004879 | 0.2801 | 0.8972 | +#> |.....................| 9.903 | 1.399 | 0.03165 | 1.155 | +#> |.....................| 0.03040 | 0.7530 | 0.8172 | 1.318 | +#> <span style='text-decoration: underline;'>|.....................| 1.144 | 1.150 |...........|...........|</span> +#> | F| Forward Diff. | 52.06 | 1.969 | 0.1359 | 0.2508 | +#> |.....................| -0.04337 | -37.95 | -8.435 | 0.2680 | +#> |.....................| -2.930 | 0.5186 | 5.955 | -8.188 | +#> <span style='text-decoration: underline;'>|.....................| -6.576 | -6.741 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 38</span>| 477.90297 | 0.9924 | -1.021 | -0.9111 | -0.8965 | +#> |.....................| -0.8436 | -0.4428 | -0.7846 | -0.8771 | +#> |.....................| -0.8659 | -0.8830 | -0.9416 | -0.7776 | +#> <span style='text-decoration: underline;'>|.....................| -0.7975 | -0.7955 |...........|...........|</span> +#> | U| 477.90297 | 92.41 | -5.324 | -0.9439 | -0.1086 | +#> |.....................| 2.293 | 1.406 | 0.03166 | 1.155 | +#> |.....................| 0.03044 | 0.7531 | 0.8151 | 1.323 | +#> <span style='text-decoration: underline;'>|.....................| 1.147 | 1.152 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 477.90297</span> | 92.41 | 0.004873 | 0.2801 | 0.8971 | +#> |.....................| 9.904 | 1.406 | 0.03166 | 1.155 | +#> |.....................| 0.03044 | 0.7531 | 0.8151 | 1.323 | +#> <span style='text-decoration: underline;'>|.....................| 1.147 | 1.152 |...........|...........|</span> +#> | F| Forward Diff. | -35.48 | 1.900 | -0.1171 | 0.1805 | +#> |.....................| -0.3013 | -36.12 | -8.554 | 1.521 | +#> |.....................| -2.082 | 0.5139 | 5.057 | -7.934 | +#> <span style='text-decoration: underline;'>|.....................| -6.421 | -6.501 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 39</span>| 477.39487 | 0.9991 | -1.022 | -0.9111 | -0.8966 | +#> |.....................| -0.8434 | -0.4296 | -0.7836 | -0.8780 | +#> |.....................| -0.8642 | -0.8831 | -0.9436 | -0.7740 | +#> <span style='text-decoration: underline;'>|.....................| -0.7946 | -0.7928 |...........|...........|</span> +#> | U| 477.39487 | 93.04 | -5.325 | -0.9439 | -0.1088 | +#> |.....................| 2.293 | 1.414 | 0.03168 | 1.154 | +#> |.....................| 0.03047 | 0.7531 | 0.8134 | 1.327 | +#> <span style='text-decoration: underline;'>|.....................| 1.150 | 1.155 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 477.39487</span> | 93.04 | 0.004868 | 0.2801 | 0.8969 | +#> |.....................| 9.906 | 1.414 | 0.03168 | 1.154 | +#> |.....................| 0.03047 | 0.7531 | 0.8134 | 1.327 | +#> <span style='text-decoration: underline;'>|.....................| 1.150 | 1.155 |...........|...........|</span> +#> | F| Forward Diff. | 53.22 | 1.947 | 0.1564 | 0.2562 | +#> |.....................| -0.02756 | -35.38 | -7.440 | 1.129 | +#> |.....................| -2.109 | 0.8531 | 5.389 | -7.888 | +#> <span style='text-decoration: underline;'>|.....................| -6.311 | -6.462 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 40</span>| 476.77835 | 0.9927 | -1.023 | -0.9112 | -0.8968 | +#> |.....................| -0.8433 | -0.4165 | -0.7840 | -0.8801 | +#> |.....................| -0.8630 | -0.8835 | -0.9455 | -0.7699 | +#> <span style='text-decoration: underline;'>|.....................| -0.7913 | -0.7897 |...........|...........|</span> +#> | U| 476.77835 | 92.44 | -5.326 | -0.9439 | -0.1090 | +#> |.....................| 2.293 | 1.422 | 0.03167 | 1.153 | +#> |.....................| 0.03048 | 0.7527 | 0.8117 | 1.332 | +#> <span style='text-decoration: underline;'>|.....................| 1.153 | 1.159 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 476.77835</span> | 92.44 | 0.004861 | 0.2801 | 0.8968 | +#> |.....................| 9.907 | 1.422 | 0.03167 | 1.153 | +#> |.....................| 0.03048 | 0.7527 | 0.8117 | 1.332 | +#> <span style='text-decoration: underline;'>|.....................| 1.153 | 1.159 |...........|...........|</span> +#> | F| Forward Diff. | -31.48 | 1.878 | -0.09989 | 0.1868 | +#> |.....................| -0.2862 | -34.69 | -7.934 | 1.303 | +#> |.....................| -2.230 | 0.5238 | 3.299 | -7.623 | +#> <span style='text-decoration: underline;'>|.....................| -6.137 | -6.207 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 41</span>| 476.29140 | 0.9988 | -1.024 | -0.9112 | -0.8970 | +#> |.....................| -0.8432 | -0.4030 | -0.7837 | -0.8817 | +#> |.....................| -0.8615 | -0.8839 | -0.9453 | -0.7660 | +#> <span style='text-decoration: underline;'>|.....................| -0.7883 | -0.7869 |...........|...........|</span> +#> | U| 476.2914 | 93.01 | -5.328 | -0.9440 | -0.1091 | +#> |.....................| 2.293 | 1.430 | 0.03168 | 1.152 | +#> |.....................| 0.03051 | 0.7524 | 0.8119 | 1.337 | +#> <span style='text-decoration: underline;'>|.....................| 1.157 | 1.162 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 476.2914</span> | 93.01 | 0.004855 | 0.2801 | 0.8966 | +#> |.....................| 9.909 | 1.430 | 0.03168 | 1.152 | +#> |.....................| 0.03051 | 0.7524 | 0.8119 | 1.337 | +#> <span style='text-decoration: underline;'>|.....................| 1.157 | 1.162 |...........|...........|</span> +#> | F| Forward Diff. | 48.73 | 1.930 | 0.1514 | 0.2545 | +#> |.....................| -0.03521 | -34.01 | -6.934 | 1.004 | +#> |.....................| -2.133 | 0.7968 | 5.252 | -7.528 | +#> <span style='text-decoration: underline;'>|.....................| -6.021 | -6.137 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 42</span>| 475.72593 | 0.9927 | -1.026 | -0.9113 | -0.8972 | +#> |.....................| -0.8430 | -0.3897 | -0.7848 | -0.8834 | +#> |.....................| -0.8598 | -0.8844 | -0.9451 | -0.7619 | +#> <span style='text-decoration: underline;'>|.....................| -0.7851 | -0.7840 |...........|...........|</span> +#> | U| 475.72593 | 92.44 | -5.329 | -0.9441 | -0.1094 | +#> |.....................| 2.294 | 1.437 | 0.03166 | 1.151 | +#> |.....................| 0.03053 | 0.7521 | 0.8121 | 1.342 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 | 1.165 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 475.72593</span> | 92.44 | 0.004847 | 0.2801 | 0.8964 | +#> |.....................| 9.910 | 1.437 | 0.03166 | 1.151 | +#> |.....................| 0.03053 | 0.7521 | 0.8121 | 1.342 | +#> <span style='text-decoration: underline;'>|.....................| 1.160 | 1.165 |...........|...........|</span> +#> | F| Forward Diff. | -31.62 | 1.868 | -0.1026 | 0.1833 | +#> |.....................| -0.2884 | -33.06 | -7.282 | 1.547 | +#> |.....................| -2.194 | 0.5347 | 3.320 | -7.249 | +#> <span style='text-decoration: underline;'>|.....................| -5.852 | -5.889 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 43</span>| 475.25217 | 0.9986 | -1.027 | -0.9113 | -0.8974 | +#> |.....................| -0.8428 | -0.3762 | -0.7856 | -0.8854 | +#> |.....................| -0.8580 | -0.8849 | -0.9453 | -0.7580 | +#> <span style='text-decoration: underline;'>|.....................| -0.7821 | -0.7812 |...........|...........|</span> +#> | U| 475.25217 | 92.99 | -5.331 | -0.9441 | -0.1096 | +#> |.....................| 2.294 | 1.445 | 0.03165 | 1.150 | +#> |.....................| 0.03056 | 0.7517 | 0.8119 | 1.346 | +#> <span style='text-decoration: underline;'>|.....................| 1.163 | 1.168 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 475.25217</span> | 92.99 | 0.004840 | 0.2801 | 0.8962 | +#> |.....................| 9.912 | 1.445 | 0.03165 | 1.150 | +#> |.....................| 0.03056 | 0.7517 | 0.8119 | 1.346 | +#> <span style='text-decoration: underline;'>|.....................| 1.163 | 1.168 |...........|...........|</span> +#> | F| Forward Diff. | 45.01 | 1.918 | 0.1424 | 0.2472 | +#> |.....................| -0.04139 | -32.61 | -6.424 | 0.9161 | +#> |.....................| -2.151 | 0.6354 | 5.209 | -7.174 | +#> <span style='text-decoration: underline;'>|.....................| -5.746 | -5.822 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 44</span>| 474.72079 | 0.9929 | -1.029 | -0.9114 | -0.8977 | +#> |.....................| -0.8427 | -0.3629 | -0.7879 | -0.8876 | +#> |.....................| -0.8559 | -0.8852 | -0.9458 | -0.7541 | +#> <span style='text-decoration: underline;'>|.....................| -0.7790 | -0.7785 |...........|...........|</span> +#> | U| 474.72079 | 92.46 | -5.333 | -0.9442 | -0.1098 | +#> |.....................| 2.294 | 1.453 | 0.03161 | 1.149 | +#> |.....................| 0.03059 | 0.7515 | 0.8114 | 1.351 | +#> <span style='text-decoration: underline;'>|.....................| 1.167 | 1.171 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 474.72079</span> | 92.46 | 0.004831 | 0.2800 | 0.8960 | +#> |.....................| 9.913 | 1.453 | 0.03161 | 1.149 | +#> |.....................| 0.03059 | 0.7515 | 0.8114 | 1.351 | +#> <span style='text-decoration: underline;'>|.....................| 1.167 | 1.171 |...........|...........|</span> +#> | F| Forward Diff. | -29.98 | 1.856 | -0.09377 | 0.1852 | +#> |.....................| -0.2753 | -32.15 | -6.889 | 1.072 | +#> |.....................| -2.266 | 0.4091 | 3.274 | -6.876 | +#> <span style='text-decoration: underline;'>|.....................| -5.564 | -5.585 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 45</span>| 474.26379 | 0.9985 | -1.031 | -0.9115 | -0.8979 | +#> |.....................| -0.8425 | -0.3491 | -0.7895 | -0.8887 | +#> |.....................| -0.8536 | -0.8852 | -0.9464 | -0.7506 | +#> <span style='text-decoration: underline;'>|.....................| -0.7762 | -0.7761 |...........|...........|</span> +#> | U| 474.26379 | 92.98 | -5.335 | -0.9443 | -0.1101 | +#> |.....................| 2.294 | 1.461 | 0.03159 | 1.148 | +#> |.....................| 0.03063 | 0.7515 | 0.8109 | 1.355 | +#> <span style='text-decoration: underline;'>|.....................| 1.170 | 1.173 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 474.26379</span> | 92.98 | 0.004822 | 0.2800 | 0.8958 | +#> |.....................| 9.915 | 1.461 | 0.03159 | 1.148 | +#> |.....................| 0.03063 | 0.7515 | 0.8109 | 1.355 | +#> <span style='text-decoration: underline;'>|.....................| 1.170 | 1.173 |...........|...........|</span> +#> | F| Forward Diff. | 42.78 | 1.902 | 0.1464 | 0.2388 | +#> |.....................| -0.03417 | -31.28 | -5.931 | 0.8375 | +#> |.....................| -2.202 | 0.7305 | 5.128 | -6.841 | +#> <span style='text-decoration: underline;'>|.....................| -5.479 | -5.554 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 46</span>| 473.76810 | 0.9929 | -1.033 | -0.9117 | -0.8982 | +#> |.....................| -0.8424 | -0.3358 | -0.7928 | -0.8897 | +#> |.....................| -0.8508 | -0.8855 | -0.9473 | -0.7471 | +#> <span style='text-decoration: underline;'>|.....................| -0.7734 | -0.7737 |...........|...........|</span> +#> | U| 473.7681 | 92.46 | -5.337 | -0.9444 | -0.1104 | +#> |.....................| 2.294 | 1.469 | 0.03154 | 1.147 | +#> |.....................| 0.03067 | 0.7512 | 0.8101 | 1.360 | +#> <span style='text-decoration: underline;'>|.....................| 1.173 | 1.176 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 473.7681</span> | 92.46 | 0.004812 | 0.2800 | 0.8955 | +#> |.....................| 9.917 | 1.469 | 0.03154 | 1.147 | +#> |.....................| 0.03067 | 0.7512 | 0.8101 | 1.360 | +#> <span style='text-decoration: underline;'>|.....................| 1.173 | 1.176 |...........|...........|</span> +#> | F| Forward Diff. | -30.83 | 1.832 | -0.1003 | 0.1743 | +#> |.....................| -0.2686 | -30.77 | -6.362 | 1.107 | +#> |.....................| -2.234 | 0.4249 | 4.678 | -6.593 | +#> <span style='text-decoration: underline;'>|.....................| -5.329 | -5.340 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 47</span>| 473.32508 | 0.9983 | -1.035 | -0.9117 | -0.8984 | +#> |.....................| -0.8422 | -0.3229 | -0.7959 | -0.8909 | +#> |.....................| -0.8482 | -0.8859 | -0.9520 | -0.7438 | +#> <span style='text-decoration: underline;'>|.....................| -0.7708 | -0.7715 |...........|...........|</span> +#> | U| 473.32508 | 92.96 | -5.339 | -0.9445 | -0.1106 | +#> |.....................| 2.294 | 1.476 | 0.03149 | 1.147 | +#> |.....................| 0.03071 | 0.7509 | 0.8061 | 1.364 | +#> <span style='text-decoration: underline;'>|.....................| 1.175 | 1.178 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 473.32508</span> | 92.96 | 0.004802 | 0.2800 | 0.8953 | +#> |.....................| 9.918 | 1.476 | 0.03149 | 1.147 | +#> |.....................| 0.03071 | 0.7509 | 0.8061 | 1.364 | +#> <span style='text-decoration: underline;'>|.....................| 1.175 | 1.178 |...........|...........|</span> +#> | F| Forward Diff. | 38.19 | 1.865 | 0.1554 | 0.2504 | +#> |.....................| -0.02116 | -30.15 | -5.522 | 0.8218 | +#> |.....................| -2.215 | 0.6878 | 4.772 | -6.537 | +#> <span style='text-decoration: underline;'>|.....................| -5.232 | -5.315 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 48</span>| 472.87290 | 0.9930 | -1.038 | -0.9119 | -0.8988 | +#> |.....................| -0.8421 | -0.3103 | -0.8002 | -0.8921 | +#> |.....................| -0.8451 | -0.8864 | -0.9564 | -0.7407 | +#> <span style='text-decoration: underline;'>|.....................| -0.7684 | -0.7695 |...........|...........|</span> +#> | U| 472.8729 | 92.47 | -5.341 | -0.9447 | -0.1109 | +#> |.....................| 2.294 | 1.483 | 0.03143 | 1.146 | +#> |.....................| 0.03075 | 0.7506 | 0.8022 | 1.367 | +#> <span style='text-decoration: underline;'>|.....................| 1.178 | 1.180 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 472.8729</span> | 92.47 | 0.004791 | 0.2800 | 0.8950 | +#> |.....................| 9.919 | 1.483 | 0.03143 | 1.146 | +#> |.....................| 0.03075 | 0.7506 | 0.8022 | 1.367 | +#> <span style='text-decoration: underline;'>|.....................| 1.178 | 1.180 |...........|...........|</span> +#> | F| Forward Diff. | -31.43 | 1.786 | -0.07853 | 0.1828 | +#> |.....................| -0.2451 | -29.69 | -5.937 | 1.129 | +#> |.....................| -2.237 | 0.5225 | 4.143 | -6.356 | +#> <span style='text-decoration: underline;'>|.....................| -5.097 | -5.139 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 49</span>| 472.45068 | 0.9981 | -1.040 | -0.9121 | -0.8991 | +#> |.....................| -0.8421 | -0.2974 | -0.8046 | -0.8935 | +#> |.....................| -0.8420 | -0.8871 | -0.9597 | -0.7375 | +#> <span style='text-decoration: underline;'>|.....................| -0.7660 | -0.7674 |...........|...........|</span> +#> | U| 472.45068 | 92.94 | -5.343 | -0.9449 | -0.1112 | +#> |.....................| 2.294 | 1.491 | 0.03136 | 1.145 | +#> |.....................| 0.03080 | 0.7500 | 0.7993 | 1.371 | +#> <span style='text-decoration: underline;'>|.....................| 1.180 | 1.183 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 472.45068</span> | 92.94 | 0.004780 | 0.2799 | 0.8947 | +#> |.....................| 9.919 | 1.491 | 0.03136 | 1.145 | +#> |.....................| 0.03080 | 0.7500 | 0.7993 | 1.371 | +#> <span style='text-decoration: underline;'>|.....................| 1.180 | 1.183 |...........|...........|</span> +#> | F| Forward Diff. | 34.69 | 1.825 | 0.1712 | 0.2558 | +#> |.....................| 0.0008262 | -30.15 | -5.461 | 0.02383 | +#> |.....................| -3.011 | 0.3236 | 4.609 | -6.242 | +#> <span style='text-decoration: underline;'>|.....................| -4.997 | -5.107 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 50</span>| 472.02915 | 0.9936 | -1.042 | -0.9125 | -0.8995 | +#> |.....................| -0.8422 | -0.2847 | -0.8092 | -0.8923 | +#> |.....................| -0.8364 | -0.8868 | -0.9626 | -0.7353 | +#> <span style='text-decoration: underline;'>|.....................| -0.7644 | -0.7660 |...........|...........|</span> +#> | U| 472.02915 | 92.52 | -5.345 | -0.9452 | -0.1116 | +#> |.....................| 2.294 | 1.498 | 0.03129 | 1.146 | +#> |.....................| 0.03088 | 0.7503 | 0.7968 | 1.374 | +#> <span style='text-decoration: underline;'>|.....................| 1.182 | 1.184 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 472.02915</span> | 92.52 | 0.004770 | 0.2799 | 0.8944 | +#> |.....................| 9.918 | 1.498 | 0.03129 | 1.146 | +#> |.....................| 0.03088 | 0.7503 | 0.7968 | 1.374 | +#> <span style='text-decoration: underline;'>|.....................| 1.182 | 1.184 |...........|...........|</span> +#> | F| Forward Diff. | -26.29 | 1.758 | -0.04843 | 0.1910 | +#> |.....................| -0.1997 | -28.69 | -5.506 | 1.097 | +#> |.....................| -2.285 | 0.4947 | 2.297 | -6.079 | +#> <span style='text-decoration: underline;'>|.....................| -4.892 | -4.970 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 51</span>| 471.69520 | 0.9992 | -1.044 | -0.9127 | -0.8998 | +#> |.....................| -0.8423 | -0.2715 | -0.8127 | -0.8918 | +#> |.....................| -0.8317 | -0.8866 | -0.9606 | -0.7330 | +#> <span style='text-decoration: underline;'>|.....................| -0.7627 | -0.7642 |...........|...........|</span> +#> | U| 471.6952 | 93.04 | -5.347 | -0.9454 | -0.1120 | +#> |.....................| 2.294 | 1.506 | 0.03124 | 1.146 | +#> |.....................| 0.03096 | 0.7504 | 0.7985 | 1.377 | +#> <span style='text-decoration: underline;'>|.....................| 1.184 | 1.186 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 471.6952</span> | 93.04 | 0.004761 | 0.2798 | 0.8941 | +#> |.....................| 9.917 | 1.506 | 0.03124 | 1.146 | +#> |.....................| 0.03096 | 0.7504 | 0.7985 | 1.377 | +#> <span style='text-decoration: underline;'>|.....................| 1.184 | 1.186 |...........|...........|</span> +#> | F| Forward Diff. | 46.70 | 1.815 | 0.2108 | 0.2607 | +#> |.....................| 0.05766 | -27.95 | -4.639 | 0.9041 | +#> |.....................| -2.201 | 0.7590 | 4.326 | -6.078 | +#> <span style='text-decoration: underline;'>|.....................| -4.851 | -4.972 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 52</span>| 471.30240 | 0.9939 | -1.046 | -0.9131 | -0.9002 | +#> |.....................| -0.8425 | -0.2596 | -0.8187 | -0.8939 | +#> |.....................| -0.8280 | -0.8876 | -0.9571 | -0.7302 | +#> <span style='text-decoration: underline;'>|.....................| -0.7606 | -0.7622 |...........|...........|</span> +#> | U| 471.3024 | 92.55 | -5.350 | -0.9458 | -0.1124 | +#> |.....................| 2.294 | 1.513 | 0.03115 | 1.145 | +#> |.....................| 0.03101 | 0.7497 | 0.8016 | 1.380 | +#> <span style='text-decoration: underline;'>|.....................| 1.186 | 1.188 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 471.3024</span> | 92.55 | 0.004750 | 0.2797 | 0.8937 | +#> |.....................| 9.915 | 1.513 | 0.03115 | 1.145 | +#> |.....................| 0.03101 | 0.7497 | 0.8016 | 1.380 | +#> <span style='text-decoration: underline;'>|.....................| 1.186 | 1.188 |...........|...........|</span> +#> | F| Forward Diff. | -23.61 | 1.763 | -0.06060 | 0.1836 | +#> |.....................| -0.1912 | -28.31 | -5.279 | 0.6597 | +#> |.....................| -2.739 | 0.2048 | 5.941 | -5.864 | +#> <span style='text-decoration: underline;'>|.....................| -4.747 | -4.787 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 53</span>| 470.94339 | 0.9985 | -1.048 | -0.9133 | -0.9006 | +#> |.....................| -0.8426 | -0.2476 | -0.8235 | -0.8946 | +#> |.....................| -0.8237 | -0.8877 | -0.9629 | -0.7278 | +#> <span style='text-decoration: underline;'>|.....................| -0.7587 | -0.7604 |...........|...........|</span> +#> | U| 470.94339 | 92.98 | -5.352 | -0.9460 | -0.1127 | +#> |.....................| 2.294 | 1.520 | 0.03108 | 1.145 | +#> |.....................| 0.03108 | 0.7496 | 0.7965 | 1.383 | +#> <span style='text-decoration: underline;'>|.....................| 1.188 | 1.190 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 470.94339</span> | 92.98 | 0.004740 | 0.2797 | 0.8934 | +#> |.....................| 9.914 | 1.520 | 0.03108 | 1.145 | +#> |.....................| 0.03108 | 0.7496 | 0.7965 | 1.383 | +#> <span style='text-decoration: underline;'>|.....................| 1.188 | 1.190 |...........|...........|</span> +#> | F| Forward Diff. | 36.04 | 1.791 | 0.1836 | 0.2544 | +#> |.....................| 0.04274 | -27.03 | -4.370 | 0.9159 | +#> |.....................| -2.217 | 0.6791 | 4.141 | -5.840 | +#> <span style='text-decoration: underline;'>|.....................| -4.667 | -4.764 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 54</span>| 470.60274 | 0.9931 | -1.051 | -0.9136 | -0.9010 | +#> |.....................| -0.8428 | -0.2366 | -0.8300 | -0.8957 | +#> |.....................| -0.8190 | -0.8879 | -0.9681 | -0.7257 | +#> <span style='text-decoration: underline;'>|.....................| -0.7570 | -0.7588 |...........|...........|</span> +#> | U| 470.60274 | 92.48 | -5.354 | -0.9463 | -0.1131 | +#> |.....................| 2.294 | 1.526 | 0.03098 | 1.144 | +#> |.....................| 0.03115 | 0.7494 | 0.7919 | 1.386 | +#> <span style='text-decoration: underline;'>|.....................| 1.190 | 1.192 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 470.60274</span> | 92.48 | 0.004728 | 0.2796 | 0.8930 | +#> |.....................| 9.912 | 1.526 | 0.03098 | 1.144 | +#> |.....................| 0.03115 | 0.7494 | 0.7919 | 1.386 | +#> <span style='text-decoration: underline;'>|.....................| 1.190 | 1.192 |...........|...........|</span> +#> | F| Forward Diff. | -35.91 | 1.718 | -0.07847 | 0.1786 | +#> |.....................| -0.1996 | -26.69 | -4.843 | 1.231 | +#> |.....................| -2.229 | 0.5625 | 3.489 | -5.662 | +#> <span style='text-decoration: underline;'>|.....................| -4.557 | -4.604 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 55</span>| 470.25392 | 0.9977 | -1.054 | -0.9140 | -0.9015 | +#> |.....................| -0.8431 | -0.2250 | -0.8375 | -0.8987 | +#> |.....................| -0.8153 | -0.8894 | -0.9673 | -0.7229 | +#> <span style='text-decoration: underline;'>|.....................| -0.7550 | -0.7569 |...........|...........|</span> +#> | U| 470.25392 | 92.90 | -5.357 | -0.9467 | -0.1136 | +#> |.....................| 2.293 | 1.533 | 0.03087 | 1.142 | +#> |.....................| 0.03120 | 0.7483 | 0.7927 | 1.389 | +#> <span style='text-decoration: underline;'>|.....................| 1.192 | 1.194 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 470.25392</span> | 92.90 | 0.004715 | 0.2796 | 0.8926 | +#> |.....................| 9.909 | 1.533 | 0.03087 | 1.142 | +#> |.....................| 0.03120 | 0.7483 | 0.7927 | 1.389 | +#> <span style='text-decoration: underline;'>|.....................| 1.192 | 1.194 |...........|...........|</span> +#> | F| Forward Diff. | 23.42 | 1.753 | 0.1414 | 0.2393 | +#> |.....................| 0.01691 | -26.51 | -4.262 | 0.6993 | +#> |.....................| -2.408 | 0.5525 | 2.318 | -5.573 | +#> <span style='text-decoration: underline;'>|.....................| -4.475 | -4.572 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 56</span>| 469.96066 | 0.9934 | -1.056 | -0.9144 | -0.9019 | +#> |.....................| -0.8434 | -0.2128 | -0.8432 | -0.9002 | +#> |.....................| -0.8113 | -0.8903 | -0.9627 | -0.7205 | +#> <span style='text-decoration: underline;'>|.....................| -0.7531 | -0.7551 |...........|...........|</span> +#> | U| 469.96066 | 92.50 | -5.359 | -0.9470 | -0.1140 | +#> |.....................| 2.293 | 1.540 | 0.03078 | 1.141 | +#> |.....................| 0.03126 | 0.7476 | 0.7967 | 1.392 | +#> <span style='text-decoration: underline;'>|.....................| 1.194 | 1.196 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 469.96066</span> | 92.50 | 0.004704 | 0.2795 | 0.8922 | +#> |.....................| 9.906 | 1.540 | 0.03078 | 1.141 | +#> |.....................| 0.03126 | 0.7476 | 0.7967 | 1.392 | +#> <span style='text-decoration: underline;'>|.....................| 1.194 | 1.196 |...........|...........|</span> +#> | F| Forward Diff. | -33.10 | 1.713 | -0.09549 | 0.1710 | +#> |.....................| -0.1943 | -25.89 | -4.557 | 1.045 | +#> |.....................| -2.243 | 0.5648 | 3.834 | -5.392 | +#> <span style='text-decoration: underline;'>|.....................| -4.399 | -4.402 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 57</span>| 469.66426 | 0.9983 | -1.059 | -0.9147 | -0.9023 | +#> |.....................| -0.8437 | -0.2012 | -0.8503 | -0.9014 | +#> |.....................| -0.8068 | -0.8914 | -0.9589 | -0.7186 | +#> <span style='text-decoration: underline;'>|.....................| -0.7515 | -0.7537 |...........|...........|</span> +#> | U| 469.66426 | 92.95 | -5.362 | -0.9473 | -0.1144 | +#> |.....................| 2.293 | 1.547 | 0.03068 | 1.141 | +#> |.....................| 0.03133 | 0.7468 | 0.8000 | 1.394 | +#> <span style='text-decoration: underline;'>|.....................| 1.196 | 1.197 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 469.66426</span> | 92.95 | 0.004691 | 0.2794 | 0.8919 | +#> |.....................| 9.903 | 1.547 | 0.03068 | 1.141 | +#> |.....................| 0.03133 | 0.7468 | 0.8000 | 1.394 | +#> <span style='text-decoration: underline;'>|.....................| 1.196 | 1.197 |...........|...........|</span> +#> | F| Forward Diff. | 29.48 | 1.769 | 0.1441 | 0.2362 | +#> |.....................| 0.03493 | -25.40 | -3.876 | 0.7581 | +#> |.....................| -2.246 | 0.6653 | 4.370 | -5.362 | +#> <span style='text-decoration: underline;'>|.....................| -4.340 | -4.389 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 58</span>| 469.35361 | 0.9940 | -1.062 | -0.9149 | -0.9027 | +#> |.....................| -0.8440 | -0.1900 | -0.8585 | -0.9032 | +#> |.....................| -0.8026 | -0.8931 | -0.9615 | -0.7168 | +#> <span style='text-decoration: underline;'>|.....................| -0.7497 | -0.7523 |...........|...........|</span> +#> | U| 469.35361 | 92.56 | -5.365 | -0.9475 | -0.1149 | +#> |.....................| 2.293 | 1.553 | 0.03055 | 1.140 | +#> |.....................| 0.03139 | 0.7454 | 0.7977 | 1.396 | +#> <span style='text-decoration: underline;'>|.....................| 1.198 | 1.199 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 469.35361</span> | 92.56 | 0.004677 | 0.2794 | 0.8915 | +#> |.....................| 9.900 | 1.553 | 0.03055 | 1.140 | +#> |.....................| 0.03139 | 0.7454 | 0.7977 | 1.396 | +#> <span style='text-decoration: underline;'>|.....................| 1.198 | 1.199 |...........|...........|</span> +#> | F| Forward Diff. | -26.71 | 1.702 | -0.07338 | 0.1729 | +#> |.....................| -0.1601 | -26.00 | -4.465 | 0.4354 | +#> |.....................| -2.821 | 0.3110 | 5.728 | -5.228 | +#> <span style='text-decoration: underline;'>|.....................| -4.240 | -4.266 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 59</span>| 469.04262 | 0.9978 | -1.064 | -0.9151 | -0.9031 | +#> |.....................| -0.8443 | -0.1798 | -0.8657 | -0.9030 | +#> |.....................| -0.7971 | -0.8938 | -0.9685 | -0.7157 | +#> <span style='text-decoration: underline;'>|.....................| -0.7487 | -0.7515 |...........|...........|</span> +#> | U| 469.04262 | 92.91 | -5.368 | -0.9477 | -0.1152 | +#> |.....................| 2.292 | 1.559 | 0.03044 | 1.140 | +#> |.....................| 0.03147 | 0.7450 | 0.7916 | 1.398 | +#> <span style='text-decoration: underline;'>|.....................| 1.199 | 1.200 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 469.04262</span> | 92.91 | 0.004665 | 0.2794 | 0.8912 | +#> |.....................| 9.897 | 1.559 | 0.03044 | 1.140 | +#> |.....................| 0.03147 | 0.7450 | 0.7916 | 1.398 | +#> <span style='text-decoration: underline;'>|.....................| 1.199 | 1.200 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 60</span>| 468.78438 | 0.9975 | -1.068 | -0.9154 | -0.9036 | +#> |.....................| -0.8447 | -0.1709 | -0.8764 | -0.9025 | +#> |.....................| -0.7900 | -0.8946 | -0.9771 | -0.7153 | +#> <span style='text-decoration: underline;'>|.....................| -0.7482 | -0.7514 |...........|...........|</span> +#> | U| 468.78438 | 92.88 | -5.371 | -0.9479 | -0.1157 | +#> |.....................| 2.292 | 1.564 | 0.03028 | 1.140 | +#> |.....................| 0.03158 | 0.7443 | 0.7841 | 1.398 | +#> <span style='text-decoration: underline;'>|.....................| 1.200 | 1.200 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 468.78438</span> | 92.88 | 0.004649 | 0.2793 | 0.8907 | +#> |.....................| 9.893 | 1.564 | 0.03028 | 1.140 | +#> |.....................| 0.03158 | 0.7443 | 0.7841 | 1.398 | +#> <span style='text-decoration: underline;'>|.....................| 1.200 | 1.200 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 61</span>| 467.65199 | 0.9960 | -1.083 | -0.9167 | -0.9058 | +#> |.....................| -0.8469 | -0.1283 | -0.9284 | -0.9002 | +#> |.....................| -0.7560 | -0.8987 | -1.018 | -0.7133 | +#> <span style='text-decoration: underline;'>|.....................| -0.7456 | -0.7506 |...........|...........|</span> +#> | U| 467.65199 | 92.74 | -5.387 | -0.9492 | -0.1179 | +#> |.....................| 2.290 | 1.589 | 0.02950 | 1.141 | +#> |.....................| 0.03209 | 0.7413 | 0.7481 | 1.401 | +#> <span style='text-decoration: underline;'>|.....................| 1.202 | 1.201 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 467.65199</span> | 92.74 | 0.004577 | 0.2791 | 0.8887 | +#> |.....................| 9.872 | 1.589 | 0.02950 | 1.141 | +#> |.....................| 0.03209 | 0.7413 | 0.7481 | 1.401 | +#> <span style='text-decoration: underline;'>|.....................| 1.202 | 1.201 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 62</span>| 464.96560 | 0.9898 | -1.148 | -0.9222 | -0.9151 | +#> |.....................| -0.8556 | 0.04847 | -1.144 | -0.8910 | +#> |.....................| -0.6148 | -0.9154 | -1.189 | -0.7051 | +#> <span style='text-decoration: underline;'>|.....................| -0.7350 | -0.7474 |...........|...........|</span> +#> | U| 464.9656 | 92.17 | -5.451 | -0.9543 | -0.1273 | +#> |.....................| 2.281 | 1.691 | 0.02626 | 1.147 | +#> |.....................| 0.03421 | 0.7285 | 0.5986 | 1.411 | +#> <span style='text-decoration: underline;'>|.....................| 1.214 | 1.204 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 464.9656</span> | 92.17 | 0.004291 | 0.2780 | 0.8805 | +#> |.....................| 9.786 | 1.691 | 0.02626 | 1.147 | +#> |.....................| 0.03421 | 0.7285 | 0.5986 | 1.411 | +#> <span style='text-decoration: underline;'>|.....................| 1.214 | 1.204 |...........|...........|</span> +#> | F| Forward Diff. | -134.9 | 0.8693 | 0.2607 | 0.2086 | +#> |.....................| 0.2111 | -19.53 | -3.427 | 3.399 | +#> |.....................| -2.172 | 1.526 | -11.79 | -4.993 | +#> <span style='text-decoration: underline;'>|.....................| -3.321 | -4.659 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 63</span>| 458.88877 | 1.003 | -1.235 | -0.9465 | -0.9328 | +#> |.....................| -0.8841 | 0.3192 | -1.460 | -0.9475 | +#> |.....................| -0.4237 | -0.9768 | -1.134 | -0.6574 | +#> <span style='text-decoration: underline;'>|.....................| -0.7075 | -0.6995 |...........|...........|</span> +#> | U| 458.88877 | 93.40 | -5.538 | -0.9774 | -0.1450 | +#> |.....................| 2.252 | 1.848 | 0.02152 | 1.114 | +#> |.....................| 0.03709 | 0.6820 | 0.6469 | 1.468 | +#> <span style='text-decoration: underline;'>|.....................| 1.243 | 1.255 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 458.88877</span> | 93.40 | 0.003933 | 0.2734 | 0.8651 | +#> |.....................| 9.511 | 1.848 | 0.02152 | 1.114 | +#> |.....................| 0.03709 | 0.6820 | 0.6469 | 1.468 | +#> <span style='text-decoration: underline;'>|.....................| 1.243 | 1.255 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 64</span>| 455.19412 | 1.006 | -1.330 | -0.9732 | -0.9522 | +#> |.....................| -0.9154 | 0.6143 | -1.806 | -1.009 | +#> |.....................| -0.2144 | -1.044 | -1.075 | -0.6056 | +#> <span style='text-decoration: underline;'>|.....................| -0.6776 | -0.6473 |...........|...........|</span> +#> | U| 455.19412 | 93.67 | -5.634 | -1.003 | -0.1644 | +#> |.....................| 2.221 | 2.019 | 0.01631 | 1.078 | +#> |.....................| 0.04023 | 0.6311 | 0.6989 | 1.531 | +#> <span style='text-decoration: underline;'>|.....................| 1.275 | 1.311 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 455.19412</span> | 93.67 | 0.003576 | 0.2684 | 0.8484 | +#> |.....................| 9.218 | 2.019 | 0.01631 | 1.078 | +#> |.....................| 0.04023 | 0.6311 | 0.6989 | 1.531 | +#> <span style='text-decoration: underline;'>|.....................| 1.275 | 1.311 |...........|...........|</span> +#> | F| Forward Diff. | 18.82 | 0.9889 | -1.032 | -0.1489 | +#> |.....................| 0.2009 | -8.117 | -0.5123 | 0.1656 | +#> |.....................| -2.314 | -3.473 | -0.8284 | 0.3432 | +#> <span style='text-decoration: underline;'>|.....................| -0.8357 | 0.04588 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 65</span>| 458.62552 | 1.004 | -1.494 | -0.8145 | -0.9319 | +#> |.....................| -0.9630 | 1.033 | -2.192 | -1.036 | +#> |.....................| 0.2529 | -0.5036 | -0.8838 | -0.8679 | +#> <span style='text-decoration: underline;'>|.....................| -0.7178 | -0.8209 |...........|...........|</span> +#> | U| 458.62552 | 93.52 | -5.797 | -0.8527 | -0.1440 | +#> |.....................| 2.174 | 2.262 | 0.01051 | 1.062 | +#> |.....................| 0.04725 | 1.041 | 0.8656 | 1.213 | +#> <span style='text-decoration: underline;'>|.....................| 1.232 | 1.125 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 458.62552</span> | 93.52 | 0.003036 | 0.2989 | 0.8659 | +#> |.....................| 8.789 | 2.262 | 0.01051 | 1.062 | +#> |.....................| 0.04725 | 1.041 | 0.8656 | 1.213 | +#> <span style='text-decoration: underline;'>|.....................| 1.232 | 1.125 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 66</span>| 454.48694 | 1.003 | -1.384 | -0.9206 | -0.9455 | +#> |.....................| -0.9312 | 0.7538 | -1.934 | -1.018 | +#> |.....................| -0.05956 | -0.8649 | -1.011 | -0.6924 | +#> <span style='text-decoration: underline;'>|.....................| -0.6908 | -0.7048 |...........|...........|</span> +#> | U| 454.48694 | 93.41 | -5.688 | -0.9529 | -0.1576 | +#> |.....................| 2.205 | 2.100 | 0.01439 | 1.073 | +#> |.....................| 0.04256 | 0.7669 | 0.7542 | 1.426 | +#> <span style='text-decoration: underline;'>|.....................| 1.261 | 1.250 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 454.48694</span> | 93.41 | 0.003387 | 0.2783 | 0.8542 | +#> |.....................| 9.074 | 2.100 | 0.01439 | 1.073 | +#> |.....................| 0.04256 | 0.7669 | 0.7542 | 1.426 | +#> <span style='text-decoration: underline;'>|.....................| 1.261 | 1.250 |...........|...........|</span> +#> | F| Forward Diff. | -11.88 | 0.8805 | 1.030 | 0.0001663 | +#> |.....................| -0.3119 | -6.748 | -1.151 | 0.2517 | +#> |.....................| -3.379 | 3.981 | 5.317 | -4.395 | +#> <span style='text-decoration: underline;'>|.....................| -1.890 | -2.785 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 67</span>| 453.47854 | 1.004 | -1.455 | -0.9097 | -0.9308 | +#> |.....................| -0.9364 | 0.8078 | -2.047 | -1.046 | +#> |.....................| 0.2383 | -0.8443 | -0.9977 | -0.6524 | +#> <span style='text-decoration: underline;'>|.....................| -0.6789 | -0.6970 |...........|...........|</span> +#> | U| 453.47854 | 93.48 | -5.759 | -0.9426 | -0.1429 | +#> |.....................| 2.200 | 2.132 | 0.01270 | 1.056 | +#> |.....................| 0.04703 | 0.7825 | 0.7661 | 1.474 | +#> <span style='text-decoration: underline;'>|.....................| 1.274 | 1.258 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 453.47854</span> | 93.48 | 0.003156 | 0.2804 | 0.8668 | +#> |.....................| 9.026 | 2.132 | 0.01270 | 1.056 | +#> |.....................| 0.04703 | 0.7825 | 0.7661 | 1.474 | +#> <span style='text-decoration: underline;'>|.....................| 1.274 | 1.258 |...........|...........|</span> +#> | F| Forward Diff. | -7.580 | 0.7096 | 1.748 | 0.4450 | +#> |.....................| -0.3063 | -5.686 | -1.090 | 2.089 | +#> |.....................| -1.806 | 4.661 | 3.477 | -2.550 | +#> <span style='text-decoration: underline;'>|.....................| -1.063 | -2.646 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 68</span>| 452.65869 | 1.010 | -1.604 | -0.9910 | -0.9601 | +#> |.....................| -0.9321 | 0.9548 | -2.236 | -1.333 | +#> |.....................| 0.7427 | -0.9083 | -1.017 | -0.7899 | +#> <span style='text-decoration: underline;'>|.....................| -0.7453 | -0.6781 |...........|...........|</span> +#> | U| 452.65869 | 94.06 | -5.907 | -1.019 | -0.1723 | +#> |.....................| 2.204 | 2.217 | 0.009851 | 0.8906 | +#> |.....................| 0.05461 | 0.7340 | 0.7490 | 1.308 | +#> <span style='text-decoration: underline;'>|.....................| 1.203 | 1.278 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.65869</span> | 94.06 | 0.002719 | 0.2652 | 0.8418 | +#> |.....................| 9.065 | 2.217 | 0.009851 | 0.8906 | +#> |.....................| 0.05461 | 0.7340 | 0.7490 | 1.308 | +#> <span style='text-decoration: underline;'>|.....................| 1.203 | 1.278 |...........|...........|</span> +#> | F| Forward Diff. | 87.74 | 0.4343 | -0.7887 | -0.2527 | +#> |.....................| -0.1232 | -3.287 | -0.3715 | -5.728 | +#> |.....................| -3.469 | 4.620 | 5.104 | -8.863 | +#> <span style='text-decoration: underline;'>|.....................| -5.024 | -1.180 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 69</span>| 455.46876 | 1.000 | -1.721 | -0.9929 | -1.109 | +#> |.....................| -0.8905 | 1.109 | -2.343 | -1.386 | +#> |.....................| 1.193 | -1.162 | -0.9750 | -0.9277 | +#> <span style='text-decoration: underline;'>|.....................| -0.5804 | -0.9245 |...........|...........|</span> +#> | U| 455.46876 | 93.13 | -6.025 | -1.021 | -0.3216 | +#> |.....................| 2.246 | 2.306 | 0.008241 | 0.8595 | +#> |.....................| 0.06138 | 0.5417 | 0.7859 | 1.140 | +#> <span style='text-decoration: underline;'>|.....................| 1.379 | 1.014 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 455.46876</span> | 93.13 | 0.002419 | 0.2648 | 0.7250 | +#> |.....................| 9.450 | 2.306 | 0.008241 | 0.8595 | +#> |.....................| 0.06138 | 0.5417 | 0.7859 | 1.140 | +#> <span style='text-decoration: underline;'>|.....................| 1.379 | 1.014 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 70</span>| 453.13548 | 0.9926 | -1.633 | -0.9913 | -0.9976 | +#> |.....................| -0.9216 | 0.9941 | -2.263 | -1.345 | +#> |.....................| 0.8563 | -0.9728 | -1.008 | -0.8230 | +#> <span style='text-decoration: underline;'>|.....................| -0.7030 | -0.7398 |...........|...........|</span> +#> | U| 453.13548 | 92.43 | -5.937 | -1.020 | -0.2097 | +#> |.....................| 2.215 | 2.240 | 0.009448 | 0.8833 | +#> |.....................| 0.05632 | 0.6851 | 0.7575 | 1.268 | +#> <span style='text-decoration: underline;'>|.....................| 1.248 | 1.212 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 453.13548</span> | 92.43 | 0.002640 | 0.2651 | 0.8108 | +#> |.....................| 9.161 | 2.240 | 0.009448 | 0.8833 | +#> |.....................| 0.05632 | 0.6851 | 0.7575 | 1.268 | +#> <span style='text-decoration: underline;'>|.....................| 1.248 | 1.212 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 71</span>| 453.54485 | 0.9910 | -1.615 | -0.9910 | -0.9747 | +#> |.....................| -0.9280 | 0.9706 | -2.247 | -1.337 | +#> |.....................| 0.7875 | -0.9341 | -1.014 | -0.8015 | +#> <span style='text-decoration: underline;'>|.....................| -0.7281 | -0.7020 |...........|...........|</span> +#> | U| 453.54485 | 92.28 | -5.919 | -1.019 | -0.1868 | +#> |.....................| 2.209 | 2.226 | 0.009694 | 0.8882 | +#> |.....................| 0.05529 | 0.7144 | 0.7517 | 1.294 | +#> <span style='text-decoration: underline;'>|.....................| 1.221 | 1.253 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 453.54485</span> | 92.28 | 0.002688 | 0.2651 | 0.8296 | +#> |.....................| 9.103 | 2.226 | 0.009694 | 0.8882 | +#> |.....................| 0.05529 | 0.7144 | 0.7517 | 1.294 | +#> <span style='text-decoration: underline;'>|.....................| 1.221 | 1.253 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 72</span>| 453.87696 | 0.9902 | -1.606 | -0.9909 | -0.9627 | +#> |.....................| -0.9313 | 0.9582 | -2.238 | -1.332 | +#> |.....................| 0.7513 | -0.9138 | -1.018 | -0.7903 | +#> <span style='text-decoration: underline;'>|.....................| -0.7413 | -0.6822 |...........|...........|</span> +#> | U| 453.87696 | 92.21 | -5.909 | -1.019 | -0.1748 | +#> |.....................| 2.205 | 2.219 | 0.009824 | 0.8908 | +#> |.....................| 0.05474 | 0.7298 | 0.7487 | 1.307 | +#> <span style='text-decoration: underline;'>|.....................| 1.207 | 1.274 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 453.87696</span> | 92.21 | 0.002714 | 0.2652 | 0.8396 | +#> |.....................| 9.072 | 2.219 | 0.009824 | 0.8908 | +#> |.....................| 0.05474 | 0.7298 | 0.7487 | 1.307 | +#> <span style='text-decoration: underline;'>|.....................| 1.207 | 1.274 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 73</span>| 452.40810 | 1.003 | -1.604 | -0.9910 | -0.9601 | +#> |.....................| -0.9321 | 0.9550 | -2.236 | -1.332 | +#> |.....................| 0.7430 | -0.9087 | -1.018 | -0.7892 | +#> <span style='text-decoration: underline;'>|.....................| -0.7449 | -0.6781 |...........|...........|</span> +#> | U| 452.4081 | 93.41 | -5.907 | -1.019 | -0.1722 | +#> |.....................| 2.204 | 2.217 | 0.009851 | 0.8908 | +#> |.....................| 0.05462 | 0.7337 | 0.7487 | 1.309 | +#> <span style='text-decoration: underline;'>|.....................| 1.203 | 1.278 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.4081</span> | 93.41 | 0.002719 | 0.2652 | 0.8418 | +#> |.....................| 9.065 | 2.217 | 0.009851 | 0.8908 | +#> |.....................| 0.05462 | 0.7337 | 0.7487 | 1.309 | +#> <span style='text-decoration: underline;'>|.....................| 1.203 | 1.278 |...........|...........|</span> +#> | F| Forward Diff. | -20.28 | 0.3985 | -0.9900 | -0.3302 | +#> |.....................| -0.4580 | -3.509 | -0.7634 | -5.125 | +#> |.....................| -3.224 | 3.921 | 4.784 | -8.607 | +#> <span style='text-decoration: underline;'>|.....................| -4.910 | -1.049 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 74</span>| 452.35774 | 1.005 | -1.605 | -0.9906 | -0.9617 | +#> |.....................| -0.9314 | 0.9567 | -2.238 | -1.332 | +#> |.....................| 0.7462 | -0.9112 | -1.018 | -0.7890 | +#> <span style='text-decoration: underline;'>|.....................| -0.7417 | -0.6810 |...........|...........|</span> +#> | U| 452.35774 | 93.58 | -5.909 | -1.019 | -0.1738 | +#> |.....................| 2.205 | 2.218 | 0.009828 | 0.8908 | +#> |.....................| 0.05467 | 0.7317 | 0.7485 | 1.309 | +#> <span style='text-decoration: underline;'>|.....................| 1.207 | 1.275 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.35774</span> | 93.58 | 0.002715 | 0.2652 | 0.8405 | +#> |.....................| 9.072 | 2.218 | 0.009828 | 0.8908 | +#> |.....................| 0.05467 | 0.7317 | 0.7485 | 1.309 | +#> <span style='text-decoration: underline;'>|.....................| 1.207 | 1.275 |...........|...........|</span> +#> | F| Forward Diff. | 9.319 | 0.4042 | -0.9262 | -0.3428 | +#> |.....................| -0.3413 | -3.482 | -0.6441 | -5.151 | +#> |.....................| -3.223 | 3.864 | 4.863 | -8.623 | +#> <span style='text-decoration: underline;'>|.....................| -4.770 | -1.217 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 75</span>| 452.31017 | 1.003 | -1.607 | -0.9902 | -0.9631 | +#> |.....................| -0.9307 | 0.9586 | -2.239 | -1.332 | +#> |.....................| 0.7493 | -0.9137 | -1.019 | -0.7876 | +#> <span style='text-decoration: underline;'>|.....................| -0.7383 | -0.6834 |...........|...........|</span> +#> | U| 452.31017 | 93.41 | -5.910 | -1.019 | -0.1752 | +#> |.....................| 2.206 | 2.219 | 0.009807 | 0.8910 | +#> |.....................| 0.05471 | 0.7298 | 0.7478 | 1.310 | +#> <span style='text-decoration: underline;'>|.....................| 1.210 | 1.273 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.31017</span> | 93.41 | 0.002711 | 0.2653 | 0.8393 | +#> |.....................| 9.078 | 2.219 | 0.009807 | 0.8910 | +#> |.....................| 0.05471 | 0.7298 | 0.7478 | 1.310 | +#> <span style='text-decoration: underline;'>|.....................| 1.210 | 1.273 |...........|...........|</span> +#> | F| Forward Diff. | -20.20 | 0.3903 | -0.9767 | -0.3983 | +#> |.....................| -0.4106 | -3.495 | -0.7375 | -5.052 | +#> |.....................| -3.297 | 3.718 | 4.704 | -8.538 | +#> <span style='text-decoration: underline;'>|.....................| -4.606 | -1.295 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 76</span>| 452.25868 | 1.005 | -1.609 | -0.9898 | -0.9648 | +#> |.....................| -0.9300 | 0.9604 | -2.241 | -1.332 | +#> |.....................| 0.7529 | -0.9160 | -1.019 | -0.7870 | +#> <span style='text-decoration: underline;'>|.....................| -0.7354 | -0.6858 |...........|...........|</span> +#> | U| 452.25868 | 93.58 | -5.912 | -1.018 | -0.1770 | +#> |.....................| 2.207 | 2.220 | 0.009778 | 0.8908 | +#> |.....................| 0.05477 | 0.7281 | 0.7476 | 1.311 | +#> <span style='text-decoration: underline;'>|.....................| 1.213 | 1.270 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.25868</span> | 93.58 | 0.002707 | 0.2654 | 0.8378 | +#> |.....................| 9.084 | 2.220 | 0.009778 | 0.8908 | +#> |.....................| 0.05477 | 0.7281 | 0.7476 | 1.311 | +#> <span style='text-decoration: underline;'>|.....................| 1.213 | 1.270 |...........|...........|</span> +#> | F| Forward Diff. | 8.768 | 0.3959 | -0.9108 | -0.4152 | +#> |.....................| -0.2985 | -3.789 | -0.7277 | -5.480 | +#> |.....................| -3.800 | 3.463 | 7.165 | -8.525 | +#> <span style='text-decoration: underline;'>|.....................| -4.480 | -1.429 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 77</span>| 452.20380 | 1.003 | -1.610 | -0.9896 | -0.9665 | +#> |.....................| -0.9299 | 0.9625 | -2.243 | -1.331 | +#> |.....................| 0.7574 | -0.9182 | -1.020 | -0.7855 | +#> <span style='text-decoration: underline;'>|.....................| -0.7330 | -0.6868 |...........|...........|</span> +#> | U| 452.2038 | 93.42 | -5.913 | -1.018 | -0.1787 | +#> |.....................| 2.207 | 2.221 | 0.009753 | 0.8912 | +#> |.....................| 0.05483 | 0.7265 | 0.7464 | 1.313 | +#> <span style='text-decoration: underline;'>|.....................| 1.216 | 1.269 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.2038</span> | 93.42 | 0.002704 | 0.2654 | 0.8364 | +#> |.....................| 9.085 | 2.221 | 0.009753 | 0.8912 | +#> |.....................| 0.05483 | 0.7265 | 0.7464 | 1.313 | +#> <span style='text-decoration: underline;'>|.....................| 1.216 | 1.269 |...........|...........|</span> +#> | F| Forward Diff. | -17.51 | 0.3875 | -0.9566 | -0.4713 | +#> |.....................| -0.3666 | -3.384 | -0.7134 | -4.862 | +#> |.....................| -3.257 | 3.566 | 3.539 | -8.382 | +#> <span style='text-decoration: underline;'>|.....................| -4.308 | -1.428 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 78</span>| 452.15674 | 1.006 | -1.611 | -0.9895 | -0.9681 | +#> |.....................| -0.9296 | 0.9646 | -2.244 | -1.331 | +#> |.....................| 0.7624 | -0.9204 | -1.020 | -0.7847 | +#> <span style='text-decoration: underline;'>|.....................| -0.7317 | -0.6876 |...........|...........|</span> +#> | U| 452.15674 | 93.63 | -5.915 | -1.018 | -0.1803 | +#> |.....................| 2.207 | 2.222 | 0.009729 | 0.8915 | +#> |.....................| 0.05491 | 0.7248 | 0.7463 | 1.314 | +#> <span style='text-decoration: underline;'>|.....................| 1.217 | 1.268 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.15674</span> | 93.63 | 0.002700 | 0.2654 | 0.8350 | +#> |.....................| 9.088 | 2.222 | 0.009729 | 0.8915 | +#> |.....................| 0.05491 | 0.7248 | 0.7463 | 1.314 | +#> <span style='text-decoration: underline;'>|.....................| 1.217 | 1.268 |...........|...........|</span> +#> | F| Forward Diff. | 16.34 | 0.3942 | -0.8917 | -0.4820 | +#> |.....................| -0.2498 | -3.403 | -0.6022 | -5.023 | +#> |.....................| -3.383 | 3.482 | 3.627 | -8.397 | +#> <span style='text-decoration: underline;'>|.....................| -4.266 | -1.517 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 79</span>| 452.11013 | 1.004 | -1.613 | -0.9892 | -0.9692 | +#> |.....................| -0.9285 | 0.9667 | -2.245 | -1.330 | +#> |.....................| 0.7674 | -0.9230 | -1.020 | -0.7840 | +#> <span style='text-decoration: underline;'>|.....................| -0.7312 | -0.6887 |...........|...........|</span> +#> | U| 452.11013 | 93.48 | -5.917 | -1.018 | -0.1814 | +#> |.....................| 2.208 | 2.224 | 0.009710 | 0.8921 | +#> |.....................| 0.05499 | 0.7229 | 0.7466 | 1.315 | +#> <span style='text-decoration: underline;'>|.....................| 1.218 | 1.267 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.11013</span> | 93.48 | 0.002694 | 0.2655 | 0.8341 | +#> |.....................| 9.098 | 2.224 | 0.009710 | 0.8921 | +#> |.....................| 0.05499 | 0.7229 | 0.7466 | 1.315 | +#> <span style='text-decoration: underline;'>|.....................| 1.218 | 1.267 |...........|...........|</span> +#> | F| Forward Diff. | -8.858 | 0.3784 | -0.9339 | -0.5242 | +#> |.....................| -0.2958 | -3.274 | -0.6451 | -4.716 | +#> |.....................| -3.235 | 3.524 | 3.578 | -8.323 | +#> <span style='text-decoration: underline;'>|.....................| -4.226 | -1.527 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 80</span>| 452.06081 | 1.006 | -1.615 | -0.9885 | -0.9698 | +#> |.....................| -0.9277 | 0.9688 | -2.247 | -1.329 | +#> |.....................| 0.7723 | -0.9255 | -1.020 | -0.7822 | +#> <span style='text-decoration: underline;'>|.....................| -0.7302 | -0.6891 |...........|...........|</span> +#> | U| 452.06081 | 93.65 | -5.919 | -1.017 | -0.1820 | +#> |.....................| 2.209 | 2.225 | 0.009693 | 0.8927 | +#> |.....................| 0.05506 | 0.7209 | 0.7465 | 1.317 | +#> <span style='text-decoration: underline;'>|.....................| 1.219 | 1.266 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.06081</span> | 93.65 | 0.002689 | 0.2656 | 0.8336 | +#> |.....................| 9.105 | 2.225 | 0.009693 | 0.8927 | +#> |.....................| 0.05506 | 0.7209 | 0.7465 | 1.317 | +#> <span style='text-decoration: underline;'>|.....................| 1.219 | 1.266 |...........|...........|</span> +#> | F| Forward Diff. | 18.08 | 0.3814 | -0.8701 | -0.5179 | +#> |.....................| -0.1901 | -3.027 | -0.4828 | -4.583 | +#> |.....................| -3.046 | 3.385 | 4.724 | -8.292 | +#> <span style='text-decoration: underline;'>|.....................| -4.215 | -1.583 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 81</span>| 452.00089 | 1.004 | -1.618 | -0.9864 | -0.9698 | +#> |.....................| -0.9276 | 0.9701 | -2.249 | -1.331 | +#> |.....................| 0.7751 | -0.9261 | -1.021 | -0.7787 | +#> <span style='text-decoration: underline;'>|.....................| -0.7281 | -0.6889 |...........|...........|</span> +#> | U| 452.00089 | 93.48 | -5.921 | -1.015 | -0.1820 | +#> |.....................| 2.209 | 2.226 | 0.009656 | 0.8916 | +#> |.....................| 0.05510 | 0.7205 | 0.7459 | 1.321 | +#> <span style='text-decoration: underline;'>|.....................| 1.221 | 1.267 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 452.00089</span> | 93.48 | 0.002683 | 0.2660 | 0.8336 | +#> |.....................| 9.107 | 2.226 | 0.009656 | 0.8916 | +#> |.....................| 0.05510 | 0.7205 | 0.7459 | 1.321 | +#> <span style='text-decoration: underline;'>|.....................| 1.221 | 1.267 |...........|...........|</span> +#> | F| Forward Diff. | -8.141 | 0.3688 | -0.8752 | -0.5418 | +#> |.....................| -0.2687 | -3.191 | -0.6153 | -4.612 | +#> |.....................| -3.168 | 3.248 | 4.602 | -8.159 | +#> <span style='text-decoration: underline;'>|.....................| -4.118 | -1.545 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 82</span>| 451.94404 | 1.006 | -1.619 | -0.9850 | -0.9696 | +#> |.....................| -0.9279 | 0.9711 | -2.251 | -1.332 | +#> |.....................| 0.7767 | -0.9258 | -1.022 | -0.7739 | +#> <span style='text-decoration: underline;'>|.....................| -0.7256 | -0.6877 |...........|...........|</span> +#> | U| 451.94404 | 93.65 | -5.922 | -1.014 | -0.1817 | +#> |.....................| 2.209 | 2.226 | 0.009627 | 0.8908 | +#> |.....................| 0.05512 | 0.7207 | 0.7445 | 1.327 | +#> <span style='text-decoration: underline;'>|.....................| 1.224 | 1.268 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 451.94404</span> | 93.65 | 0.002679 | 0.2663 | 0.8338 | +#> |.....................| 9.104 | 2.226 | 0.009627 | 0.8908 | +#> |.....................| 0.05512 | 0.7207 | 0.7445 | 1.327 | +#> <span style='text-decoration: underline;'>|.....................| 1.224 | 1.268 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 83</span>| 451.90577 | 1.006 | -1.621 | -0.9832 | -0.9693 | +#> |.....................| -0.9284 | 0.9716 | -2.254 | -1.336 | +#> |.....................| 0.7778 | -0.9242 | -1.023 | -0.7696 | +#> <span style='text-decoration: underline;'>|.....................| -0.7233 | -0.6864 |...........|...........|</span> +#> | U| 451.90577 | 93.65 | -5.925 | -1.012 | -0.1815 | +#> |.....................| 2.208 | 2.227 | 0.009581 | 0.8887 | +#> |.....................| 0.05514 | 0.7219 | 0.7437 | 1.332 | +#> <span style='text-decoration: underline;'>|.....................| 1.226 | 1.269 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 451.90577</span> | 93.65 | 0.002673 | 0.2666 | 0.8340 | +#> |.....................| 9.099 | 2.227 | 0.009581 | 0.8887 | +#> |.....................| 0.05514 | 0.7219 | 0.7437 | 1.332 | +#> <span style='text-decoration: underline;'>|.....................| 1.226 | 1.269 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 84</span>| 451.74017 | 1.006 | -1.632 | -0.9740 | -0.9682 | +#> |.....................| -0.9311 | 0.9738 | -2.270 | -1.354 | +#> |.....................| 0.7839 | -0.9163 | -1.028 | -0.7474 | +#> <span style='text-decoration: underline;'>|.....................| -0.7117 | -0.6796 |...........|...........|</span> +#> | U| 451.74017 | 93.64 | -5.935 | -1.003 | -0.1804 | +#> |.....................| 2.205 | 2.228 | 0.009348 | 0.8780 | +#> |.....................| 0.05523 | 0.7279 | 0.7400 | 1.359 | +#> <span style='text-decoration: underline;'>|.....................| 1.239 | 1.277 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 451.74017</span> | 93.64 | 0.002645 | 0.2683 | 0.8350 | +#> |.....................| 9.074 | 2.228 | 0.009348 | 0.8780 | +#> |.....................| 0.05523 | 0.7279 | 0.7400 | 1.359 | +#> <span style='text-decoration: underline;'>|.....................| 1.239 | 1.277 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 85</span>| 451.58673 | 1.005 | -1.675 | -0.9364 | -0.9637 | +#> |.....................| -0.9422 | 0.9828 | -2.333 | -1.429 | +#> |.....................| 0.8084 | -0.8841 | -1.045 | -0.6570 | +#> <span style='text-decoration: underline;'>|.....................| -0.6645 | -0.6522 |...........|...........|</span> +#> | U| 451.58673 | 93.57 | -5.978 | -0.9678 | -0.1758 | +#> |.....................| 2.194 | 2.233 | 0.008399 | 0.8346 | +#> |.....................| 0.05560 | 0.7523 | 0.7245 | 1.469 | +#> <span style='text-decoration: underline;'>|.....................| 1.289 | 1.306 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 451.58673</span> | 93.57 | 0.002533 | 0.2753 | 0.8388 | +#> |.....................| 8.974 | 2.233 | 0.008399 | 0.8346 | +#> |.....................| 0.05560 | 0.7523 | 0.7245 | 1.469 | +#> <span style='text-decoration: underline;'>|.....................| 1.289 | 1.306 |...........|...........|</span> +#> | F| Forward Diff. | 7.829 | 0.3494 | 0.8366 | -0.4922 | +#> |.....................| -0.7083 | -3.782 | -0.9020 | -9.523 | +#> |.....................| -4.571 | 4.733 | 3.935 | -3.194 | +#> <span style='text-decoration: underline;'>|.....................| -1.280 | 0.5510 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 86</span>| 450.56328 | 1.003 | -1.760 | -0.9418 | -0.9563 | +#> |.....................| -0.9480 | 1.050 | -2.445 | -1.421 | +#> |.....................| 0.9402 | -0.9310 | -1.041 | -0.6107 | +#> <span style='text-decoration: underline;'>|.....................| -0.6547 | -0.6413 |...........|...........|</span> +#> | U| 450.56328 | 93.41 | -6.064 | -0.9728 | -0.1684 | +#> |.....................| 2.189 | 2.272 | 0.006706 | 0.8396 | +#> |.....................| 0.05758 | 0.7168 | 0.7280 | 1.525 | +#> <span style='text-decoration: underline;'>|.....................| 1.300 | 1.318 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 450.56328</span> | 93.41 | 0.002326 | 0.2743 | 0.8450 | +#> |.....................| 8.923 | 2.272 | 0.006706 | 0.8396 | +#> |.....................| 0.05758 | 0.7168 | 0.7280 | 1.525 | +#> <span style='text-decoration: underline;'>|.....................| 1.300 | 1.318 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 87</span>| 449.70344 | 1.004 | -1.916 | -0.9511 | -0.9429 | +#> |.....................| -0.9589 | 1.170 | -2.653 | -1.409 | +#> |.....................| 1.180 | -1.015 | -1.032 | -0.5274 | +#> <span style='text-decoration: underline;'>|.....................| -0.6372 | -0.6210 |...........|...........|</span> +#> | U| 449.70344 | 93.47 | -6.220 | -0.9817 | -0.1550 | +#> |.....................| 2.178 | 2.342 | 0.003591 | 0.8462 | +#> |.....................| 0.06119 | 0.6534 | 0.7360 | 1.626 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.340 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.70344</span> | 93.47 | 0.001990 | 0.2726 | 0.8564 | +#> |.....................| 8.826 | 2.342 | 0.003591 | 0.8462 | +#> |.....................| 0.06119 | 0.6534 | 0.7360 | 1.626 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.340 |...........|...........|</span> +#> | F| Forward Diff. | -19.90 | -0.3168 | 0.4549 | 0.1875 | +#> |.....................| -1.116 | -0.4934 | -0.07687 | -3.113 | +#> |.....................| -2.715 | -1.586 | 5.430 | 3.365 | +#> <span style='text-decoration: underline;'>|.....................| 0.3009 | 1.974 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 88</span>| 451.98935 | 1.002 | -1.890 | -1.062 | -1.052 | +#> |.....................| -0.7983 | 1.243 | -2.828 | -1.513 | +#> |.....................| 1.600 | -1.043 | -1.029 | -0.6268 | +#> <span style='text-decoration: underline;'>|.....................| -0.3463 | -0.6648 |...........|...........|</span> +#> | U| 451.98935 | 93.35 | -6.193 | -1.087 | -0.2643 | +#> |.....................| 2.338 | 2.384 | 0.0009551 | 0.7857 | +#> |.....................| 0.06749 | 0.6319 | 0.7389 | 1.506 | +#> <span style='text-decoration: underline;'>|.....................| 1.629 | 1.293 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 451.98935</span> | 93.35 | 0.002043 | 0.2523 | 0.7677 | +#> |.....................| 10.36 | 2.384 | 0.0009551 | 0.7857 | +#> |.....................| 0.06749 | 0.6319 | 0.7389 | 1.506 | +#> <span style='text-decoration: underline;'>|.....................| 1.629 | 1.293 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 89</span>| 449.56377 | 1.005 | -1.911 | -0.9716 | -0.9631 | +#> |.....................| -0.9292 | 1.184 | -2.685 | -1.428 | +#> |.....................| 1.258 | -1.020 | -1.032 | -0.5459 | +#> <span style='text-decoration: underline;'>|.....................| -0.5835 | -0.6292 |...........|...........|</span> +#> | U| 449.56377 | 93.56 | -6.215 | -1.001 | -0.1752 | +#> |.....................| 2.207 | 2.350 | 0.003105 | 0.8351 | +#> |.....................| 0.06235 | 0.6495 | 0.7362 | 1.604 | +#> <span style='text-decoration: underline;'>|.....................| 1.376 | 1.331 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.56377</span> | 93.56 | 0.002000 | 0.2687 | 0.8393 | +#> |.....................| 9.092 | 2.350 | 0.003105 | 0.8351 | +#> |.....................| 0.06235 | 0.6495 | 0.7362 | 1.604 | +#> <span style='text-decoration: underline;'>|.....................| 1.376 | 1.331 |...........|...........|</span> +#> | F| Forward Diff. | -8.503 | -0.3085 | -0.7128 | -0.4858 | +#> |.....................| -0.1462 | -0.3349 | -0.04630 | -2.615 | +#> |.....................| -2.539 | -1.761 | 5.421 | 2.664 | +#> <span style='text-decoration: underline;'>|.....................| 3.069 | 1.771 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 90</span>| 449.37295 | 1.008 | -1.883 | -0.9569 | -0.9753 | +#> |.....................| -0.9112 | 1.201 | -2.710 | -1.458 | +#> |.....................| 1.352 | -1.030 | -1.036 | -0.5467 | +#> <span style='text-decoration: underline;'>|.....................| -0.5933 | -0.6460 |...........|...........|</span> +#> | U| 449.37295 | 93.89 | -6.186 | -0.9871 | -0.1875 | +#> |.....................| 2.225 | 2.360 | 0.002726 | 0.8181 | +#> |.....................| 0.06377 | 0.6417 | 0.7326 | 1.603 | +#> <span style='text-decoration: underline;'>|.....................| 1.365 | 1.313 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.37295</span> | 93.89 | 0.002058 | 0.2715 | 0.8291 | +#> |.....................| 9.256 | 2.360 | 0.002726 | 0.8181 | +#> |.....................| 0.06377 | 0.6417 | 0.7326 | 1.603 | +#> <span style='text-decoration: underline;'>|.....................| 1.365 | 1.313 |...........|...........|</span> +#> | F| Forward Diff. | 31.95 | -0.2055 | 0.2861 | -0.8772 | +#> |.....................| 0.4589 | 0.008909 | 0.01409 | -2.994 | +#> |.....................| -2.511 | -2.129 | 5.021 | 2.567 | +#> <span style='text-decoration: underline;'>|.....................| 2.446 | 1.004 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 91</span>| 449.07232 | 1.007 | -1.848 | -0.9883 | -0.9607 | +#> |.....................| -0.9269 | 1.208 | -2.721 | -1.473 | +#> |.....................| 1.446 | -1.013 | -1.041 | -0.5472 | +#> <span style='text-decoration: underline;'>|.....................| -0.6000 | -0.6251 |...........|...........|</span> +#> | U| 449.07232 | 93.73 | -6.151 | -1.017 | -0.1729 | +#> |.....................| 2.210 | 2.364 | 0.002568 | 0.8093 | +#> |.....................| 0.06518 | 0.6543 | 0.7283 | 1.602 | +#> <span style='text-decoration: underline;'>|.....................| 1.358 | 1.335 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.07232</span> | 93.73 | 0.002130 | 0.2656 | 0.8412 | +#> |.....................| 9.113 | 2.364 | 0.002568 | 0.8093 | +#> |.....................| 0.06518 | 0.6543 | 0.7283 | 1.602 | +#> <span style='text-decoration: underline;'>|.....................| 1.358 | 1.335 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 92</span>| 449.34581 | 1.013 | -1.744 | -1.083 | -0.9172 | +#> |.....................| -0.9739 | 1.229 | -2.752 | -1.520 | +#> |.....................| 1.728 | -0.9642 | -1.054 | -0.5478 | +#> <span style='text-decoration: underline;'>|.....................| -0.6192 | -0.5619 |...........|...........|</span> +#> | U| 449.34581 | 94.33 | -6.047 | -1.106 | -0.1294 | +#> |.....................| 2.163 | 2.376 | 0.002092 | 0.7821 | +#> |.....................| 0.06942 | 0.6916 | 0.7169 | 1.601 | +#> <span style='text-decoration: underline;'>|.....................| 1.337 | 1.403 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.34581</span> | 94.33 | 0.002364 | 0.2486 | 0.8787 | +#> |.....................| 8.694 | 2.376 | 0.002092 | 0.7821 | +#> |.....................| 0.06942 | 0.6916 | 0.7169 | 1.601 | +#> <span style='text-decoration: underline;'>|.....................| 1.337 | 1.403 |...........|...........|</span> +#> | F| Forward Diff. | 11.36 | -0.08356 | -1.544 | -0.3785 | +#> |.....................| -0.02879 | 0.1985 | 0.04898 | -2.532 | +#> |.....................| -2.210 | -1.428 | 5.624 | 2.440 | +#> <span style='text-decoration: underline;'>|.....................| 2.104 | 1.894 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 93</span>| 449.83746 | 0.9966 | -1.806 | -0.8436 | -0.9213 | +#> |.....................| -1.016 | 1.236 | -2.752 | -1.567 | +#> |.....................| 1.816 | -1.085 | -1.056 | -0.6567 | +#> <span style='text-decoration: underline;'>|.....................| -0.5363 | -0.5852 |...........|...........|</span> +#> | U| 449.83746 | 92.80 | -6.109 | -0.8802 | -0.1335 | +#> |.....................| 2.121 | 2.380 | 0.002093 | 0.7548 | +#> |.....................| 0.07074 | 0.5997 | 0.7149 | 1.469 | +#> <span style='text-decoration: underline;'>|.....................| 1.426 | 1.378 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.83746</span> | 92.80 | 0.002222 | 0.2931 | 0.8750 | +#> |.....................| 8.340 | 2.380 | 0.002093 | 0.7548 | +#> |.....................| 0.07074 | 0.5997 | 0.7149 | 1.469 | +#> <span style='text-decoration: underline;'>|.....................| 1.426 | 1.378 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 94</span>| 449.05525 | 1.000 | -1.836 | -0.9477 | -0.9497 | +#> |.....................| -0.9515 | 1.216 | -2.730 | -1.498 | +#> |.....................| 1.549 | -1.033 | -1.047 | -0.5784 | +#> <span style='text-decoration: underline;'>|.....................| -0.5830 | -0.6146 |...........|...........|</span> +#> | U| 449.05525 | 93.13 | -6.140 | -0.9784 | -0.1618 | +#> |.....................| 2.185 | 2.368 | 0.002436 | 0.7946 | +#> |.....................| 0.06673 | 0.6395 | 0.7230 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.376 | 1.346 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.05525</span> | 93.13 | 0.002156 | 0.2732 | 0.8506 | +#> |.....................| 8.891 | 2.368 | 0.002436 | 0.7946 | +#> |.....................| 0.06673 | 0.6395 | 0.7230 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.376 | 1.346 |...........|...........|</span> +#> | F| Forward Diff. | -56.82 | -0.05113 | 0.4930 | -0.04031 | +#> |.....................| -1.049 | 0.03445 | -0.05944 | -2.319 | +#> |.....................| -2.208 | -2.328 | 3.545 | 0.3775 | +#> <span style='text-decoration: underline;'>|.....................| 2.643 | 2.387 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 95</span>| 448.75128 | 1.006 | -1.837 | -0.9543 | -0.9497 | +#> |.....................| -0.9537 | 1.219 | -2.732 | -1.514 | +#> |.....................| 1.608 | -1.030 | -1.050 | -0.5750 | +#> <span style='text-decoration: underline;'>|.....................| -0.5860 | -0.6263 |...........|...........|</span> +#> | U| 448.75128 | 93.69 | -6.140 | -0.9847 | -0.1618 | +#> |.....................| 2.183 | 2.370 | 0.002396 | 0.7854 | +#> |.....................| 0.06761 | 0.6415 | 0.7208 | 1.568 | +#> <span style='text-decoration: underline;'>|.....................| 1.373 | 1.334 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 448.75128</span> | 93.69 | 0.002154 | 0.2720 | 0.8506 | +#> |.....................| 8.872 | 2.370 | 0.002396 | 0.7854 | +#> |.....................| 0.06761 | 0.6415 | 0.7208 | 1.568 | +#> <span style='text-decoration: underline;'>|.....................| 1.373 | 1.334 |...........|...........|</span> +#> | F| Forward Diff. | 6.795 | -0.02569 | 0.3964 | 0.03329 | +#> |.....................| -0.8574 | 0.1774 | 0.01390 | -2.462 | +#> |.....................| -2.149 | -2.476 | 3.910 | 1.045 | +#> <span style='text-decoration: underline;'>|.....................| 2.743 | 2.014 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 96</span>| 448.60805 | 1.005 | -1.844 | -0.9658 | -0.9658 | +#> |.....................| -0.9330 | 1.222 | -2.731 | -1.528 | +#> |.....................| 1.652 | -1.023 | -1.051 | -0.5597 | +#> <span style='text-decoration: underline;'>|.....................| -0.5993 | -0.6478 |...........|...........|</span> +#> | U| 448.60805 | 93.55 | -6.147 | -0.9955 | -0.1780 | +#> |.....................| 2.204 | 2.372 | 0.002406 | 0.7773 | +#> |.....................| 0.06828 | 0.6470 | 0.7198 | 1.587 | +#> <span style='text-decoration: underline;'>|.....................| 1.359 | 1.311 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 448.60805</span> | 93.55 | 0.002140 | 0.2698 | 0.8370 | +#> |.....................| 9.057 | 2.372 | 0.002406 | 0.7773 | +#> |.....................| 0.06828 | 0.6470 | 0.7198 | 1.587 | +#> <span style='text-decoration: underline;'>|.....................| 1.359 | 1.311 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 97</span>| 448.54893 | 1.004 | -1.854 | -0.9831 | -0.9905 | +#> |.....................| -0.9018 | 1.226 | -2.730 | -1.550 | +#> |.....................| 1.719 | -1.013 | -1.051 | -0.5361 | +#> <span style='text-decoration: underline;'>|.....................| -0.6188 | -0.6800 |...........|...........|</span> +#> | U| 448.54893 | 93.53 | -6.157 | -1.012 | -0.2026 | +#> |.....................| 2.235 | 2.374 | 0.002422 | 0.7645 | +#> |.....................| 0.06928 | 0.6548 | 0.7192 | 1.616 | +#> <span style='text-decoration: underline;'>|.....................| 1.338 | 1.276 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 448.54893</span> | 93.53 | 0.002118 | 0.2666 | 0.8166 | +#> |.....................| 9.344 | 2.374 | 0.002422 | 0.7645 | +#> |.....................| 0.06928 | 0.6548 | 0.7192 | 1.616 | +#> <span style='text-decoration: underline;'>|.....................| 1.338 | 1.276 |...........|...........|</span> +#> | F| Forward Diff. | -11.31 | -0.05480 | -1.344 | -1.332 | +#> |.....................| 0.5363 | 0.1616 | -0.02955 | -2.282 | +#> |.....................| -1.949 | -1.541 | 5.051 | 2.875 | +#> <span style='text-decoration: underline;'>|.....................| 1.005 | -0.6800 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 98</span>| 448.23423 | 1.005 | -1.862 | -0.9802 | -0.9885 | +#> |.....................| -0.8649 | 1.225 | -2.731 | -1.570 | +#> |.....................| 1.863 | -0.9934 | -1.058 | -0.5422 | +#> <span style='text-decoration: underline;'>|.....................| -0.6330 | -0.6404 |...........|...........|</span> +#> | U| 448.23423 | 93.60 | -6.165 | -1.009 | -0.2007 | +#> |.....................| 2.272 | 2.374 | 0.002415 | 0.7529 | +#> |.....................| 0.07145 | 0.6695 | 0.7131 | 1.608 | +#> <span style='text-decoration: underline;'>|.....................| 1.323 | 1.319 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 448.23423</span> | 93.60 | 0.002101 | 0.2671 | 0.8182 | +#> |.....................| 9.695 | 2.374 | 0.002415 | 0.7529 | +#> |.....................| 0.07145 | 0.6695 | 0.7131 | 1.608 | +#> <span style='text-decoration: underline;'>|.....................| 1.323 | 1.319 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 99</span>| 448.52797 | 1.003 | -1.887 | -0.9721 | -0.9832 | +#> |.....................| -0.7539 | 1.222 | -2.732 | -1.631 | +#> |.....................| 2.296 | -0.9358 | -1.078 | -0.5592 | +#> <span style='text-decoration: underline;'>|.....................| -0.6753 | -0.5215 |...........|...........|</span> +#> | U| 448.52797 | 93.41 | -6.190 | -1.001 | -0.1954 | +#> |.....................| 2.383 | 2.371 | 0.002396 | 0.7173 | +#> |.....................| 0.07796 | 0.7131 | 0.6963 | 1.588 | +#> <span style='text-decoration: underline;'>|.....................| 1.277 | 1.446 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 448.52797</span> | 93.41 | 0.002050 | 0.2687 | 0.8225 | +#> |.....................| 10.83 | 2.371 | 0.002396 | 0.7173 | +#> |.....................| 0.07796 | 0.7131 | 0.6963 | 1.588 | +#> <span style='text-decoration: underline;'>|.....................| 1.277 | 1.446 |...........|...........|</span> +#> | F| Forward Diff. | -1.417 | -0.03842 | -1.058 | -1.257 | +#> |.....................| 1.697 | 0.2446 | 0.02601 | -1.725 | +#> |.....................| -1.728 | -0.7541 | 3.822 | 2.423 | +#> <span style='text-decoration: underline;'>|.....................| 0.4552 | 1.132 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 100</span>| 447.48636 | 1.010 | -1.889 | -1.018 | -0.9136 | +#> |.....................| -0.9465 | 1.241 | -2.741 | -1.706 | +#> |.....................| 2.465 | -0.9635 | -1.095 | -0.5705 | +#> <span style='text-decoration: underline;'>|.....................| -0.6276 | -0.6598 |...........|...........|</span> +#> | U| 447.48636 | 94.00 | -6.193 | -1.045 | -0.1257 | +#> |.....................| 2.190 | 2.383 | 0.002265 | 0.6743 | +#> |.....................| 0.08050 | 0.6921 | 0.6807 | 1.574 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.298 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.48636</span> | 94.00 | 0.002044 | 0.2602 | 0.8818 | +#> |.....................| 8.935 | 2.383 | 0.002265 | 0.6743 | +#> |.....................| 0.08050 | 0.6921 | 0.6807 | 1.574 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.298 |...........|...........|</span> +#> | F| Forward Diff. | 49.18 | 0.06228 | -2.520 | 1.219 | +#> |.....................| -0.3402 | 0.5332 | 0.01803 | -1.013 | +#> |.....................| -0.7363 | 0.9697 | 2.720 | 0.6118 | +#> <span style='text-decoration: underline;'>|.....................| 0.4882 | -0.1519 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 101</span>| 448.59314 | 1.009 | -1.906 | -0.9798 | -1.202 | +#> |.....................| -1.107 | 1.243 | -2.730 | -1.791 | +#> |.....................| 2.989 | -0.9474 | -1.110 | -0.5914 | +#> <span style='text-decoration: underline;'>|.....................| -0.6423 | -0.5882 |...........|...........|</span> +#> | U| 448.59314 | 93.96 | -6.209 | -1.009 | -0.4139 | +#> |.....................| 2.029 | 2.384 | 0.002422 | 0.6247 | +#> |.....................| 0.08837 | 0.7043 | 0.6679 | 1.549 | +#> <span style='text-decoration: underline;'>|.....................| 1.313 | 1.375 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 448.59314</span> | 93.96 | 0.002010 | 0.2672 | 0.6611 | +#> |.....................| 7.610 | 2.384 | 0.002422 | 0.6247 | +#> |.....................| 0.08837 | 0.7043 | 0.6679 | 1.549 | +#> <span style='text-decoration: underline;'>|.....................| 1.313 | 1.375 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 102</span>| 447.34338 | 1.004 | -1.893 | -1.010 | -0.9727 | +#> |.....................| -0.9794 | 1.241 | -2.739 | -1.723 | +#> |.....................| 2.572 | -0.9603 | -1.099 | -0.5748 | +#> <span style='text-decoration: underline;'>|.....................| -0.6307 | -0.6452 |...........|...........|</span> +#> | U| 447.34338 | 93.48 | -6.196 | -1.037 | -0.1848 | +#> |.....................| 2.157 | 2.383 | 0.002297 | 0.6642 | +#> |.....................| 0.08211 | 0.6946 | 0.6778 | 1.569 | +#> <span style='text-decoration: underline;'>|.....................| 1.325 | 1.314 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.34338</span> | 93.48 | 0.002037 | 0.2617 | 0.8313 | +#> |.....................| 8.647 | 2.383 | 0.002297 | 0.6642 | +#> |.....................| 0.08211 | 0.6946 | 0.6778 | 1.569 | +#> <span style='text-decoration: underline;'>|.....................| 1.325 | 1.314 |...........|...........|</span> +#> | F| Forward Diff. | -27.99 | 0.05620 | -2.283 | -0.5861 | +#> |.....................| -1.399 | 0.3409 | -0.05316 | -0.7185 | +#> |.....................| -0.6589 | 0.7167 | 1.472 | 0.2167 | +#> <span style='text-decoration: underline;'>|.....................| 0.2339 | 0.7351 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 103</span>| 447.24116 | 1.004 | -1.898 | -0.9880 | -0.9438 | +#> |.....................| -0.9421 | 1.243 | -2.723 | -1.759 | +#> |.....................| 2.683 | -0.9737 | -1.096 | -0.5790 | +#> <span style='text-decoration: underline;'>|.....................| -0.6284 | -0.6557 |...........|...........|</span> +#> | U| 447.24116 | 93.50 | -6.201 | -1.017 | -0.1559 | +#> |.....................| 2.195 | 2.384 | 0.002530 | 0.6435 | +#> |.....................| 0.08377 | 0.6844 | 0.6802 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.302 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.24116</span> | 93.50 | 0.002027 | 0.2657 | 0.8556 | +#> |.....................| 8.976 | 2.384 | 0.002530 | 0.6435 | +#> |.....................| 0.08377 | 0.6844 | 0.6802 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.302 |...........|...........|</span> +#> | F| Forward Diff. | -22.25 | 0.02611 | -1.124 | 0.2366 | +#> |.....................| -0.4078 | 0.2597 | -0.06938 | -0.8187 | +#> |.....................| -0.5375 | 0.002218 | 1.533 | -0.1306 | +#> <span style='text-decoration: underline;'>|.....................| 0.2372 | 0.1318 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 104</span>| 447.36545 | 1.010 | -1.910 | -0.9563 | -1.018 | +#> |.....................| -0.9640 | 1.238 | -2.696 | -1.806 | +#> |.....................| 2.921 | -0.9760 | -1.100 | -0.5866 | +#> <span style='text-decoration: underline;'>|.....................| -0.6320 | -0.6434 |...........|...........|</span> +#> | U| 447.36545 | 94.05 | -6.214 | -0.9866 | -0.2304 | +#> |.....................| 2.173 | 2.381 | 0.002941 | 0.6159 | +#> |.....................| 0.08734 | 0.6827 | 0.6770 | 1.554 | +#> <span style='text-decoration: underline;'>|.....................| 1.324 | 1.315 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.36545</span> | 94.05 | 0.002002 | 0.2716 | 0.7942 | +#> |.....................| 8.780 | 2.381 | 0.002941 | 0.6159 | +#> |.....................| 0.08734 | 0.6827 | 0.6770 | 1.554 | +#> <span style='text-decoration: underline;'>|.....................| 1.324 | 1.315 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 105</span>| 447.25244 | 1.009 | -1.902 | -0.9770 | -0.9694 | +#> |.....................| -0.9495 | 1.241 | -2.714 | -1.775 | +#> |.....................| 2.765 | -0.9745 | -1.097 | -0.5816 | +#> <span style='text-decoration: underline;'>|.....................| -0.6297 | -0.6515 |...........|...........|</span> +#> | U| 447.25244 | 93.94 | -6.205 | -1.006 | -0.1815 | +#> |.....................| 2.187 | 2.383 | 0.002671 | 0.6341 | +#> |.....................| 0.08500 | 0.6838 | 0.6790 | 1.560 | +#> <span style='text-decoration: underline;'>|.....................| 1.326 | 1.307 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.25244</span> | 93.94 | 0.002018 | 0.2677 | 0.8340 | +#> |.....................| 8.909 | 2.383 | 0.002671 | 0.6341 | +#> |.....................| 0.08500 | 0.6838 | 0.6790 | 1.560 | +#> <span style='text-decoration: underline;'>|.....................| 1.326 | 1.307 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 106</span>| 447.24908 | 1.008 | -1.900 | -0.9828 | -0.9557 | +#> |.....................| -0.9455 | 1.242 | -2.719 | -1.766 | +#> |.....................| 2.721 | -0.9741 | -1.097 | -0.5802 | +#> <span style='text-decoration: underline;'>|.....................| -0.6290 | -0.6537 |...........|...........|</span> +#> | U| 447.24908 | 93.91 | -6.203 | -1.012 | -0.1678 | +#> |.....................| 2.191 | 2.383 | 0.002596 | 0.6392 | +#> |.....................| 0.08434 | 0.6841 | 0.6795 | 1.562 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.304 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.24908</span> | 93.91 | 0.002023 | 0.2667 | 0.8455 | +#> |.....................| 8.945 | 2.383 | 0.002596 | 0.6392 | +#> |.....................| 0.08434 | 0.6841 | 0.6795 | 1.562 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.304 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 107</span>| 447.25180 | 1.008 | -1.899 | -0.9855 | -0.9493 | +#> |.....................| -0.9436 | 1.242 | -2.721 | -1.762 | +#> |.....................| 2.700 | -0.9739 | -1.097 | -0.5796 | +#> <span style='text-decoration: underline;'>|.....................| -0.6287 | -0.6548 |...........|...........|</span> +#> | U| 447.2518 | 93.89 | -6.202 | -1.014 | -0.1614 | +#> |.....................| 2.193 | 2.383 | 0.002560 | 0.6416 | +#> |.....................| 0.08403 | 0.6843 | 0.6798 | 1.563 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.303 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.2518</span> | 93.89 | 0.002025 | 0.2662 | 0.8509 | +#> |.....................| 8.962 | 2.383 | 0.002560 | 0.6416 | +#> |.....................| 0.08403 | 0.6843 | 0.6798 | 1.563 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.303 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 108</span>| 447.25421 | 1.008 | -1.898 | -0.9869 | -0.9460 | +#> |.....................| -0.9426 | 1.242 | -2.722 | -1.760 | +#> |.....................| 2.690 | -0.9738 | -1.096 | -0.5792 | +#> <span style='text-decoration: underline;'>|.....................| -0.6286 | -0.6553 |...........|...........|</span> +#> | U| 447.25421 | 93.88 | -6.202 | -1.015 | -0.1582 | +#> |.....................| 2.194 | 2.384 | 0.002542 | 0.6428 | +#> |.....................| 0.08388 | 0.6843 | 0.6799 | 1.563 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.303 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.25421</span> | 93.88 | 0.002026 | 0.2659 | 0.8537 | +#> |.....................| 8.970 | 2.384 | 0.002542 | 0.6428 | +#> |.....................| 0.08388 | 0.6843 | 0.6799 | 1.563 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.303 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 109</span>| 447.24978 | 1.008 | -1.898 | -0.9878 | -0.9438 | +#> |.....................| -0.9420 | 1.242 | -2.723 | -1.759 | +#> |.....................| 2.683 | -0.9737 | -1.096 | -0.5790 | +#> <span style='text-decoration: underline;'>|.....................| -0.6285 | -0.6557 |...........|...........|</span> +#> | U| 447.24978 | 93.86 | -6.201 | -1.016 | -0.1560 | +#> |.....................| 2.195 | 2.384 | 0.002530 | 0.6436 | +#> |.....................| 0.08377 | 0.6844 | 0.6800 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.302 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.24978</span> | 93.86 | 0.002027 | 0.2657 | 0.8556 | +#> |.....................| 8.976 | 2.384 | 0.002530 | 0.6436 | +#> |.....................| 0.08377 | 0.6844 | 0.6800 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.302 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 110</span>| 447.22094 | 1.006 | -1.898 | -0.9879 | -0.9438 | +#> |.....................| -0.9420 | 1.243 | -2.723 | -1.759 | +#> |.....................| 2.683 | -0.9737 | -1.096 | -0.5790 | +#> <span style='text-decoration: underline;'>|.....................| -0.6284 | -0.6557 |...........|...........|</span> +#> | U| 447.22094 | 93.66 | -6.201 | -1.016 | -0.1560 | +#> |.....................| 2.195 | 2.384 | 0.002530 | 0.6435 | +#> |.....................| 0.08377 | 0.6844 | 0.6801 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.302 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.22094</span> | 93.66 | 0.002027 | 0.2657 | 0.8556 | +#> |.....................| 8.976 | 2.384 | 0.002530 | 0.6435 | +#> |.....................| 0.08377 | 0.6844 | 0.6801 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.328 | 1.302 |...........|...........|</span> +#> | F| Forward Diff. | 0.7136 | 0.03206 | -1.028 | 0.2620 | +#> |.....................| -0.3312 | 0.3050 | -0.05505 | -0.8960 | +#> |.....................| -0.4549 | 0.03409 | 2.494 | -0.1555 | +#> <span style='text-decoration: underline;'>|.....................| 0.2265 | 0.1085 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 111</span>| 447.21344 | 1.005 | -1.898 | -0.9873 | -0.9440 | +#> |.....................| -0.9418 | 1.242 | -2.723 | -1.758 | +#> |.....................| 2.683 | -0.9737 | -1.098 | -0.5789 | +#> <span style='text-decoration: underline;'>|.....................| -0.6286 | -0.6557 |...........|...........|</span> +#> | U| 447.21344 | 93.62 | -6.201 | -1.016 | -0.1561 | +#> |.....................| 2.195 | 2.384 | 0.002531 | 0.6439 | +#> |.....................| 0.08377 | 0.6844 | 0.6789 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.302 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.21344</span> | 93.62 | 0.002027 | 0.2658 | 0.8555 | +#> |.....................| 8.978 | 2.384 | 0.002531 | 0.6439 | +#> |.....................| 0.08377 | 0.6844 | 0.6789 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.302 |...........|...........|</span> +#> | F| Forward Diff. | -4.689 | 0.03686 | -1.013 | 0.2539 | +#> |.....................| -0.3408 | 0.6592 | 0.03740 | -0.5502 | +#> |.....................| -0.2201 | 0.3219 | 2.382 | -0.1778 | +#> <span style='text-decoration: underline;'>|.....................| 0.2028 | 0.08770 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 112</span>| 447.19216 | 1.006 | -1.899 | -0.9854 | -0.9463 | +#> |.....................| -0.9420 | 1.239 | -2.724 | -1.756 | +#> |.....................| 2.680 | -0.9744 | -1.101 | -0.5784 | +#> <span style='text-decoration: underline;'>|.....................| -0.6293 | -0.6560 |...........|...........|</span> +#> | U| 447.19216 | 93.64 | -6.203 | -1.014 | -0.1585 | +#> |.....................| 2.195 | 2.382 | 0.002523 | 0.6453 | +#> |.....................| 0.08373 | 0.6839 | 0.6759 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.302 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.19216</span> | 93.64 | 0.002024 | 0.2662 | 0.8535 | +#> |.....................| 8.976 | 2.382 | 0.002523 | 0.6453 | +#> |.....................| 0.08373 | 0.6839 | 0.6759 | 1.564 | +#> <span style='text-decoration: underline;'>|.....................| 1.327 | 1.302 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 113</span>| 447.14896 | 1.005 | -1.904 | -0.9796 | -0.9535 | +#> |.....................| -0.9426 | 1.230 | -2.725 | -1.748 | +#> |.....................| 2.670 | -0.9764 | -1.111 | -0.5767 | +#> <span style='text-decoration: underline;'>|.....................| -0.6315 | -0.6570 |...........|...........|</span> +#> | U| 447.14896 | 93.56 | -6.208 | -1.009 | -0.1657 | +#> |.....................| 2.194 | 2.376 | 0.002500 | 0.6498 | +#> |.....................| 0.08358 | 0.6823 | 0.6675 | 1.566 | +#> <span style='text-decoration: underline;'>|.....................| 1.324 | 1.301 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.14896</span> | 93.56 | 0.002014 | 0.2673 | 0.8473 | +#> |.....................| 8.971 | 2.376 | 0.002500 | 0.6498 | +#> |.....................| 0.08358 | 0.6823 | 0.6675 | 1.566 | +#> <span style='text-decoration: underline;'>|.....................| 1.324 | 1.301 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 114</span>| 447.12523 | 1.003 | -1.923 | -0.9566 | -0.9821 | +#> |.....................| -0.9448 | 1.194 | -2.731 | -1.717 | +#> |.....................| 2.632 | -0.9846 | -1.149 | -0.5701 | +#> <span style='text-decoration: underline;'>|.....................| -0.6401 | -0.6607 |...........|...........|</span> +#> | U| 447.12523 | 93.36 | -6.227 | -0.9868 | -0.1943 | +#> |.....................| 2.192 | 2.355 | 0.002410 | 0.6677 | +#> |.....................| 0.08300 | 0.6762 | 0.6336 | 1.574 | +#> <span style='text-decoration: underline;'>|.....................| 1.315 | 1.297 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.12523</span> | 93.36 | 0.001976 | 0.2715 | 0.8234 | +#> |.....................| 8.951 | 2.355 | 0.002410 | 0.6677 | +#> |.....................| 0.08300 | 0.6762 | 0.6336 | 1.574 | +#> <span style='text-decoration: underline;'>|.....................| 1.315 | 1.297 |...........|...........|</span> +#> | F| Forward Diff. | -42.78 | 0.1470 | 0.5793 | -0.8455 | +#> |.....................| -0.3546 | -0.4331 | -0.1071 | -0.02049 | +#> |.....................| -0.3358 | -0.3904 | -2.177 | 0.2043 | +#> <span style='text-decoration: underline;'>|.....................| -0.1377 | -0.3207 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 115</span>| 447.09924 | 1.007 | -1.940 | -0.9416 | -1.018 | +#> |.....................| -0.9550 | 1.181 | -2.719 | -1.734 | +#> |.....................| 2.734 | -0.9861 | -1.153 | -0.5706 | +#> <span style='text-decoration: underline;'>|.....................| -0.6433 | -0.6564 |...........|...........|</span> +#> | U| 447.09924 | 93.80 | -6.243 | -0.9727 | -0.2297 | +#> |.....................| 2.182 | 2.348 | 0.002591 | 0.6578 | +#> |.....................| 0.08453 | 0.6750 | 0.6303 | 1.574 | +#> <span style='text-decoration: underline;'>|.....................| 1.312 | 1.301 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.09924</span> | 93.80 | 0.001943 | 0.2743 | 0.7947 | +#> |.....................| 8.860 | 2.348 | 0.002591 | 0.6578 | +#> |.....................| 0.08453 | 0.6750 | 0.6303 | 1.574 | +#> <span style='text-decoration: underline;'>|.....................| 1.312 | 1.301 |...........|...........|</span> +#> | F| Forward Diff. | 15.04 | 0.1387 | 1.646 | -1.777 | +#> |.....................| -0.3749 | -0.5049 | -0.07528 | 0.1505 | +#> |.....................| -0.2071 | -0.6675 | -2.129 | 0.2735 | +#> <span style='text-decoration: underline;'>|.....................| -0.05533 | -0.2849 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 116</span>| 447.06926 | 1.008 | -1.968 | -0.9759 | -0.9363 | +#> |.....................| -0.9300 | 1.192 | -2.714 | -1.733 | +#> |.....................| 2.676 | -0.9757 | -1.142 | -0.5672 | +#> <span style='text-decoration: underline;'>|.....................| -0.6383 | -0.6598 |...........|...........|</span> +#> | U| 447.06926 | 93.90 | -6.272 | -1.005 | -0.1484 | +#> |.....................| 2.207 | 2.354 | 0.002664 | 0.6586 | +#> |.....................| 0.08367 | 0.6829 | 0.6398 | 1.578 | +#> <span style='text-decoration: underline;'>|.....................| 1.317 | 1.298 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.06926</span> | 93.90 | 0.001889 | 0.2679 | 0.8621 | +#> |.....................| 9.084 | 2.354 | 0.002664 | 0.6586 | +#> |.....................| 0.08367 | 0.6829 | 0.6398 | 1.578 | +#> <span style='text-decoration: underline;'>|.....................| 1.317 | 1.298 |...........|...........|</span> +#> | F| Forward Diff. | 31.57 | 0.06960 | -0.1881 | 0.5445 | +#> |.....................| 0.2088 | -0.3879 | -0.06801 | -0.3419 | +#> |.....................| -0.4021 | 0.02711 | -1.273 | 0.2199 | +#> <span style='text-decoration: underline;'>|.....................| -0.1004 | -0.4182 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 117</span>| 447.12806 | 1.006 | -2.047 | -0.9734 | -0.9587 | +#> |.....................| -0.9336 | 1.189 | -2.704 | -1.764 | +#> |.....................| 2.737 | -0.9879 | -1.112 | -0.5826 | +#> <span style='text-decoration: underline;'>|.....................| -0.6349 | -0.6438 |...........|...........|</span> +#> | U| 447.12806 | 93.67 | -6.350 | -1.003 | -0.1708 | +#> |.....................| 2.203 | 2.352 | 0.002825 | 0.6405 | +#> |.....................| 0.08458 | 0.6737 | 0.6664 | 1.559 | +#> <span style='text-decoration: underline;'>|.....................| 1.321 | 1.315 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.12806</span> | 93.67 | 0.001747 | 0.2684 | 0.8430 | +#> |.....................| 9.052 | 2.352 | 0.002825 | 0.6405 | +#> |.....................| 0.08458 | 0.6737 | 0.6664 | 1.559 | +#> <span style='text-decoration: underline;'>|.....................| 1.321 | 1.315 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 118</span>| 447.05003 | 1.006 | -1.997 | -0.9750 | -0.9445 | +#> |.....................| -0.9313 | 1.191 | -2.710 | -1.744 | +#> |.....................| 2.698 | -0.9801 | -1.131 | -0.5728 | +#> <span style='text-decoration: underline;'>|.....................| -0.6370 | -0.6539 |...........|...........|</span> +#> | U| 447.05003 | 93.71 | -6.300 | -1.004 | -0.1566 | +#> |.....................| 2.205 | 2.354 | 0.002723 | 0.6520 | +#> |.....................| 0.08400 | 0.6796 | 0.6495 | 1.571 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.304 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.05003</span> | 93.71 | 0.001836 | 0.2681 | 0.8551 | +#> |.....................| 9.073 | 2.354 | 0.002723 | 0.6520 | +#> |.....................| 0.08400 | 0.6796 | 0.6495 | 1.571 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.304 |...........|...........|</span> +#> | F| Forward Diff. | 4.860 | -0.01375 | -0.2473 | 0.2780 | +#> |.....................| 0.08862 | -0.4372 | -0.08802 | -0.3404 | +#> |.....................| -0.3654 | -0.2345 | -0.3468 | 0.08396 | +#> <span style='text-decoration: underline;'>|.....................| -0.01035 | -0.06837 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 119</span>| 447.04716 | 1.006 | -1.989 | -0.9725 | -0.9518 | +#> |.....................| -0.9334 | 1.193 | -2.718 | -1.756 | +#> |.....................| 2.735 | -0.9825 | -1.129 | -0.5738 | +#> <span style='text-decoration: underline;'>|.....................| -0.6372 | -0.6523 |...........|...........|</span> +#> | U| 447.04716 | 93.69 | -6.292 | -1.002 | -0.1639 | +#> |.....................| 2.203 | 2.355 | 0.002610 | 0.6452 | +#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.306 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.04716</span> | 93.69 | 0.001850 | 0.2686 | 0.8488 | +#> |.....................| 9.053 | 2.355 | 0.002610 | 0.6452 | +#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.306 |...........|...........|</span> +#> | F| Forward Diff. | 2.456 | -0.007589 | -0.1181 | 0.06051 | +#> |.....................| 0.03158 | -0.4028 | -0.08358 | -0.4018 | +#> |.....................| -0.3358 | -0.3459 | -0.2609 | 0.03632 | +#> <span style='text-decoration: underline;'>|.....................| -0.03277 | 0.02331 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 120</span>| 447.04716 | 1.006 | -1.989 | -0.9725 | -0.9518 | +#> |.....................| -0.9334 | 1.193 | -2.718 | -1.756 | +#> |.....................| 2.735 | -0.9825 | -1.129 | -0.5738 | +#> <span style='text-decoration: underline;'>|.....................| -0.6372 | -0.6523 |...........|...........|</span> +#> | U| 447.04716 | 93.69 | -6.292 | -1.002 | -0.1639 | +#> |.....................| 2.203 | 2.355 | 0.002610 | 0.6452 | +#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.306 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 447.04716</span> | 93.69 | 0.001850 | 0.2686 | 0.8488 | +#> |.....................| 9.053 | 2.355 | 0.002610 | 0.6452 | +#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | +#> <span style='text-decoration: underline;'>|.....................| 1.318 | 1.306 |...........|...........|</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: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</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='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: 0.799 0.044 0.842</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'>#> 1: 9.1294e+01 -5.0486e+00 -1.7441e+00 -3.5640e+00 -2.1387e+00 4.8639e-01 5.5948e+00 1.4680e+00 1.1057e+00 2.3810e+00 4.8150e-01 4.3452e-01 1.0359e+01 2.3790e-05 7.8082e+00 5.1813e-01 +#> 2: 9.1224e+01 -5.2308e+00 -1.9743e+00 -4.0115e+00 -1.8311e+00 9.8058e-02 5.3151e+00 1.3946e+00 1.0504e+00 2.8908e+00 4.5742e-01 5.2252e-01 5.9132e+00 5.7000e-04 6.5362e+00 1.8571e-07 +#> 3: 9.1371e+01 -5.5075e+00 -2.1136e+00 -4.0542e+00 -1.4871e+00 -4.1222e-02 5.0493e+00 1.3249e+00 9.9785e-01 3.4546e+00 4.3455e-01 6.3380e-01 4.0626e+00 1.0302e-05 4.6845e+00 5.0378e-04 +#> 4: 91.3391 -5.7912 -2.1450 -3.9623 -1.3302 -0.1356 4.7969 1.2586 0.9480 3.2819 0.4128 0.6021 3.3624 0.0249 3.6770 0.0248 +#> 5: 91.5018 -6.0214 -2.1492 -3.9323 -1.2118 -0.0647 4.5570 1.1957 0.9006 3.1178 0.3922 0.5720 2.9393 0.0349 3.1610 0.0371 +#> 6: 91.4496 -5.8734 -2.0974 -3.9977 -1.0936 -0.0608 4.3292 1.3347 0.8695 3.1231 0.3726 0.5434 2.5921 0.0366 2.7534 0.0396 +#> 7: 91.6540 -5.8545 -2.1019 -3.9268 -0.9717 -0.1622 4.1127 1.8221 0.8771 2.9670 0.3539 0.5162 2.3468 0.0466 2.4323 0.0474 +#> 8: 91.7226 -5.8139 -2.0764 -4.0030 -0.9804 -0.1283 3.9071 2.3972 0.9978 2.9945 0.3362 0.4904 2.0001 0.0405 2.0620 0.0557 +#> 9: 91.9975 -5.6339 -2.0812 -3.9379 -0.9156 -0.0654 4.4265 2.2773 0.9479 3.0945 0.3194 0.4659 1.8817 0.0397 1.4473 0.0845 +#> 10: 91.9477 -5.6101 -2.0459 -3.8821 -0.9368 -0.0428 4.9868 2.2787 0.9297 3.2162 0.3035 0.4426 1.6910 0.0397 1.3759 0.0892 +#> 11: 92.1798 -5.5425 -2.0676 -3.9349 -0.9248 -0.0339 5.0312 2.1648 0.8832 3.0554 0.2883 0.4205 1.6613 0.0375 1.3387 0.0823 +#> 12: 92.1456 -5.6294 -2.1011 -3.8899 -0.9195 -0.0410 4.7796 2.0565 0.9077 3.1066 0.3013 0.3995 1.6018 0.0393 1.5496 0.0691 +#> 13: 91.6764 -5.5607 -2.0911 -3.8832 -0.9268 -0.0367 4.5407 1.9537 0.9246 3.0425 0.2862 0.3795 1.6900 0.0350 1.4050 0.0712 +#> 14: 91.4832 -5.5007 -2.1133 -3.8869 -0.9208 -0.0202 4.3136 1.8560 0.8795 3.0389 0.2719 0.3605 1.5526 0.0389 1.7056 0.0502 +#> 15: 91.7854 -5.4454 -2.1124 -3.8750 -0.8842 -0.0608 4.0979 1.7632 0.9004 3.0463 0.2583 0.3425 1.6201 0.0384 1.2463 0.0747 +#> 16: 91.7608 -5.4097 -2.1449 -3.8750 -0.8797 -0.0532 3.8930 1.6751 0.9666 3.0463 0.2454 0.3254 1.6086 0.0384 1.0840 0.0850 +#> 17: 91.6692 -5.5401 -2.1688 -3.8762 -0.9022 -0.0101 3.6984 1.8405 1.0323 2.9672 0.2331 0.3091 1.4625 0.0371 1.1135 0.0841 +#> 18: 91.3169 -5.5720 -2.1777 -3.8851 -0.9396 0.0040 3.5135 1.8186 1.0419 3.0783 0.2215 0.2936 1.4778 0.0396 1.3403 0.0732 +#> 19: 91.4384 -5.6696 -2.1469 -3.8892 -0.9318 -0.0103 3.3378 2.2700 1.0489 3.0592 0.2128 0.2790 1.3854 0.0379 1.1760 0.0858 +#> 20: 91.3273 -5.7800 -2.1388 -3.9004 -0.9536 -0.0159 3.1709 2.7506 1.0297 3.0477 0.2021 0.2650 1.4542 0.0419 1.1576 0.0856 +#> 21: 91.7477 -5.7952 -2.1436 -3.9164 -0.9263 -0.0184 3.0124 3.0737 1.0414 3.0435 0.1948 0.2518 1.5026 0.0398 1.1833 0.0791 +#> 22: 91.6492 -6.0575 -2.1196 -3.9168 -0.9471 -0.0153 2.8617 4.1317 1.0322 3.0494 0.1850 0.2392 1.4351 0.0409 1.0739 0.0873 +#> 23: 91.8536 -6.2824 -2.1596 -3.9174 -0.9405 0.0031 2.7187 5.3935 1.0143 3.1085 0.1758 0.2272 1.4534 0.0404 1.0651 0.0805 +#> 24: 92.1616 -6.2246 -2.0912 -3.9224 -0.9338 0.0118 2.5827 5.7533 0.9636 3.0780 0.1741 0.2158 1.5863 0.0336 1.0915 0.0804 +#> 25: 92.2576 -6.2746 -2.1058 -3.9587 -0.9355 0.0189 2.4536 5.4656 0.9706 3.3477 0.1780 0.2051 1.4555 0.0365 1.0838 0.0782 +#> 26: 92.3314 -6.1739 -2.1211 -3.9676 -0.9474 0.0525 2.4934 5.5785 0.9981 3.3705 0.1835 0.1948 1.4433 0.0379 1.1300 0.0783 +#> 27: 92.8206 -6.1111 -2.0900 -3.9787 -0.9472 0.0058 2.5201 5.4329 1.0145 3.5013 0.1856 0.1851 1.4484 0.0391 1.1809 0.0723 +#> 28: 92.8685 -6.0934 -2.0963 -3.9872 -0.9693 0.0053 2.9812 5.1612 0.9925 3.5416 0.1816 0.1758 1.4713 0.0389 1.1766 0.0704 +#> 29: 92.6774 -5.8779 -2.0833 -3.9954 -0.9546 -0.0099 4.3751 4.9032 1.0762 3.5483 0.1755 0.1670 1.4844 0.0378 1.3435 0.0599 +#> 30: 92.6704 -5.9657 -2.0746 -3.9920 -0.9342 -0.0329 4.1563 4.6580 1.0571 3.5382 0.1667 0.1587 1.4510 0.0427 1.2218 0.0678 +#> 31: 92.4139 -5.7428 -2.0922 -3.9765 -0.9178 -0.0302 3.9485 4.4251 1.0210 3.5601 0.1596 0.1507 1.5981 0.0349 1.3086 0.0619 +#> 32: 92.8243 -5.8072 -2.1154 -3.9699 -0.9130 0.0065 3.7511 4.2039 1.0622 3.4768 0.1667 0.1432 1.5321 0.0333 1.3779 0.0611 +#> 33: 92.8737 -5.6655 -2.1132 -3.9763 -0.9155 0.0183 3.5635 3.9937 1.1068 3.5075 0.1583 0.1360 1.5351 0.0341 1.2700 0.0673 +#> 34: 93.0233 -5.7429 -2.1022 -3.9648 -0.9057 0.0202 3.3853 3.7940 1.0830 3.4532 0.1504 0.1292 1.5128 0.0368 1.1942 0.0702 +#> 35: 93.1333 -5.7707 -2.1003 -4.0004 -0.9031 0.0201 3.2161 3.6043 1.1161 3.4701 0.1429 0.1228 1.6003 0.0307 1.1387 0.0734 +#> 36: 93.1398 -5.7700 -2.1168 -3.9678 -0.9038 0.0107 3.0553 3.4241 1.1209 3.4126 0.1358 0.1166 1.4919 0.0331 1.0642 0.0755 +#> 37: 92.8847 -5.6651 -2.1538 -3.9634 -0.9176 0.0364 2.9995 3.2529 1.1108 3.3776 0.1402 0.1173 1.5093 0.0396 1.1550 0.0693 +#> 38: 93.2326 -5.5244 -2.1571 -3.9909 -0.9231 0.0179 2.8832 3.0902 1.0763 3.5170 0.1332 0.1205 1.4962 0.0472 1.1657 0.0679 +#> 39: 92.9946 -5.4516 -2.1475 -3.9365 -0.9067 0.0309 3.0986 2.9357 1.0562 3.4194 0.1265 0.1251 1.4786 0.0464 1.1183 0.0721 +#> 40: 93.2028 -5.6148 -2.1367 -3.9235 -0.9048 0.0099 2.9436 2.7889 1.1256 3.3460 0.1241 0.1288 1.4515 0.0459 1.0449 0.0753 +#> 41: 93.1297 -5.4665 -2.0545 -4.0108 -0.9136 -0.0216 2.7964 2.6495 1.1471 3.4754 0.1281 0.1223 1.7359 0.0321 1.0876 0.0780 +#> 42: 93.0469 -5.3767 -2.0820 -4.0213 -0.9361 -0.0264 2.6566 2.5170 1.0897 3.5120 0.1411 0.1162 1.7070 0.0276 1.2377 0.0691 +#> 43: 93.3305 -5.4943 -2.0910 -4.0226 -0.9414 -0.0201 2.5238 2.3912 1.0896 3.4589 0.1621 0.1126 1.5584 0.0393 1.1485 0.0705 +#> 44: 93.2566 -5.4919 -2.1016 -4.0718 -0.9373 0.0024 2.3976 2.2716 1.0451 3.8959 0.1612 0.1162 1.5769 0.0286 1.2778 0.0693 +#> 45: 93.0284 -5.4885 -2.1012 -4.0740 -0.9202 -0.0197 2.2777 2.1580 1.0268 3.9297 0.1553 0.1104 1.5589 0.0289 1.1388 0.0778 +#> 46: 92.7188 -5.5807 -2.1102 -4.0875 -0.9465 0.0076 2.1638 2.2084 0.9840 4.0322 0.1475 0.1048 1.6729 0.0295 1.2763 0.0735 +#> 47: 92.6718 -5.5108 -2.1268 -4.0638 -0.9220 0.0131 2.0556 2.0980 1.0064 3.8306 0.1475 0.0996 1.6527 0.0271 1.3190 0.0659 +#> 48: 92.6727 -5.5268 -2.1326 -4.0693 -0.8999 0.0259 1.9529 2.2445 1.0387 3.8064 0.1459 0.0946 1.6587 0.0283 1.3555 0.0604 +#> 49: 92.5230 -5.5592 -2.1701 -4.0595 -0.9087 0.0350 1.8552 2.5181 1.0238 3.7514 0.1552 0.0899 1.5473 0.0307 1.2437 0.0662 +#> 50: 92.4920 -5.5778 -2.1309 -4.0711 -0.9317 0.0383 1.7625 2.6771 1.0203 3.7435 0.1587 0.0854 1.5727 0.0330 1.2555 0.0611 +#> 51: 92.4606 -5.5485 -2.1346 -4.0687 -0.9148 0.0638 1.6743 2.8079 1.0402 3.6978 0.1513 0.0811 1.5476 0.0335 1.2744 0.0658 +#> 52: 92.6305 -5.6829 -2.1658 -4.0697 -0.9298 0.0848 1.5906 2.8530 1.0565 3.6998 0.1644 0.0798 1.4751 0.0296 1.1351 0.0747 +#> 53: 92.6412 -5.5519 -2.1984 -4.1605 -0.9472 0.0803 1.8328 2.7103 1.0501 4.4111 0.1626 0.0758 1.5735 0.0343 1.2247 0.0643 +#> 54: 92.7616 -5.5718 -2.1826 -4.2028 -0.9382 0.0939 1.9108 2.5748 1.0708 4.7287 0.1775 0.0720 1.4860 0.0299 1.2190 0.0638 +#> 55: 92.8466 -5.6434 -2.1590 -4.0501 -0.9219 0.0660 2.3709 2.4461 1.0399 4.4922 0.1686 0.0684 1.5899 0.0297 1.2586 0.0598 +#> 56: 92.8839 -5.6503 -2.1758 -4.0467 -0.9265 0.0765 2.2523 2.3238 1.0755 4.2676 0.1698 0.0666 1.5357 0.0319 1.1854 0.0633 +#> 57: 92.8882 -5.3950 -2.1926 -4.0282 -0.9455 0.0600 2.4994 2.2076 1.0411 4.0542 0.1684 0.0633 1.5839 0.0342 1.2789 0.0612 +#> 58: 92.9510 -5.4362 -2.1993 -4.0402 -0.9349 0.0576 2.3744 2.0972 1.0184 3.8515 0.1757 0.0604 1.5796 0.0328 1.3027 0.0570 +#> 59: 92.8806 -5.4605 -2.2176 -4.2201 -0.9360 0.0998 2.2557 1.9923 1.0248 5.1421 0.1904 0.0573 1.6469 0.0325 1.4177 0.0534 +#> 60: 92.8606 -5.4697 -2.2016 -4.1707 -0.9218 0.0747 2.1429 1.8927 1.0489 4.8850 0.1809 0.0545 1.5984 0.0318 1.2879 0.0589 +#> 61: 92.8939 -5.5167 -2.2169 -4.1567 -0.9434 0.0680 2.1067 1.9160 1.0677 4.6408 0.1775 0.0517 1.5223 0.0404 1.2033 0.0623 +#> 62: 93.1569 -5.6121 -2.2073 -4.1427 -0.9431 0.0717 2.5977 2.0627 1.0518 4.5133 0.1758 0.0494 1.4644 0.0364 1.1857 0.0621 +#> 63: 93.2362 -5.5056 -2.1832 -4.0832 -0.9433 0.0754 3.4639 1.9596 1.0905 4.2877 0.1851 0.0536 1.5500 0.0320 1.2533 0.0610 +#> 64: 93.3935 -5.4320 -2.1735 -4.0754 -0.9601 0.0719 5.0337 1.8616 1.0723 4.0733 0.1907 0.0649 1.5436 0.0270 1.4154 0.0546 +#> 65: 93.1102 -5.5419 -2.1870 -4.0496 -0.9481 0.0753 5.0250 1.9760 1.1263 3.8696 0.1902 0.0617 1.4779 0.0262 1.1326 0.0712 +#> 66: 92.9832 -5.7640 -2.1941 -4.0532 -0.9444 0.0635 5.2049 2.6553 1.1258 3.7699 0.1915 0.0586 1.4926 0.0307 1.0960 0.0645 +#> 67: 92.6674 -5.6976 -2.1858 -4.0855 -0.9209 0.0562 4.9447 2.5225 1.1285 4.0204 0.1948 0.0556 1.4667 0.0315 1.1023 0.0650 +#> 68: 92.7718 -5.7724 -2.1760 -4.0242 -0.9354 0.0441 4.6975 2.8536 1.1471 3.8194 0.1922 0.0529 1.4283 0.0329 1.1174 0.0664 +#> 69: 92.8377 -5.7554 -2.1833 -4.0670 -0.9412 0.0834 4.4626 2.7404 1.1565 3.7904 0.1826 0.0502 1.4628 0.0318 1.0793 0.0747 +#> 70: 92.6830 -5.9071 -2.2266 -4.0604 -0.9399 0.0730 4.2394 3.5629 1.1459 3.7282 0.1734 0.0477 1.4892 0.0331 1.1526 0.0683 +#> 71: 92.5729 -5.8185 -2.2009 -4.0623 -0.9401 0.0878 4.0275 3.3847 1.0886 3.7348 0.1648 0.0453 1.4739 0.0373 1.0902 0.0678 +#> 72: 92.1755 -6.0270 -2.2108 -4.1507 -0.9564 0.0665 3.8261 3.9851 1.1200 4.1726 0.1617 0.0431 1.4478 0.0348 1.1400 0.0673 +#> 73: 91.8986 -6.0175 -2.1916 -4.1416 -0.9347 0.0243 3.6348 4.0607 1.1553 4.0576 0.1802 0.0409 1.4330 0.0406 1.0914 0.0712 +#> 74: 91.7729 -5.8767 -2.1898 -4.0934 -0.9122 0.0184 3.4531 3.8577 1.1254 3.8547 0.1827 0.0389 1.3372 0.0524 1.0717 0.0687 +#> 75: 91.3098 -5.9950 -2.1572 -4.1349 -0.9427 0.0190 3.4756 3.8000 1.1626 3.8402 0.1969 0.0369 1.3378 0.0501 1.1602 0.0685 +#> 76: 91.3766 -5.8701 -2.2042 -4.1128 -0.9081 0.0539 3.9350 3.6100 1.2348 3.7994 0.1891 0.0369 1.3400 0.0495 1.0656 0.0738 +#> 77: 91.6057 -5.7437 -2.1988 -4.1241 -0.8890 0.0500 5.0868 3.4295 1.1971 3.8470 0.1950 0.0469 1.4928 0.0397 1.1129 0.0700 +#> 78: 91.7868 -5.7832 -2.1844 -4.1102 -0.9104 0.0698 4.8325 3.2580 1.1670 3.6547 0.1993 0.0502 1.4336 0.0340 0.9512 0.0805 +#> 79: 91.7221 -5.7881 -2.2166 -4.1137 -0.9160 0.0672 4.5909 3.0951 1.1582 3.5765 0.1928 0.0486 1.4632 0.0352 1.0210 0.0728 +#> 80: 91.8608 -5.8064 -2.2006 -4.0971 -0.9209 0.0642 4.3613 3.2163 1.1481 3.4758 0.1832 0.0462 1.4368 0.0356 1.0605 0.0710 +#> 81: 91.6423 -5.8749 -2.2037 -4.0893 -0.9187 0.0503 4.1432 3.5329 1.0907 3.5148 0.2011 0.0451 1.4719 0.0346 1.1684 0.0646 +#> 82: 91.8319 -6.0898 -2.2251 -4.0826 -0.9368 0.0842 4.1509 4.4964 1.0606 3.4836 0.1910 0.0428 1.4468 0.0387 1.1605 0.0637 +#> 83: 91.9794 -6.0417 -2.1947 -4.1042 -0.9114 0.0741 6.5949 4.5668 1.1113 3.6409 0.1815 0.0407 1.4780 0.0346 1.1277 0.0634 +#> 84: 91.8669 -6.1877 -2.1979 -4.1052 -0.9300 0.0807 6.2651 5.1958 1.1750 3.6752 0.1724 0.0386 1.4931 0.0278 1.0401 0.0685 +#> 85: 91.6789 -6.0634 -2.1896 -4.1357 -0.9371 0.0933 5.9519 4.9360 1.1259 3.8493 0.1732 0.0367 1.5058 0.0275 1.1356 0.0670 +#> 86: 91.6989 -6.2114 -2.2056 -4.1542 -0.9646 0.0882 5.6543 5.0411 1.1091 3.9411 0.1988 0.0349 1.4099 0.0338 1.1811 0.0636 +#> 87: 92.3758 -6.3779 -2.2062 -4.1739 -0.9385 0.0916 5.3716 6.2290 1.1213 4.0290 0.1889 0.0331 1.4809 0.0306 1.1443 0.0626 +#> 88: 92.2757 -6.2016 -2.2215 -4.1389 -0.9582 0.0942 5.1030 5.9176 1.0797 4.0768 0.1990 0.0315 1.4282 0.0386 1.2235 0.0629 +#> 89: 92.1970 -6.3356 -2.2081 -4.1412 -0.9555 0.1057 4.8478 5.9597 1.1474 4.0677 0.1890 0.0299 1.3856 0.0377 1.1807 0.0640 +#> 90: 92.0813 -6.4550 -2.2045 -4.1524 -0.9553 0.0885 4.6054 6.9999 1.1542 3.9901 0.1880 0.0284 1.3416 0.0416 1.1379 0.0653 +#> 91: 91.7111 -6.5289 -2.2203 -4.1763 -0.9288 0.0823 5.4933 6.9237 1.1601 4.0435 0.1839 0.0360 1.3387 0.0401 1.1768 0.0591 +#> 92: 92.1217 -6.5567 -2.2232 -4.2082 -0.9411 0.0815 8.0692 6.7286 1.1684 3.9422 0.1763 0.0411 1.3740 0.0463 1.1538 0.0613 +#> 93: 92.7497 -6.3512 -2.2463 -4.1806 -0.9633 0.0724 7.6657 6.3922 1.1870 3.8858 0.1796 0.0391 1.4232 0.0454 1.3749 0.0497 +#> 94: 92.2679 -6.3542 -2.2473 -4.1873 -0.9382 0.0711 7.2824 6.0726 1.1940 3.8847 0.1956 0.0371 1.3812 0.0465 1.2897 0.0521 +#> 95: 92.0257 -6.2448 -2.2624 -4.1681 -0.9624 0.0810 6.9183 5.7690 1.1345 3.8091 0.1858 0.0359 1.3026 0.0509 1.3000 0.0530 +#> 96: 91.5166 -5.9442 -2.2924 -4.2449 -0.9238 0.1058 7.1159 5.4805 1.1231 4.2529 0.1953 0.0343 1.4063 0.0445 1.3479 0.0482 +#> 97: 91.1606 -5.8541 -2.2912 -4.2398 -0.8875 0.1101 9.4515 5.2065 1.1256 4.3194 0.2081 0.0337 1.3436 0.0498 1.3317 0.0496 +#> 98: 91.2787 -6.0967 -2.2703 -4.2641 -0.9260 0.0869 8.9789 4.9462 1.2070 4.2238 0.1977 0.0373 1.3124 0.0495 1.1362 0.0653 +#> 99: 91.6449 -5.9441 -2.2562 -4.2355 -0.9312 0.1237 8.5300 4.6988 1.2343 4.0468 0.1878 0.0369 1.3508 0.0462 1.0542 0.0704 +#> 100: 91.7795 -5.8857 -2.2516 -4.3381 -0.9344 0.1291 8.1035 4.4639 1.2355 4.6941 0.1968 0.0393 1.4327 0.0358 1.1170 0.0668 +#> 101: 92.2537 -5.7930 -2.2345 -4.3477 -0.9272 0.1340 8.3402 4.2407 1.1961 4.7638 0.1933 0.0402 1.4683 0.0375 1.1216 0.0626 +#> 102: 92.3920 -6.0193 -2.2332 -4.3487 -0.9155 0.1565 11.1006 4.2977 1.1700 4.8048 0.2260 0.0444 1.4443 0.0342 1.0888 0.0674 +#> 103: 92.0043 -5.7825 -2.2376 -4.2616 -0.9043 0.1686 10.5455 4.0829 1.1587 4.5646 0.2147 0.0422 1.4198 0.0338 1.1639 0.0625 +#> 104: 92.1575 -5.8497 -2.2470 -4.2456 -0.9128 0.1762 10.0183 3.8787 1.1405 4.3364 0.2040 0.0440 1.3919 0.0379 1.2040 0.0582 +#> 105: 92.2784 -5.7971 -2.2582 -4.2100 -0.9128 0.1731 9.5173 3.6848 1.1351 4.1196 0.1938 0.0418 1.3982 0.0404 1.1069 0.0656 +#> 106: 92.4336 -5.7752 -2.2690 -4.3771 -0.8925 0.1644 9.0415 3.5005 1.1547 5.0970 0.1841 0.0476 1.3670 0.0423 1.1716 0.0625 +#> 107: 92.5128 -5.8328 -2.2549 -4.4193 -0.9403 0.2268 8.5894 3.3255 1.1160 5.2711 0.1749 0.0453 1.4023 0.0347 1.0279 0.0757 +#> 108: 92.8926 -5.7266 -2.2606 -4.5037 -0.9392 0.2394 8.1599 3.1592 1.1293 5.9652 0.1661 0.0447 1.3837 0.0346 0.9545 0.0747 +#> 109: 92.4657 -5.8687 -2.2884 -4.4108 -0.9043 0.2611 7.7519 4.0001 1.0729 5.6669 0.1578 0.0424 1.3441 0.0351 0.9758 0.0708 +#> 110: 92.6620 -5.6900 -2.2825 -4.4337 -0.9003 0.2602 7.3643 3.8001 1.0843 5.3836 0.1499 0.0433 1.4652 0.0302 0.9950 0.0722 +#> 111: 92.8949 -5.6946 -2.2661 -4.5240 -0.9233 0.2372 6.9961 3.6101 1.0845 5.8133 0.1551 0.0411 1.5005 0.0327 0.9284 0.0753 +#> 112: 93.4237 -5.6562 -2.2474 -4.4809 -0.9441 0.2322 6.6463 3.4296 1.1498 5.5227 0.1474 0.0409 1.4612 0.0317 0.9336 0.0762 +#> 113: 93.1883 -5.6891 -2.2846 -4.3984 -0.9416 0.2317 6.3140 3.2581 1.1062 5.2465 0.1596 0.0463 1.3924 0.0380 1.0268 0.0698 +#> 114: 93.4464 -5.7087 -2.2902 -4.4274 -0.9401 0.2638 5.9983 3.0952 1.1170 5.0203 0.1516 0.0495 1.4108 0.0361 1.0355 0.0682 +#> 115: 93.1873 -5.8732 -2.2668 -4.5086 -0.9636 0.2516 5.6984 3.3427 1.1141 5.7549 0.1440 0.0490 1.5010 0.0309 1.0443 0.0679 +#> 116: 92.6878 -5.8520 -2.2903 -4.5349 -0.9663 0.2612 5.4135 3.2444 1.1048 5.8809 0.1471 0.0511 1.3910 0.0360 1.0423 0.0702 +#> 117: 92.7775 -5.7892 -2.2897 -4.4572 -0.9544 0.2380 5.1428 3.0822 1.0731 5.5869 0.1397 0.0703 1.3493 0.0360 0.9831 0.0713 +#> 118: 93.1533 -5.8045 -2.2859 -4.4787 -0.9667 0.2150 4.8857 3.0277 1.0872 5.6786 0.1439 0.0812 1.3838 0.0373 1.0547 0.0696 +#> 119: 92.8370 -5.7208 -2.2738 -4.4627 -0.9462 0.2095 4.6414 2.8764 1.1172 5.6197 0.1643 0.0772 1.3394 0.0348 0.9180 0.0803 +#> 120: 92.5430 -5.7795 -2.3004 -4.4203 -0.9479 0.2313 4.4093 2.8377 1.1312 5.3387 0.1655 0.0803 1.2967 0.0360 1.0699 0.0761 +#> 121: 92.5318 -5.6550 -2.2866 -4.5065 -0.9166 0.2321 4.1888 2.6959 1.0994 6.0180 0.1686 0.0763 1.3882 0.0322 0.9895 0.0733 +#> 122: 92.7380 -5.6688 -2.2968 -4.4523 -0.9279 0.2529 3.9794 2.5611 1.0642 5.7171 0.1601 0.0851 1.3786 0.0316 0.9358 0.0742 +#> 123: 93.0753 -5.7451 -2.2896 -4.5423 -0.9371 0.2724 3.7804 2.9938 1.0758 5.9349 0.1521 0.0808 1.4275 0.0339 0.9652 0.0727 +#> 124: 93.2708 -5.8004 -2.2782 -4.4951 -0.9451 0.2590 3.5914 3.0594 1.0875 5.6382 0.1607 0.0768 1.3628 0.0340 1.0577 0.0693 +#> 125: 93.4025 -5.7710 -2.2990 -4.4498 -0.9661 0.2633 3.4118 2.9276 1.0809 5.3563 0.1527 0.0730 1.3816 0.0406 1.0295 0.0671 +#> 126: 93.4928 -5.7054 -2.3002 -4.4087 -0.9394 0.2965 3.4732 2.7812 1.1275 5.0884 0.1481 0.0693 1.2949 0.0423 0.9084 0.0726 +#> 127: 93.6449 -5.6593 -2.2683 -4.3418 -0.9194 0.2560 4.2986 2.6422 1.1070 4.8340 0.1449 0.0707 1.4258 0.0341 0.8802 0.0777 +#> 128: 93.7430 -5.6359 -2.2686 -4.4174 -0.9500 0.2279 5.2477 2.5101 1.1046 5.5376 0.1512 0.0859 1.4523 0.0327 0.8659 0.0826 +#> 129: 93.7432 -5.6851 -2.2849 -4.2019 -0.9660 0.1995 7.2497 2.8789 1.1315 5.2607 0.1762 0.0972 1.3901 0.0357 1.1264 0.0743 +#> 130: 93.2409 -5.8965 -2.2946 -4.1880 -0.9774 0.1719 7.4467 3.2276 1.1464 4.9977 0.1720 0.0924 1.3517 0.0446 1.0461 0.0705 +#> 131: 92.7780 -6.0551 -2.2647 -4.1894 -0.9579 0.1391 7.0744 3.7584 1.1291 4.7478 0.1714 0.0995 1.2542 0.0438 0.9139 0.0777 +#> 132: 92.7157 -6.1161 -2.2501 -4.1784 -0.9651 0.1146 6.7207 3.9259 1.1674 4.5104 0.1712 0.0957 1.2549 0.0473 0.8964 0.0803 +#> 133: 92.2696 -5.8545 -2.2717 -4.1907 -0.9782 0.0985 6.3846 3.7296 1.1652 4.2849 0.1626 0.1198 1.2208 0.0498 0.9730 0.0822 +#> 134: 92.2067 -5.8603 -2.2743 -4.2095 -0.9754 0.1398 6.0654 3.5431 1.1551 4.0706 0.1695 0.1138 1.3022 0.0432 0.9960 0.0795 +#> 135: 92.3979 -5.9500 -2.3053 -4.1938 -0.9425 0.1134 5.7621 3.3660 1.1771 3.8671 0.1610 0.1081 1.3373 0.0462 1.1323 0.0665 +#> 136: 92.3749 -5.8701 -2.2979 -4.2493 -0.9386 0.1504 5.4740 3.3090 1.1638 3.9609 0.1724 0.1027 1.3578 0.0389 1.1943 0.0650 +#> 137: 92.6942 -5.9020 -2.2755 -4.2318 -0.9464 0.1541 5.2003 3.5521 1.1704 3.8948 0.1685 0.0976 1.4170 0.0399 1.1472 0.0626 +#> 138: 92.7234 -5.8085 -2.2653 -4.2164 -0.9662 0.1808 4.9403 3.3745 1.1977 3.8348 0.1694 0.0927 1.4229 0.0387 1.0934 0.0708 +#> 139: 92.7341 -5.7737 -2.2685 -4.1759 -0.9334 0.1554 4.6933 3.2057 1.1971 3.6962 0.1917 0.0881 1.4324 0.0363 1.1669 0.0652 +#> 140: 92.1593 -5.6287 -2.2576 -4.1977 -0.9232 0.1345 4.6967 3.0455 1.1676 3.8133 0.2060 0.0837 1.5032 0.0349 1.1418 0.0678 +#> 141: 92.3199 -5.8323 -2.2451 -4.1948 -0.9447 0.1295 4.9624 3.3893 1.1408 3.8423 0.1957 0.0795 1.4470 0.0325 1.0892 0.0739 +#> 142: 92.7246 -6.1252 -2.2304 -4.1984 -0.9160 0.0816 4.7143 4.6501 1.1420 3.8554 0.1901 0.0755 1.4847 0.0386 1.2815 0.0576 +#> 143: 92.4130 -6.0231 -2.2261 -4.2205 -0.9495 0.1020 4.4786 4.4176 1.1454 4.0301 0.1929 0.0717 1.4103 0.0410 1.0418 0.0739 +#> 144: 92.4006 -5.9898 -2.2232 -4.2429 -0.9553 0.1131 4.2547 4.1967 1.1579 4.2583 0.1904 0.0681 1.4272 0.0339 1.0591 0.0737 +#> 145: 92.5011 -6.2340 -2.2232 -4.1872 -0.9560 0.1322 6.1775 4.8941 1.1594 4.0453 0.1811 0.0647 1.4059 0.0298 1.0219 0.0752 +#> 146: 92.7460 -6.2989 -2.2417 -4.2501 -0.9650 0.1527 5.8686 5.6454 1.1154 4.0076 0.1720 0.0758 1.4027 0.0348 1.1220 0.0689 +#> 147: 93.0630 -6.0839 -2.2217 -4.1822 -0.9661 0.1634 5.5752 5.3631 1.0596 3.8072 0.1743 0.0733 1.3682 0.0393 1.0992 0.0700 +#> 148: 92.7639 -5.8682 -2.2550 -4.1926 -0.9440 0.1599 5.8048 5.0950 1.0858 3.6230 0.1749 0.0696 1.3364 0.0436 1.0967 0.0721 +#> 149: 92.6183 -6.1270 -2.2379 -4.1103 -0.9643 0.1202 5.8027 4.8402 1.1089 3.4860 0.1661 0.0661 1.3061 0.0457 1.0014 0.0724 +#> 150: 92.7472 -6.1515 -2.2199 -4.1027 -0.9611 0.1014 5.6767 4.5982 1.1061 3.6113 0.1578 0.0654 1.3543 0.0405 1.0847 0.0707 +#> 151: 92.9566 -5.8911 -2.2174 -4.0722 -0.9516 0.0992 5.9638 4.3683 1.1057 3.5122 0.1767 0.0621 1.3619 0.0396 1.0158 0.0734 +#> 152: 93.0035 -5.8395 -2.2559 -4.0650 -0.9389 0.0928 4.4799 3.2331 1.0387 3.4826 0.1713 0.0604 1.3425 0.0428 1.1101 0.0635 +#> 153: 92.7242 -5.7832 -2.2538 -4.1288 -0.9159 0.1047 4.6102 3.0838 1.0527 3.8052 0.1718 0.0597 1.3905 0.0398 1.1371 0.0635 +#> 154: 92.2125 -5.9077 -2.2400 -4.0922 -0.9106 0.1033 4.4732 3.8350 1.0261 3.6148 0.1955 0.0643 1.3176 0.0419 1.1130 0.0635 +#> 155: 92.6226 -5.6271 -2.2239 -4.0122 -0.8948 0.0647 4.5553 2.5675 1.0412 3.0513 0.1845 0.0866 1.3266 0.0459 1.0244 0.0680 +#> 156: 92.6532 -5.5576 -2.2251 -4.0066 -0.9006 0.0922 3.8517 2.3273 1.0455 3.0971 0.1928 0.0863 1.4000 0.0394 0.9203 0.0754 +#> 157: 92.5192 -5.4834 -2.2356 -4.0069 -0.9321 0.0904 3.0410 1.8841 0.9867 3.1990 0.1905 0.0816 1.3927 0.0407 1.1517 0.0614 +#> 158: 92.5628 -5.5318 -2.2044 -4.0269 -0.9319 0.0742 3.5124 1.9585 1.0692 3.1835 0.1958 0.0934 1.4038 0.0324 0.9680 0.0758 +#> 159: 92.9690 -5.6416 -2.2134 -4.0156 -0.9556 0.0560 4.3830 2.2442 1.0543 3.2358 0.1873 0.0951 1.3624 0.0375 1.1207 0.0696 +#> 160: 92.9861 -5.5872 -2.2207 -3.9908 -0.9190 0.0417 4.1202 2.1685 1.0711 3.1521 0.1766 0.0913 1.3760 0.0371 1.0970 0.0713 +#> 161: 93.3139 -5.5349 -2.1972 -3.9860 -0.9365 0.0011 4.2865 1.8741 1.0759 3.0304 0.2007 0.0750 1.3650 0.0411 1.1220 0.0662 +#> 162: 93.3324 -5.6135 -2.1579 -4.0151 -0.9507 -0.0091 4.6402 2.0208 1.0535 3.0349 0.1935 0.0764 1.4069 0.0383 1.2550 0.0598 +#> 163: 93.0110 -5.5253 -2.1419 -4.0151 -0.9197 -0.0072 5.8946 1.9087 1.0965 3.0349 0.1833 0.0814 1.5095 0.0290 1.1314 0.0665 +#> 164: 93.0848 -5.4980 -2.1670 -4.0213 -0.9345 0.0150 4.9128 1.8293 1.0379 3.0653 0.1728 0.0835 1.4913 0.0343 1.0589 0.0687 +#> 165: 92.9407 -5.3978 -2.1707 -4.0090 -0.9480 0.0126 3.4620 1.3870 1.0594 3.0115 0.1702 0.0982 1.5550 0.0296 1.0978 0.0694 +#> 166: 93.1504 -5.4880 -2.1890 -3.9958 -0.9511 0.0316 2.7859 1.8457 1.0294 3.0739 0.1738 0.1031 1.5109 0.0308 1.1800 0.0651 +#> 167: 92.8442 -5.4673 -2.1984 -4.0259 -0.9262 0.0243 2.0497 1.6348 1.0469 3.1258 0.1650 0.0981 1.6185 0.0291 1.1733 0.0655 +#> 168: 92.9484 -5.6255 -2.2012 -4.0136 -0.9309 0.0199 1.8121 2.0784 1.0415 3.1795 0.1816 0.0929 1.5727 0.0268 1.4222 0.0543 +#> 169: 93.0266 -5.6135 -2.1677 -4.0179 -0.9279 0.0375 1.7553 2.1663 1.0298 3.1675 0.2013 0.0926 1.5356 0.0274 1.2960 0.0596 +#> 170: 92.9844 -5.6286 -2.1839 -4.0509 -0.9471 0.0414 1.9485 2.4078 1.0656 3.2787 0.2112 0.0950 1.5210 0.0265 1.3069 0.0616 +#> 171: 92.6832 -5.6238 -2.2059 -4.0710 -0.9175 0.0383 1.5941 2.2918 1.1095 3.3435 0.1921 0.0895 1.4678 0.0345 1.2189 0.0618 +#> 172: 92.5302 -5.5653 -2.2086 -4.0429 -0.9412 0.0773 1.5302 2.2565 1.1293 3.2157 0.1924 0.0680 1.4438 0.0367 1.2084 0.0661 +#> 173: 92.3877 -5.5357 -2.2141 -4.0246 -0.9268 0.0866 1.2153 2.0588 1.0844 3.2941 0.2060 0.0726 1.4686 0.0359 1.3683 0.0596 +#> 174: 92.4410 -5.4921 -2.1955 -4.0398 -0.9269 0.0645 1.6903 2.0042 1.1236 3.3646 0.1847 0.0804 1.5533 0.0310 1.2320 0.0675 +#> 175: 92.4192 -5.4726 -2.1945 -4.0271 -0.9222 0.0728 1.1344 1.9292 1.1085 3.3173 0.1875 0.0912 1.5350 0.0302 1.2461 0.0679 +#> 176: 92.3581 -5.5256 -2.2055 -3.9958 -0.9211 0.0720 1.1140 1.8097 1.0898 3.1459 0.2018 0.1104 1.4391 0.0323 1.2240 0.0677 +#> 177: 92.2144 -5.6699 -2.2357 -4.0017 -0.9402 0.0785 1.1932 2.6190 1.0355 3.1852 0.2266 0.1125 1.4705 0.0327 1.2866 0.0621 +#> 178: 92.3608 -5.7040 -2.2245 -4.0242 -0.9642 0.0596 0.7932 2.6061 0.9408 3.1080 0.1958 0.1180 1.5158 0.0365 1.3571 0.0600 +#> 179: 92.4358 -5.6877 -2.2243 -4.0166 -0.9486 0.0595 0.7591 2.3791 0.9241 3.0638 0.1900 0.1257 1.4317 0.0363 1.2359 0.0686 +#> 180: 92.5146 -5.7856 -2.2343 -4.0098 -0.9522 0.0522 0.4573 2.6882 0.9636 3.0406 0.1835 0.1270 1.4631 0.0361 1.2192 0.0701 +#> 181: 92.5469 -5.7684 -2.2220 -4.0549 -0.9488 0.0901 0.4189 2.4963 0.9873 3.1470 0.1744 0.1268 1.5165 0.0336 1.1359 0.0760 +#> 182: 92.5829 -5.7658 -2.2385 -4.0362 -0.9723 0.0572 0.3720 2.5387 0.9203 3.0397 0.1769 0.1636 1.4781 0.0375 1.2697 0.0677 +#> 183: 92.5737 -5.9187 -2.2130 -4.0638 -0.9876 0.0797 0.3084 3.3137 0.9467 3.0532 0.1737 0.1599 1.4288 0.0309 1.3024 0.0617 +#> 184: 92.4989 -5.9837 -2.1994 -4.0476 -0.9737 0.0594 0.2533 3.6658 0.9248 3.1230 0.1776 0.1552 1.3829 0.0316 1.2818 0.0621 +#> 185: 92.5677 -6.0227 -2.2084 -4.0403 -0.9584 0.0609 0.2215 3.8810 0.9134 3.0961 0.1739 0.1473 1.4202 0.0319 1.2731 0.0579 +#> 186: 92.7090 -5.9641 -2.2218 -4.0319 -0.9573 0.0575 0.2917 3.9574 0.9373 3.0666 0.1691 0.1703 1.4378 0.0296 1.2775 0.0601 +#> 187: 92.7358 -6.2503 -2.2003 -4.0534 -0.9742 0.0691 0.3037 5.2011 0.9333 3.0796 0.1647 0.1553 1.4254 0.0293 1.1987 0.0629 +#> 188: 92.6733 -6.1434 -2.1988 -4.0792 -0.9878 0.0860 0.3122 4.9451 0.9080 3.1891 0.1628 0.1558 1.4099 0.0317 1.3162 0.0593 +#> 189: 92.7256 -6.0886 -2.1766 -4.0419 -0.9672 0.0550 0.3758 4.3461 0.9140 3.0795 0.1697 0.1649 1.5310 0.0301 1.3258 0.0566 +#> 190: 92.5144 -6.1827 -2.2159 -4.0525 -0.9677 0.0728 0.3855 4.3370 0.9706 3.0518 0.1486 0.1841 1.4390 0.0295 1.1259 0.0740 +#> 191: 92.6209 -6.1257 -2.2287 -4.1095 -0.9670 0.1034 0.3340 4.3051 0.9486 3.1970 0.1549 0.1776 1.4397 0.0296 1.2004 0.0684 +#> 192: 92.6156 -6.1289 -2.2067 -4.1191 -0.9900 0.1090 0.3069 4.1314 0.9134 3.1476 0.1596 0.1912 1.4380 0.0301 1.1238 0.0720 +#> 193: 92.5434 -5.9782 -2.1800 -4.0845 -0.9547 0.1173 0.2694 3.6834 0.9005 2.9479 0.1582 0.1733 1.4538 0.0294 0.8798 0.0866 +#> 194: 92.5884 -5.7815 -2.2110 -4.0714 -0.9510 0.0928 0.2493 2.8236 0.9615 2.9852 0.1488 0.1730 1.4409 0.0297 1.1446 0.0677 +#> 195: 92.6180 -5.9277 -2.2213 -4.0714 -0.9379 0.1177 0.1993 3.5172 0.8976 2.9852 0.1449 0.1735 1.5012 0.0299 1.2131 0.0618 +#> 196: 92.5920 -5.7723 -2.2496 -4.0669 -0.9184 0.1262 0.2595 3.2454 0.9419 2.9697 0.1600 0.1881 1.4017 0.0338 0.9594 0.0790 +#> 197: 92.6292 -5.8658 -2.2434 -4.0640 -0.9365 0.1216 0.2491 3.3540 0.9267 2.9523 0.1598 0.1749 1.3953 0.0383 1.0788 0.0702 +#> 198: 92.6911 -5.8407 -2.2605 -4.0640 -0.9319 0.1264 0.1930 3.2321 0.8884 2.9523 0.1320 0.1940 1.4026 0.0358 1.0613 0.0704 +#> 199: 92.6480 -5.6988 -2.2599 -4.0668 -0.9395 0.1328 0.1412 2.6535 0.8915 2.9610 0.1573 0.2052 1.4353 0.0360 0.9900 0.0742 +#> 200: 92.7139 -5.6152 -2.2522 -4.0684 -0.9192 0.1589 0.1686 2.4362 0.9098 3.0185 0.1702 0.1705 1.4153 0.0338 1.1747 0.0705 +#> 201: 92.7134 -5.7029 -2.2504 -4.0502 -0.9270 0.1453 0.1499 2.6851 0.8909 2.9484 0.1749 0.1772 1.3851 0.0363 1.1255 0.0714 +#> 202: 92.7087 -5.7236 -2.2421 -4.0499 -0.9364 0.1238 0.1324 2.7215 0.8810 2.9507 0.1694 0.1913 1.3864 0.0365 1.1192 0.0705 +#> 203: 92.7013 -5.7563 -2.2293 -4.0494 -0.9394 0.1134 0.1269 2.8279 0.8866 2.9501 0.1618 0.1915 1.3981 0.0356 1.0942 0.0710 +#> 204: 92.6964 -5.8134 -2.2208 -4.0646 -0.9373 0.1144 0.1192 3.1058 0.8973 3.0279 0.1523 0.1983 1.4126 0.0345 1.0629 0.0723 +#> 205: 92.6936 -5.8441 -2.2195 -4.0787 -0.9373 0.1144 0.1068 3.2553 0.9029 3.0962 0.1473 0.2001 1.4217 0.0344 1.0532 0.0719 +#> 206: 92.6881 -5.8805 -2.2209 -4.0887 -0.9432 0.1187 0.1016 3.4269 0.9126 3.1477 0.1479 0.1957 1.4251 0.0348 1.0697 0.0712 +#> 207: 92.6929 -5.9304 -2.2259 -4.0987 -0.9473 0.1234 0.1028 3.6444 0.9261 3.1982 0.1469 0.1910 1.4170 0.0348 1.0586 0.0717 +#> 208: 92.6907 -5.9413 -2.2275 -4.1043 -0.9482 0.1267 0.1038 3.6864 0.9313 3.2244 0.1467 0.1889 1.4121 0.0343 1.0499 0.0718 +#> 209: 92.6917 -5.9265 -2.2304 -4.1109 -0.9498 0.1289 0.1022 3.5975 0.9363 3.2487 0.1478 0.1863 1.4053 0.0344 1.0521 0.0716 +#> 210: 92.6966 -5.9218 -2.2322 -4.1164 -0.9516 0.1337 0.0984 3.5650 0.9413 3.2688 0.1493 0.1874 1.3949 0.0342 1.0499 0.0719 +#> 211: 92.7020 -5.9160 -2.2351 -4.1209 -0.9542 0.1385 0.0958 3.5091 0.9390 3.2968 0.1503 0.1873 1.3925 0.0345 1.0547 0.0718 +#> 212: 92.7065 -5.9119 -2.2376 -4.1247 -0.9564 0.1432 0.0933 3.4520 0.9373 3.3205 0.1531 0.1901 1.3869 0.0346 1.0625 0.0717 +#> 213: 92.7107 -5.9047 -2.2402 -4.1286 -0.9575 0.1455 0.0930 3.3990 0.9361 3.3369 0.1536 0.1932 1.3814 0.0349 1.0698 0.0712 +#> 214: 92.7110 -5.9061 -2.2415 -4.1321 -0.9585 0.1483 0.0921 3.3864 0.9364 3.3517 0.1542 0.1963 1.3794 0.0348 1.0721 0.0712 +#> 215: 92.7116 -5.9128 -2.2417 -4.1360 -0.9581 0.1510 0.0941 3.4201 0.9347 3.3646 0.1545 0.1988 1.3764 0.0350 1.0731 0.0712 +#> 216: 92.7135 -5.9184 -2.2432 -4.1383 -0.9589 0.1540 0.0957 3.4623 0.9337 3.3698 0.1541 0.2016 1.3761 0.0353 1.0737 0.0714 +#> 217: 92.7143 -5.9262 -2.2453 -4.1428 -0.9604 0.1568 0.0981 3.5202 0.9323 3.3854 0.1542 0.2053 1.3770 0.0352 1.0779 0.0716 +#> 218: 92.7102 -5.9169 -2.2463 -4.1446 -0.9606 0.1604 0.1000 3.4823 0.9305 3.3851 0.1530 0.2083 1.3802 0.0353 1.0819 0.0716 +#> 219: 92.7062 -5.9089 -2.2470 -4.1481 -0.9597 0.1636 0.1000 3.4465 0.9295 3.3874 0.1529 0.2125 1.3779 0.0352 1.0836 0.0716 +#> 220: 92.7027 -5.9052 -2.2480 -4.1509 -0.9594 0.1668 0.1020 3.4302 0.9264 3.3877 0.1531 0.2168 1.3780 0.0352 1.0893 0.0713 +#> 221: 92.7029 -5.8990 -2.2497 -4.1541 -0.9586 0.1696 0.1017 3.4007 0.9227 3.3916 0.1535 0.2208 1.3781 0.0354 1.0925 0.0709 +#> 222: 92.7063 -5.8993 -2.2519 -4.1604 -0.9582 0.1732 0.1025 3.4099 0.9190 3.4135 0.1537 0.2268 1.3791 0.0355 1.1031 0.0702 +#> 223: 92.7090 -5.8932 -2.2537 -4.1669 -0.9573 0.1757 0.1022 3.3946 0.9157 3.4424 0.1543 0.2319 1.3802 0.0356 1.1040 0.0701 +#> 224: 92.7116 -5.8930 -2.2545 -4.1712 -0.9561 0.1774 0.1017 3.3964 0.9133 3.4673 0.1550 0.2355 1.3795 0.0356 1.1018 0.0701 +#> 225: 92.7136 -5.8911 -2.2564 -4.1715 -0.9551 0.1788 0.1016 3.4013 0.9125 3.4628 0.1548 0.2380 1.3756 0.0359 1.1003 0.0700 +#> 226: 92.7153 -5.8883 -2.2569 -4.1711 -0.9536 0.1793 0.1016 3.4046 0.9134 3.4575 0.1549 0.2398 1.3737 0.0360 1.1016 0.0699 +#> 227: 92.7163 -5.8830 -2.2575 -4.1720 -0.9526 0.1796 0.1019 3.3952 0.9129 3.4575 0.1545 0.2407 1.3718 0.0363 1.1015 0.0698 +#> 228: 92.7182 -5.8865 -2.2578 -4.1728 -0.9528 0.1804 0.1017 3.4198 0.9113 3.4576 0.1538 0.2433 1.3722 0.0363 1.1068 0.0695 +#> 229: 92.7199 -5.8965 -2.2578 -4.1718 -0.9523 0.1812 0.1023 3.5030 0.9097 3.4503 0.1529 0.2463 1.3749 0.0363 1.1093 0.0694 +#> 230: 92.7205 -5.8997 -2.2578 -4.1712 -0.9514 0.1825 0.1025 3.5337 0.9071 3.4446 0.1519 0.2497 1.3802 0.0362 1.1115 0.0693 +#> 231: 92.7208 -5.9001 -2.2581 -4.1711 -0.9511 0.1838 0.1044 3.5537 0.9037 3.4423 0.1510 0.2533 1.3834 0.0361 1.1125 0.0693 +#> 232: 92.7183 -5.9041 -2.2588 -4.1715 -0.9504 0.1855 0.1061 3.5958 0.9001 3.4391 0.1503 0.2572 1.3871 0.0362 1.1161 0.0690 +#> 233: 92.7169 -5.9106 -2.2593 -4.1725 -0.9490 0.1866 0.1073 3.6433 0.8968 3.4367 0.1496 0.2609 1.3900 0.0362 1.1179 0.0688 +#> 234: 92.7125 -5.9165 -2.2594 -4.1728 -0.9479 0.1873 0.1098 3.6870 0.8932 3.4321 0.1498 0.2641 1.3907 0.0363 1.1177 0.0687 +#> 235: 92.7072 -5.9203 -2.2592 -4.1729 -0.9472 0.1876 0.1128 3.7229 0.8899 3.4269 0.1506 0.2676 1.3913 0.0364 1.1212 0.0686 +#> 236: 92.7048 -5.9319 -2.2603 -4.1724 -0.9467 0.1879 0.1147 3.7863 0.8879 3.4175 0.1510 0.2705 1.3898 0.0365 1.1181 0.0688 +#> 237: 92.7037 -5.9349 -2.2609 -4.1720 -0.9461 0.1881 0.1152 3.8047 0.8862 3.4096 0.1512 0.2731 1.3891 0.0367 1.1164 0.0688 +#> 238: 92.7027 -5.9359 -2.2605 -4.1715 -0.9459 0.1884 0.1151 3.7997 0.8842 3.4023 0.1516 0.2755 1.3905 0.0366 1.1171 0.0688 +#> 239: 92.7027 -5.9375 -2.2599 -4.1712 -0.9463 0.1881 0.1143 3.8187 0.8835 3.3954 0.1521 0.2780 1.3923 0.0366 1.1193 0.0688 +#> 240: 92.7025 -5.9409 -2.2593 -4.1710 -0.9467 0.1884 0.1135 3.8437 0.8830 3.3888 0.1530 0.2797 1.3939 0.0366 1.1266 0.0685 +#> 241: 92.7006 -5.9429 -2.2589 -4.1703 -0.9469 0.1887 0.1130 3.8580 0.8825 3.3820 0.1529 0.2815 1.3967 0.0364 1.1299 0.0685 +#> 242: 92.6977 -5.9366 -2.2594 -4.1693 -0.9471 0.1887 0.1130 3.8245 0.8810 3.3742 0.1534 0.2833 1.3967 0.0364 1.1323 0.0685 +#> 243: 92.6951 -5.9310 -2.2605 -4.1683 -0.9473 0.1891 0.1131 3.7904 0.8807 3.3666 0.1541 0.2853 1.3953 0.0364 1.1380 0.0683 +#> 244: 92.6928 -5.9289 -2.2610 -4.1680 -0.9471 0.1899 0.1130 3.7709 0.8797 3.3604 0.1545 0.2880 1.3947 0.0364 1.1399 0.0683 +#> 245: 92.6902 -5.9291 -2.2615 -4.1677 -0.9472 0.1914 0.1129 3.7637 0.8787 3.3538 0.1549 0.2898 1.3942 0.0364 1.1440 0.0681 +#> 246: 92.6880 -5.9271 -2.2617 -4.1677 -0.9472 0.1926 0.1131 3.7457 0.8785 3.3500 0.1549 0.2916 1.3938 0.0364 1.1468 0.0681 +#> 247: 92.6865 -5.9264 -2.2613 -4.1676 -0.9471 0.1930 0.1127 3.7331 0.8793 3.3487 0.1551 0.2918 1.3931 0.0364 1.1464 0.0683 +#> 248: 92.6855 -5.9212 -2.2604 -4.1671 -0.9476 0.1935 0.1116 3.7055 0.8795 3.3451 0.1549 0.2923 1.3942 0.0363 1.1453 0.0684 +#> 249: 92.6848 -5.9190 -2.2600 -4.1667 -0.9482 0.1939 0.1110 3.6857 0.8801 3.3428 0.1548 0.2923 1.3942 0.0363 1.1440 0.0685 +#> 250: 92.6858 -5.9194 -2.2605 -4.1663 -0.9489 0.1945 0.1109 3.6821 0.8806 3.3397 0.1547 0.2920 1.3932 0.0363 1.1430 0.0686 +#> 251: 92.6849 -5.9179 -2.2610 -4.1665 -0.9492 0.1950 0.1111 3.6795 0.8814 3.3392 0.1550 0.2919 1.3922 0.0364 1.1434 0.0685 +#> 252: 92.6848 -5.9141 -2.2615 -4.1660 -0.9493 0.1957 0.1110 3.6611 0.8818 3.3363 0.1548 0.2918 1.3919 0.0364 1.1423 0.0686 +#> 253: 92.6837 -5.9110 -2.2637 -4.1634 -0.9493 0.1952 0.1114 3.6462 0.8788 3.3481 0.1550 0.2920 1.3941 0.0363 1.1417 0.0688 +#> 254: 92.6827 -5.9082 -2.2650 -4.1608 -0.9492 0.1944 0.1117 3.6309 0.8753 3.3595 0.1548 0.2921 1.3964 0.0361 1.1415 0.0688 +#> 255: 92.6829 -5.9076 -2.2662 -4.1585 -0.9495 0.1934 0.1118 3.6221 0.8723 3.3737 0.1547 0.2923 1.3977 0.0359 1.1397 0.0689 +#> 256: 92.6821 -5.9079 -2.2672 -4.1559 -0.9495 0.1923 0.1118 3.6279 0.8697 3.3865 0.1547 0.2925 1.3990 0.0357 1.1387 0.0691 +#> 257: 92.6822 -5.9054 -2.2686 -4.1534 -0.9499 0.1914 0.1119 3.6202 0.8673 3.3988 0.1548 0.2923 1.4010 0.0356 1.1438 0.0690 +#> 258: 92.6828 -5.9054 -2.2700 -4.1509 -0.9498 0.1900 0.1121 3.6166 0.8651 3.4085 0.1547 0.2926 1.4028 0.0356 1.1473 0.0688 +#> 259: 92.6842 -5.9087 -2.2710 -4.1474 -0.9496 0.1890 0.1128 3.6314 0.8629 3.4154 0.1548 0.2923 1.4040 0.0355 1.1482 0.0689 +#> 260: 92.6852 -5.9118 -2.2717 -4.1444 -0.9493 0.1885 0.1124 3.6485 0.8606 3.4227 0.1544 0.2919 1.4073 0.0354 1.1518 0.0688 +#> 261: 92.6858 -5.9137 -2.2721 -4.1419 -0.9493 0.1882 0.1122 3.6641 0.8581 3.4314 0.1543 0.2913 1.4106 0.0353 1.1577 0.0684 +#> 262: 92.6861 -5.9117 -2.2726 -4.1394 -0.9493 0.1881 0.1116 3.6572 0.8558 3.4391 0.1541 0.2908 1.4137 0.0352 1.1613 0.0682 +#> 263: 92.6855 -5.9124 -2.2730 -4.1372 -0.9494 0.1875 0.1113 3.6626 0.8533 3.4465 0.1541 0.2905 1.4152 0.0351 1.1636 0.0681 +#> 264: 92.6841 -5.9137 -2.2734 -4.1351 -0.9496 0.1871 0.1109 3.6703 0.8505 3.4529 0.1538 0.2903 1.4156 0.0350 1.1632 0.0681 +#> 265: 92.6833 -5.9153 -2.2741 -4.1327 -0.9498 0.1867 0.1108 3.6816 0.8472 3.4581 0.1535 0.2899 1.4168 0.0350 1.1647 0.0679 +#> 266: 92.6835 -5.9147 -2.2752 -4.1307 -0.9497 0.1865 0.1107 3.6768 0.8450 3.4641 0.1531 0.2896 1.4176 0.0349 1.1640 0.0679 +#> 267: 92.6835 -5.9167 -2.2761 -4.1283 -0.9499 0.1862 0.1105 3.6851 0.8430 3.4700 0.1530 0.2892 1.4178 0.0348 1.1639 0.0679 +#> 268: 92.6841 -5.9141 -2.2767 -4.1269 -0.9503 0.1860 0.1107 3.6718 0.8407 3.4775 0.1533 0.2891 1.4187 0.0348 1.1673 0.0677 +#> 269: 92.6845 -5.9094 -2.2774 -4.1253 -0.9503 0.1855 0.1112 3.6520 0.8384 3.4840 0.1535 0.2890 1.4192 0.0348 1.1686 0.0675 +#> 270: 92.6847 -5.9042 -2.2779 -4.1239 -0.9505 0.1853 0.1107 3.6288 0.8365 3.4895 0.1536 0.2889 1.4192 0.0347 1.1698 0.0675 +#> 271: 92.6849 -5.9000 -2.2785 -4.1228 -0.9506 0.1853 0.1102 3.6083 0.8348 3.4956 0.1536 0.2889 1.4191 0.0346 1.1692 0.0676 +#> 272: 92.6850 -5.8965 -2.2794 -4.1223 -0.9507 0.1853 0.1092 3.5892 0.8331 3.5071 0.1538 0.2889 1.4194 0.0345 1.1700 0.0676 +#> 273: 92.6851 -5.8916 -2.2805 -4.1222 -0.9508 0.1850 0.1089 3.5697 0.8315 3.5211 0.1538 0.2889 1.4209 0.0345 1.1720 0.0675 +#> 274: 92.6849 -5.8898 -2.2815 -4.1218 -0.9506 0.1852 0.1084 3.5607 0.8301 3.5339 0.1542 0.2886 1.4221 0.0344 1.1728 0.0675 +#> 275: 92.6844 -5.8885 -2.2830 -4.1215 -0.9504 0.1855 0.1080 3.5514 0.8284 3.5491 0.1545 0.2883 1.4238 0.0343 1.1756 0.0673 +#> 276: 92.6834 -5.8885 -2.2843 -4.1210 -0.9501 0.1859 0.1077 3.5477 0.8272 3.5648 0.1547 0.2878 1.4243 0.0343 1.1749 0.0674 +#> 277: 92.6829 -5.8892 -2.2858 -4.1208 -0.9500 0.1862 0.1071 3.5505 0.8257 3.5807 0.1552 0.2872 1.4244 0.0343 1.1747 0.0674 +#> 278: 92.6825 -5.8885 -2.2871 -4.1205 -0.9499 0.1862 0.1072 3.5463 0.8245 3.5960 0.1555 0.2866 1.4247 0.0343 1.1742 0.0675 +#> 279: 92.6815 -5.8887 -2.2883 -4.1201 -0.9501 0.1864 0.1072 3.5433 0.8239 3.6088 0.1556 0.2860 1.4247 0.0343 1.1737 0.0676 +#> 280: 92.6800 -5.8901 -2.2896 -4.1211 -0.9503 0.1865 0.1078 3.5481 0.8238 3.6285 0.1556 0.2848 1.4252 0.0344 1.1742 0.0676 +#> 281: 92.6779 -5.8914 -2.2907 -4.1218 -0.9502 0.1865 0.1084 3.5491 0.8240 3.6471 0.1558 0.2838 1.4251 0.0343 1.1732 0.0677 +#> 282: 92.6767 -5.8906 -2.2919 -4.1236 -0.9501 0.1862 0.1091 3.5462 0.8248 3.6747 0.1558 0.2825 1.4250 0.0344 1.1732 0.0677 +#> 283: 92.6750 -5.8895 -2.2928 -4.1253 -0.9499 0.1857 0.1097 3.5418 0.8260 3.7025 0.1555 0.2814 1.4253 0.0344 1.1712 0.0678 +#> 284: 92.6736 -5.8903 -2.2934 -4.1271 -0.9497 0.1854 0.1107 3.5438 0.8269 3.7297 0.1553 0.2800 1.4257 0.0343 1.1698 0.0678 +#> 285: 92.6730 -5.8917 -2.2942 -4.1284 -0.9497 0.1852 0.1116 3.5481 0.8274 3.7528 0.1551 0.2787 1.4260 0.0343 1.1689 0.0678 +#> 286: 92.6715 -5.8913 -2.2947 -4.1285 -0.9492 0.1849 0.1122 3.5473 0.8274 3.7660 0.1550 0.2775 1.4265 0.0342 1.1678 0.0679 +#> 287: 92.6702 -5.8925 -2.2952 -4.1290 -0.9489 0.1846 0.1125 3.5531 0.8268 3.7818 0.1549 0.2764 1.4269 0.0342 1.1673 0.0678 +#> 288: 92.6688 -5.8918 -2.2959 -4.1290 -0.9490 0.1843 0.1126 3.5495 0.8262 3.7946 0.1546 0.2756 1.4275 0.0341 1.1673 0.0678 +#> 289: 92.6673 -5.8907 -2.2966 -4.1295 -0.9490 0.1841 0.1124 3.5445 0.8260 3.8067 0.1543 0.2750 1.4280 0.0342 1.1690 0.0677 +#> 290: 92.6657 -5.8909 -2.2973 -4.1302 -0.9490 0.1838 0.1123 3.5433 0.8260 3.8201 0.1540 0.2744 1.4279 0.0342 1.1687 0.0676 +#> 291: 92.6642 -5.8902 -2.2978 -4.1312 -0.9493 0.1835 0.1124 3.5399 0.8262 3.8365 0.1538 0.2738 1.4279 0.0342 1.1695 0.0676 +#> 292: 92.6635 -5.8917 -2.2983 -4.1316 -0.9495 0.1831 0.1121 3.5453 0.8263 3.8517 0.1535 0.2733 1.4275 0.0342 1.1695 0.0675 +#> 293: 92.6622 -5.8936 -2.2991 -4.1323 -0.9497 0.1830 0.1121 3.5526 0.8265 3.8692 0.1533 0.2728 1.4274 0.0342 1.1701 0.0675 +#> 294: 92.6604 -5.8936 -2.2999 -4.1328 -0.9499 0.1826 0.1126 3.5505 0.8263 3.8838 0.1533 0.2723 1.4273 0.0342 1.1712 0.0675 +#> 295: 92.6593 -5.8924 -2.3007 -4.1329 -0.9498 0.1823 0.1131 3.5443 0.8262 3.9004 0.1531 0.2717 1.4276 0.0342 1.1718 0.0674 +#> 296: 92.6586 -5.8906 -2.3016 -4.1323 -0.9496 0.1822 0.1133 3.5374 0.8266 3.9103 0.1530 0.2707 1.4272 0.0343 1.1714 0.0674 +#> 297: 92.6578 -5.8889 -2.3026 -4.1329 -0.9494 0.1819 0.1139 3.5315 0.8271 3.9280 0.1528 0.2697 1.4267 0.0343 1.1697 0.0675 +#> 298: 92.6575 -5.8885 -2.3036 -4.1330 -0.9490 0.1814 0.1143 3.5303 0.8275 3.9410 0.1527 0.2689 1.4263 0.0344 1.1688 0.0675 +#> 299: 92.6566 -5.8879 -2.3047 -4.1329 -0.9488 0.1807 0.1147 3.5286 0.8282 3.9507 0.1526 0.2679 1.4263 0.0345 1.1680 0.0674 +#> 300: 92.6555 -5.8862 -2.3057 -4.1325 -0.9483 0.1802 0.1151 3.5225 0.8293 3.9582 0.1527 0.2671 1.4261 0.0345 1.1677 0.0674 +#> 301: 92.6545 -5.8854 -2.3067 -4.1326 -0.9480 0.1795 0.1156 3.5191 0.8300 3.9691 0.1530 0.2665 1.4257 0.0346 1.1672 0.0674 +#> 302: 92.6539 -5.8839 -2.3078 -4.1322 -0.9477 0.1788 0.1161 3.5154 0.8309 3.9769 0.1532 0.2657 1.4252 0.0346 1.1664 0.0675 +#> 303: 92.6541 -5.8799 -2.3089 -4.1327 -0.9474 0.1782 0.1161 3.5012 0.8319 3.9913 0.1534 0.2649 1.4242 0.0347 1.1653 0.0675 +#> 304: 92.6554 -5.8766 -2.3096 -4.1326 -0.9472 0.1774 0.1164 3.4879 0.8328 3.9978 0.1536 0.2641 1.4234 0.0348 1.1644 0.0675 +#> 305: 92.6559 -5.8732 -2.3104 -4.1325 -0.9470 0.1764 0.1161 3.4740 0.8334 4.0037 0.1535 0.2633 1.4231 0.0348 1.1634 0.0676 +#> 306: 92.6564 -5.8717 -2.3113 -4.1322 -0.9470 0.1758 0.1161 3.4705 0.8341 4.0097 0.1537 0.2622 1.4236 0.0348 1.1628 0.0676 +#> 307: 92.6573 -5.8703 -2.3121 -4.1320 -0.9469 0.1748 0.1158 3.4630 0.8349 4.0154 0.1538 0.2614 1.4231 0.0348 1.1617 0.0677 +#> 308: 92.6578 -5.8695 -2.3129 -4.1318 -0.9465 0.1738 0.1154 3.4585 0.8356 4.0210 0.1540 0.2607 1.4229 0.0348 1.1604 0.0677 +#> 309: 92.6577 -5.8691 -2.3132 -4.1317 -0.9465 0.1732 0.1151 3.4548 0.8369 4.0270 0.1540 0.2596 1.4233 0.0348 1.1589 0.0678 +#> 310: 92.6580 -5.8680 -2.3135 -4.1309 -0.9466 0.1727 0.1147 3.4472 0.8377 4.0280 0.1540 0.2587 1.4231 0.0348 1.1569 0.0679 +#> 311: 92.6575 -5.8681 -2.3141 -4.1303 -0.9466 0.1722 0.1144 3.4477 0.8384 4.0303 0.1539 0.2577 1.4236 0.0348 1.1557 0.0679 +#> 312: 92.6571 -5.8685 -2.3145 -4.1299 -0.9467 0.1720 0.1143 3.4498 0.8393 4.0328 0.1538 0.2566 1.4237 0.0348 1.1545 0.0680 +#> 313: 92.6559 -5.8685 -2.3150 -4.1296 -0.9469 0.1718 0.1142 3.4483 0.8403 4.0358 0.1537 0.2555 1.4234 0.0348 1.1532 0.0681 +#> 314: 92.6543 -5.8699 -2.3155 -4.1294 -0.9471 0.1715 0.1142 3.4526 0.8404 4.0401 0.1537 0.2546 1.4236 0.0347 1.1522 0.0681 +#> 315: 92.6528 -5.8713 -2.3161 -4.1289 -0.9472 0.1712 0.1144 3.4584 0.8402 4.0427 0.1537 0.2538 1.4234 0.0347 1.1520 0.0682 +#> 316: 92.6510 -5.8726 -2.3166 -4.1283 -0.9472 0.1705 0.1146 3.4647 0.8404 4.0443 0.1537 0.2528 1.4236 0.0347 1.1511 0.0682 +#> 317: 92.6496 -5.8736 -2.3170 -4.1281 -0.9474 0.1699 0.1147 3.4701 0.8406 4.0497 0.1536 0.2520 1.4238 0.0347 1.1504 0.0683 +#> 318: 92.6479 -5.8745 -2.3174 -4.1276 -0.9475 0.1695 0.1153 3.4729 0.8410 4.0511 0.1535 0.2510 1.4238 0.0347 1.1503 0.0683 +#> 319: 92.6463 -5.8773 -2.3175 -4.1272 -0.9476 0.1690 0.1155 3.4868 0.8409 4.0527 0.1535 0.2502 1.4234 0.0347 1.1484 0.0685 +#> 320: 92.6447 -5.8770 -2.3179 -4.1263 -0.9478 0.1684 0.1158 3.4849 0.8407 4.0516 0.1534 0.2493 1.4238 0.0347 1.1483 0.0685 +#> 321: 92.6433 -5.8768 -2.3181 -4.1255 -0.9479 0.1679 0.1161 3.4850 0.8405 4.0511 0.1533 0.2485 1.4238 0.0346 1.1474 0.0686 +#> 322: 92.6425 -5.8766 -2.3182 -4.1246 -0.9480 0.1673 0.1161 3.4839 0.8403 4.0505 0.1530 0.2474 1.4243 0.0346 1.1458 0.0687 +#> 323: 92.6414 -5.8778 -2.3183 -4.1241 -0.9481 0.1669 0.1162 3.4888 0.8402 4.0517 0.1530 0.2466 1.4244 0.0346 1.1454 0.0687 +#> 324: 92.6404 -5.8771 -2.3186 -4.1236 -0.9482 0.1666 0.1161 3.4855 0.8401 4.0525 0.1529 0.2459 1.4247 0.0345 1.1446 0.0687 +#> 325: 92.6396 -5.8753 -2.3188 -4.1231 -0.9483 0.1664 0.1156 3.4767 0.8396 4.0533 0.1529 0.2454 1.4253 0.0345 1.1438 0.0689 +#> 326: 92.6397 -5.8766 -2.3192 -4.1226 -0.9484 0.1663 0.1152 3.4798 0.8389 4.0542 0.1527 0.2449 1.4253 0.0345 1.1431 0.0690 +#> 327: 92.6395 -5.8785 -2.3197 -4.1224 -0.9483 0.1660 0.1151 3.4880 0.8382 4.0557 0.1528 0.2445 1.4250 0.0345 1.1430 0.0690 +#> 328: 92.6397 -5.8805 -2.3202 -4.1221 -0.9483 0.1657 0.1153 3.5011 0.8373 4.0568 0.1528 0.2442 1.4246 0.0345 1.1427 0.0690 +#> 329: 92.6390 -5.8838 -2.3208 -4.1219 -0.9482 0.1655 0.1161 3.5176 0.8365 4.0580 0.1530 0.2439 1.4241 0.0345 1.1429 0.0690 +#> 330: 92.6380 -5.8862 -2.3215 -4.1216 -0.9484 0.1653 0.1166 3.5286 0.8355 4.0584 0.1529 0.2437 1.4234 0.0346 1.1428 0.0690 +#> 331: 92.6367 -5.8867 -2.3223 -4.1206 -0.9484 0.1651 0.1165 3.5288 0.8348 4.0577 0.1528 0.2435 1.4233 0.0346 1.1429 0.0690 +#> 332: 92.6360 -5.8859 -2.3230 -4.1199 -0.9485 0.1650 0.1165 3.5235 0.8343 4.0572 0.1527 0.2433 1.4227 0.0346 1.1429 0.0689 +#> 333: 92.6361 -5.8839 -2.3237 -4.1194 -0.9485 0.1649 0.1162 3.5142 0.8340 4.0564 0.1527 0.2430 1.4224 0.0347 1.1429 0.0689 +#> 334: 92.6359 -5.8824 -2.3244 -4.1190 -0.9486 0.1649 0.1158 3.5070 0.8337 4.0567 0.1527 0.2424 1.4218 0.0347 1.1442 0.0689 +#> 335: 92.6366 -5.8826 -2.3250 -4.1186 -0.9485 0.1645 0.1157 3.5069 0.8334 4.0574 0.1527 0.2419 1.4214 0.0347 1.1448 0.0688 +#> 336: 92.6374 -5.8816 -2.3253 -4.1182 -0.9486 0.1644 0.1158 3.5034 0.8330 4.0580 0.1528 0.2415 1.4212 0.0347 1.1471 0.0687 +#> 337: 92.6378 -5.8810 -2.3258 -4.1176 -0.9487 0.1642 0.1159 3.5023 0.8325 4.0582 0.1528 0.2410 1.4212 0.0347 1.1467 0.0688 +#> 338: 92.6383 -5.8814 -2.3262 -4.1168 -0.9488 0.1637 0.1160 3.5028 0.8322 4.0571 0.1526 0.2409 1.4216 0.0346 1.1456 0.0689 +#> 339: 92.6392 -5.8808 -2.3266 -4.1160 -0.9490 0.1631 0.1161 3.4989 0.8318 4.0566 0.1524 0.2408 1.4220 0.0346 1.1441 0.0690 +#> 340: 92.6393 -5.8810 -2.3269 -4.1152 -0.9491 0.1626 0.1157 3.4997 0.8316 4.0564 0.1524 0.2407 1.4216 0.0346 1.1419 0.0692 +#> 341: 92.6394 -5.8807 -2.3272 -4.1148 -0.9492 0.1619 0.1153 3.4966 0.8308 4.0552 0.1523 0.2405 1.4218 0.0346 1.1415 0.0692 +#> 342: 92.6394 -5.8806 -2.3274 -4.1141 -0.9493 0.1612 0.1146 3.4936 0.8303 4.0537 0.1522 0.2405 1.4221 0.0346 1.1406 0.0692 +#> 343: 92.6398 -5.8819 -2.3277 -4.1134 -0.9494 0.1606 0.1141 3.4961 0.8297 4.0519 0.1522 0.2402 1.4219 0.0347 1.1404 0.0692 +#> 344: 92.6401 -5.8823 -2.3280 -4.1128 -0.9497 0.1599 0.1137 3.4963 0.8293 4.0504 0.1523 0.2400 1.4214 0.0346 1.1404 0.0692 +#> 345: 92.6404 -5.8829 -2.3283 -4.1124 -0.9498 0.1593 0.1136 3.4958 0.8289 4.0494 0.1523 0.2396 1.4214 0.0346 1.1398 0.0692 +#> 346: 92.6405 -5.8829 -2.3283 -4.1119 -0.9499 0.1587 0.1135 3.4953 0.8287 4.0484 0.1522 0.2394 1.4216 0.0346 1.1397 0.0692 +#> 347: 92.6404 -5.8833 -2.3288 -4.1117 -0.9500 0.1582 0.1133 3.4965 0.8289 4.0480 0.1521 0.2391 1.4211 0.0346 1.1388 0.0692 +#> 348: 92.6407 -5.8838 -2.3293 -4.1113 -0.9502 0.1578 0.1132 3.4978 0.8290 4.0471 0.1520 0.2388 1.4209 0.0346 1.1385 0.0692 +#> 349: 92.6409 -5.8847 -2.3299 -4.1110 -0.9503 0.1571 0.1128 3.5024 0.8290 4.0474 0.1519 0.2386 1.4207 0.0347 1.1379 0.0692 +#> 350: 92.6413 -5.8853 -2.3304 -4.1107 -0.9504 0.1567 0.1125 3.5037 0.8287 4.0478 0.1519 0.2383 1.4207 0.0347 1.1366 0.0693 +#> 351: 92.6415 -5.8868 -2.3310 -4.1104 -0.9504 0.1562 0.1122 3.5109 0.8287 4.0490 0.1518 0.2378 1.4208 0.0347 1.1364 0.0693 +#> 352: 92.6413 -5.8882 -2.3316 -4.1103 -0.9504 0.1557 0.1120 3.5196 0.8287 4.0517 0.1517 0.2375 1.4207 0.0346 1.1361 0.0693 +#> 353: 92.6414 -5.8890 -2.3322 -4.1101 -0.9503 0.1553 0.1117 3.5237 0.8290 4.0533 0.1517 0.2371 1.4202 0.0346 1.1345 0.0693 +#> 354: 92.6417 -5.8879 -2.3327 -4.1099 -0.9502 0.1548 0.1115 3.5206 0.8294 4.0546 0.1515 0.2368 1.4200 0.0346 1.1336 0.0694 +#> 355: 92.6417 -5.8882 -2.3333 -4.1096 -0.9500 0.1541 0.1115 3.5265 0.8296 4.0548 0.1514 0.2364 1.4203 0.0346 1.1325 0.0694 +#> 356: 92.6414 -5.8881 -2.3338 -4.1093 -0.9497 0.1535 0.1115 3.5339 0.8299 4.0553 0.1513 0.2362 1.4204 0.0346 1.1318 0.0694 +#> 357: 92.6414 -5.8874 -2.3343 -4.1087 -0.9497 0.1529 0.1117 3.5320 0.8302 4.0548 0.1512 0.2358 1.4205 0.0346 1.1315 0.0694 +#> 358: 92.6415 -5.8865 -2.3349 -4.1087 -0.9497 0.1523 0.1118 3.5274 0.8308 4.0583 0.1510 0.2354 1.4206 0.0346 1.1308 0.0695 +#> 359: 92.6415 -5.8855 -2.3352 -4.1085 -0.9497 0.1518 0.1123 3.5208 0.8308 4.0597 0.1509 0.2349 1.4205 0.0346 1.1298 0.0695 +#> 360: 92.6413 -5.8851 -2.3356 -4.1080 -0.9496 0.1513 0.1125 3.5176 0.8308 4.0606 0.1508 0.2344 1.4207 0.0346 1.1289 0.0695 +#> 361: 92.6412 -5.8854 -2.3359 -4.1076 -0.9498 0.1508 0.1126 3.5187 0.8308 4.0618 0.1508 0.2338 1.4214 0.0345 1.1279 0.0695 +#> 362: 92.6415 -5.8861 -2.3362 -4.1072 -0.9499 0.1503 0.1126 3.5210 0.8306 4.0636 0.1507 0.2333 1.4218 0.0345 1.1273 0.0695 +#> 363: 92.6412 -5.8884 -2.3364 -4.1066 -0.9499 0.1498 0.1126 3.5327 0.8305 4.0646 0.1507 0.2328 1.4221 0.0345 1.1273 0.0695 +#> 364: 92.6411 -5.8895 -2.3367 -4.1062 -0.9501 0.1494 0.1126 3.5366 0.8306 4.0659 0.1507 0.2322 1.4227 0.0345 1.1280 0.0695 +#> 365: 92.6411 -5.8908 -2.3367 -4.1060 -0.9502 0.1489 0.1125 3.5405 0.8307 4.0690 0.1507 0.2317 1.4228 0.0344 1.1280 0.0695 +#> 366: 92.6412 -5.8926 -2.3366 -4.1062 -0.9502 0.1484 0.1125 3.5483 0.8307 4.0724 0.1507 0.2311 1.4228 0.0344 1.1280 0.0695 +#> 367: 92.6406 -5.8940 -2.3366 -4.1059 -0.9503 0.1483 0.1124 3.5557 0.8308 4.0738 0.1507 0.2305 1.4228 0.0344 1.1273 0.0695 +#> 368: 92.6402 -5.8940 -2.3365 -4.1059 -0.9504 0.1483 0.1122 3.5538 0.8306 4.0773 0.1507 0.2299 1.4228 0.0344 1.1266 0.0696 +#> 369: 92.6398 -5.8933 -2.3366 -4.1058 -0.9504 0.1482 0.1122 3.5489 0.8303 4.0796 0.1507 0.2295 1.4228 0.0343 1.1261 0.0696 +#> 370: 92.6394 -5.8928 -2.3366 -4.1059 -0.9504 0.1481 0.1123 3.5445 0.8302 4.0819 0.1506 0.2291 1.4229 0.0343 1.1258 0.0696 +#> 371: 92.6390 -5.8930 -2.3369 -4.1062 -0.9503 0.1481 0.1125 3.5446 0.8299 4.0854 0.1506 0.2285 1.4230 0.0343 1.1257 0.0696 +#> 372: 92.6387 -5.8926 -2.3372 -4.1064 -0.9503 0.1482 0.1125 3.5424 0.8298 4.0887 0.1505 0.2281 1.4234 0.0343 1.1262 0.0696 +#> 373: 92.6385 -5.8927 -2.3376 -4.1067 -0.9502 0.1483 0.1126 3.5447 0.8297 4.0919 0.1504 0.2275 1.4236 0.0343 1.1268 0.0696 +#> 374: 92.6382 -5.8932 -2.3380 -4.1064 -0.9502 0.1481 0.1131 3.5490 0.8295 4.0929 0.1503 0.2272 1.4238 0.0343 1.1267 0.0696 +#> 375: 92.6385 -5.8944 -2.3383 -4.1062 -0.9502 0.1481 0.1136 3.5562 0.8292 4.0936 0.1503 0.2269 1.4240 0.0343 1.1274 0.0695 +#> 376: 92.6388 -5.8942 -2.3387 -4.1061 -0.9502 0.1481 0.1141 3.5575 0.8295 4.0942 0.1502 0.2267 1.4236 0.0343 1.1272 0.0695 +#> 377: 92.6389 -5.8942 -2.3392 -4.1060 -0.9502 0.1482 0.1145 3.5579 0.8298 4.0950 0.1501 0.2264 1.4233 0.0344 1.1272 0.0695 +#> 378: 92.6388 -5.8939 -2.3397 -4.1060 -0.9502 0.1481 0.1150 3.5558 0.8298 4.0959 0.1500 0.2261 1.4232 0.0344 1.1271 0.0695 +#> 379: 92.6388 -5.8934 -2.3399 -4.1062 -0.9500 0.1483 0.1153 3.5521 0.8294 4.0980 0.1500 0.2257 1.4236 0.0344 1.1279 0.0694 +#> 380: 92.6390 -5.8920 -2.3402 -4.1065 -0.9499 0.1484 0.1155 3.5446 0.8292 4.1007 0.1500 0.2254 1.4241 0.0344 1.1285 0.0694 +#> 381: 92.6394 -5.8906 -2.3404 -4.1069 -0.9498 0.1485 0.1157 3.5378 0.8290 4.1040 0.1500 0.2250 1.4249 0.0343 1.1296 0.0694 +#> 382: 92.6403 -5.8893 -2.3406 -4.1085 -0.9498 0.1487 0.1157 3.5319 0.8289 4.1195 0.1500 0.2246 1.4250 0.0343 1.1301 0.0694 +#> 383: 92.6402 -5.8882 -2.3408 -4.1096 -0.9499 0.1488 0.1155 3.5269 0.8287 4.1290 0.1500 0.2243 1.4253 0.0343 1.1300 0.0694 +#> 384: 92.6401 -5.8871 -2.3412 -4.1102 -0.9498 0.1490 0.1155 3.5219 0.8285 4.1340 0.1499 0.2241 1.4254 0.0343 1.1297 0.0694 +#> 385: 92.6396 -5.8867 -2.3417 -4.1105 -0.9497 0.1493 0.1155 3.5195 0.8281 4.1364 0.1498 0.2238 1.4252 0.0343 1.1297 0.0695 +#> 386: 92.6393 -5.8863 -2.3423 -4.1116 -0.9496 0.1497 0.1153 3.5190 0.8280 4.1452 0.1497 0.2235 1.4251 0.0343 1.1307 0.0694 +#> 387: 92.6391 -5.8865 -2.3429 -4.1124 -0.9495 0.1498 0.1155 3.5219 0.8280 4.1502 0.1497 0.2234 1.4247 0.0343 1.1301 0.0695 +#> 388: 92.6389 -5.8861 -2.3436 -4.1129 -0.9494 0.1501 0.1158 3.5228 0.8278 4.1540 0.1496 0.2233 1.4243 0.0343 1.1293 0.0695 +#> 389: 92.6384 -5.8849 -2.3442 -4.1132 -0.9491 0.1504 0.1159 3.5195 0.8276 4.1571 0.1496 0.2231 1.4242 0.0343 1.1284 0.0696 +#> 390: 92.6382 -5.8838 -2.3447 -4.1134 -0.9489 0.1506 0.1159 3.5172 0.8276 4.1603 0.1497 0.2230 1.4242 0.0343 1.1273 0.0697 +#> 391: 92.6380 -5.8821 -2.3454 -4.1140 -0.9486 0.1509 0.1159 3.5134 0.8274 4.1661 0.1498 0.2228 1.4238 0.0343 1.1266 0.0697 +#> 392: 92.6374 -5.8800 -2.3460 -4.1140 -0.9485 0.1513 0.1158 3.5069 0.8274 4.1673 0.1499 0.2226 1.4235 0.0343 1.1258 0.0698 +#> 393: 92.6372 -5.8785 -2.3467 -4.1140 -0.9485 0.1514 0.1159 3.5019 0.8275 4.1684 0.1499 0.2223 1.4232 0.0343 1.1258 0.0698 +#> 394: 92.6372 -5.8765 -2.3473 -4.1142 -0.9485 0.1515 0.1161 3.4955 0.8275 4.1710 0.1499 0.2221 1.4228 0.0344 1.1260 0.0697 +#> 395: 92.6371 -5.8761 -2.3476 -4.1145 -0.9485 0.1515 0.1164 3.4940 0.8273 4.1739 0.1498 0.2220 1.4227 0.0344 1.1254 0.0698 +#> 396: 92.6370 -5.8759 -2.3480 -4.1147 -0.9485 0.1516 0.1166 3.4942 0.8269 4.1764 0.1498 0.2217 1.4222 0.0344 1.1252 0.0698 +#> 397: 92.6371 -5.8756 -2.3483 -4.1149 -0.9486 0.1516 0.1167 3.4914 0.8267 4.1796 0.1498 0.2214 1.4219 0.0344 1.1253 0.0697 +#> 398: 92.6371 -5.8756 -2.3486 -4.1155 -0.9486 0.1518 0.1167 3.4909 0.8268 4.1840 0.1498 0.2210 1.4216 0.0344 1.1250 0.0697 +#> 399: 92.6368 -5.8765 -2.3489 -4.1157 -0.9485 0.1519 0.1170 3.4958 0.8266 4.1866 0.1498 0.2205 1.4213 0.0344 1.1245 0.0698 +#> 400: 92.6368 -5.8769 -2.3491 -4.1158 -0.9485 0.1522 0.1174 3.4972 0.8266 4.1888 0.1499 0.2200 1.4209 0.0344 1.1242 0.0698 +#> 401: 92.6366 -5.8768 -2.3493 -4.1161 -0.9484 0.1524 0.1175 3.4964 0.8267 4.1913 0.1499 0.2196 1.4204 0.0344 1.1240 0.0698 +#> 402: 92.6362 -5.8767 -2.3495 -4.1164 -0.9483 0.1525 0.1176 3.4961 0.8267 4.1937 0.1499 0.2192 1.4201 0.0344 1.1240 0.0698 +#> 403: 92.6362 -5.8769 -2.3497 -4.1166 -0.9483 0.1526 0.1178 3.4981 0.8270 4.1960 0.1499 0.2187 1.4197 0.0345 1.1236 0.0698 +#> 404: 92.6359 -5.8772 -2.3499 -4.1166 -0.9483 0.1527 0.1179 3.4997 0.8272 4.1968 0.1499 0.2183 1.4193 0.0345 1.1232 0.0698 +#> 405: 92.6355 -5.8763 -2.3501 -4.1165 -0.9483 0.1527 0.1180 3.4946 0.8273 4.1976 0.1500 0.2180 1.4189 0.0345 1.1230 0.0698 +#> 406: 92.6351 -5.8768 -2.3503 -4.1164 -0.9482 0.1528 0.1184 3.4953 0.8274 4.1979 0.1500 0.2176 1.4184 0.0345 1.1227 0.0698 +#> 407: 92.6346 -5.8772 -2.3505 -4.1165 -0.9481 0.1527 0.1187 3.4965 0.8275 4.1999 0.1500 0.2173 1.4182 0.0344 1.1222 0.0698 +#> 408: 92.6344 -5.8786 -2.3508 -4.1167 -0.9482 0.1528 0.1190 3.5025 0.8276 4.2020 0.1500 0.2171 1.4178 0.0344 1.1215 0.0699 +#> 409: 92.6342 -5.8806 -2.3511 -4.1168 -0.9484 0.1529 0.1193 3.5134 0.8277 4.2037 0.1500 0.2167 1.4176 0.0344 1.1212 0.0699 +#> 410: 92.6341 -5.8826 -2.3514 -4.1170 -0.9486 0.1531 0.1193 3.5229 0.8279 4.2061 0.1500 0.2163 1.4175 0.0344 1.1212 0.0699 +#> 411: 92.6339 -5.8840 -2.3517 -4.1172 -0.9488 0.1532 0.1192 3.5280 0.8280 4.2087 0.1499 0.2159 1.4175 0.0345 1.1208 0.0699 +#> 412: 92.6338 -5.8850 -2.3520 -4.1175 -0.9489 0.1534 0.1193 3.5311 0.8280 4.2121 0.1497 0.2155 1.4177 0.0345 1.1204 0.0699 +#> 413: 92.6343 -5.8859 -2.3523 -4.1177 -0.9491 0.1536 0.1191 3.5337 0.8282 4.2156 0.1497 0.2151 1.4176 0.0345 1.1198 0.0699 +#> 414: 92.6350 -5.8861 -2.3526 -4.1184 -0.9491 0.1540 0.1191 3.5350 0.8283 4.2209 0.1496 0.2147 1.4177 0.0345 1.1196 0.0699 +#> 415: 92.6354 -5.8866 -2.3528 -4.1191 -0.9492 0.1543 0.1191 3.5373 0.8284 4.2258 0.1496 0.2142 1.4179 0.0345 1.1191 0.0699 +#> 416: 92.6360 -5.8873 -2.3531 -4.1201 -0.9493 0.1548 0.1193 3.5431 0.8286 4.2328 0.1495 0.2137 1.4178 0.0345 1.1187 0.0699 +#> 417: 92.6361 -5.8878 -2.3533 -4.1213 -0.9494 0.1551 0.1192 3.5465 0.8288 4.2415 0.1494 0.2131 1.4182 0.0345 1.1189 0.0699 +#> 418: 92.6366 -5.8883 -2.3535 -4.1221 -0.9495 0.1555 0.1194 3.5499 0.8291 4.2477 0.1493 0.2127 1.4180 0.0345 1.1184 0.0699 +#> 419: 92.6367 -5.8885 -2.3536 -4.1236 -0.9495 0.1560 0.1195 3.5517 0.8292 4.2588 0.1492 0.2123 1.4179 0.0345 1.1180 0.0700 +#> 420: 92.6371 -5.8874 -2.3536 -4.1249 -0.9495 0.1564 0.1197 3.5474 0.8293 4.2666 0.1491 0.2118 1.4181 0.0345 1.1182 0.0700 +#> 421: 92.6374 -5.8860 -2.3537 -4.1263 -0.9494 0.1569 0.1197 3.5416 0.8292 4.2759 0.1492 0.2114 1.4184 0.0345 1.1188 0.0699 +#> 422: 92.6377 -5.8850 -2.3538 -4.1279 -0.9493 0.1572 0.1197 3.5365 0.8292 4.2865 0.1491 0.2110 1.4185 0.0345 1.1188 0.0700 +#> 423: 92.6380 -5.8844 -2.3540 -4.1299 -0.9494 0.1576 0.1196 3.5323 0.8290 4.2999 0.1491 0.2106 1.4186 0.0345 1.1192 0.0699 +#> 424: 92.6382 -5.8842 -2.3541 -4.1312 -0.9495 0.1581 0.1198 3.5309 0.8290 4.3092 0.1491 0.2103 1.4184 0.0345 1.1197 0.0699 +#> 425: 92.6382 -5.8838 -2.3543 -4.1320 -0.9495 0.1584 0.1197 3.5281 0.8289 4.3140 0.1491 0.2099 1.4185 0.0346 1.1196 0.0699 +#> 426: 92.6380 -5.8829 -2.3545 -4.1327 -0.9494 0.1587 0.1196 3.5234 0.8293 4.3183 0.1491 0.2096 1.4182 0.0346 1.1194 0.0699 +#> 427: 92.6375 -5.8823 -2.3548 -4.1335 -0.9494 0.1589 0.1197 3.5189 0.8295 4.3233 0.1492 0.2092 1.4180 0.0346 1.1196 0.0699 +#> 428: 92.6370 -5.8813 -2.3552 -4.1343 -0.9494 0.1592 0.1199 3.5140 0.8295 4.3286 0.1491 0.2088 1.4182 0.0346 1.1198 0.0699 +#> 429: 92.6368 -5.8802 -2.3556 -4.1356 -0.9495 0.1597 0.1202 3.5093 0.8296 4.3372 0.1491 0.2086 1.4182 0.0346 1.1208 0.0699 +#> 430: 92.6370 -5.8794 -2.3560 -4.1366 -0.9496 0.1602 0.1201 3.5058 0.8297 4.3439 0.1492 0.2084 1.4183 0.0346 1.1216 0.0698 +#> 431: 92.6371 -5.8792 -2.3564 -4.1372 -0.9497 0.1606 0.1201 3.5029 0.8298 4.3473 0.1493 0.2082 1.4182 0.0346 1.1215 0.0698 +#> 432: 92.6371 -5.8793 -2.3567 -4.1377 -0.9499 0.1609 0.1201 3.5008 0.8297 4.3499 0.1494 0.2080 1.4180 0.0346 1.1218 0.0698 +#> 433: 92.6370 -5.8799 -2.3570 -4.1387 -0.9501 0.1612 0.1201 3.5014 0.8298 4.3560 0.1495 0.2078 1.4180 0.0346 1.1218 0.0699 +#> 434: 92.6371 -5.8790 -2.3573 -4.1398 -0.9501 0.1615 0.1200 3.4982 0.8300 4.3624 0.1496 0.2076 1.4179 0.0346 1.1213 0.0699 +#> 435: 92.6368 -5.8789 -2.3576 -4.1409 -0.9501 0.1619 0.1199 3.4979 0.8302 4.3697 0.1496 0.2074 1.4176 0.0346 1.1205 0.0699 +#> 436: 92.6365 -5.8792 -2.3579 -4.1424 -0.9500 0.1623 0.1197 3.4987 0.8304 4.3798 0.1497 0.2073 1.4173 0.0346 1.1198 0.0699 +#> 437: 92.6364 -5.8798 -2.3582 -4.1439 -0.9500 0.1627 0.1195 3.5017 0.8307 4.3905 0.1497 0.2071 1.4172 0.0346 1.1191 0.0700 +#> 438: 92.6362 -5.8803 -2.3585 -4.1450 -0.9499 0.1631 0.1193 3.5053 0.8309 4.3973 0.1497 0.2070 1.4172 0.0346 1.1186 0.0700 +#> 439: 92.6361 -5.8811 -2.3588 -4.1463 -0.9498 0.1634 0.1190 3.5101 0.8312 4.4052 0.1496 0.2069 1.4172 0.0346 1.1188 0.0700 +#> 440: 92.6360 -5.8816 -2.3591 -4.1477 -0.9498 0.1637 0.1187 3.5127 0.8315 4.4145 0.1495 0.2068 1.4172 0.0346 1.1189 0.0700 +#> 441: 92.6357 -5.8816 -2.3594 -4.1492 -0.9499 0.1640 0.1185 3.5136 0.8319 4.4252 0.1494 0.2069 1.4175 0.0346 1.1191 0.0700 +#> 442: 92.6356 -5.8819 -2.3596 -4.1501 -0.9500 0.1642 0.1181 3.5151 0.8323 4.4310 0.1494 0.2070 1.4176 0.0346 1.1193 0.0700 +#> 443: 92.6356 -5.8825 -2.3598 -4.1512 -0.9501 0.1643 0.1180 3.5178 0.8324 4.4379 0.1493 0.2071 1.4179 0.0346 1.1196 0.0700 +#> 444: 92.6352 -5.8827 -2.3602 -4.1525 -0.9502 0.1644 0.1180 3.5169 0.8327 4.4458 0.1493 0.2073 1.4178 0.0346 1.1198 0.0700 +#> 445: 92.6348 -5.8828 -2.3605 -4.1534 -0.9502 0.1643 0.1180 3.5178 0.8329 4.4505 0.1493 0.2074 1.4178 0.0346 1.1202 0.0700 +#> 446: 92.6342 -5.8830 -2.3609 -4.1541 -0.9503 0.1643 0.1183 3.5182 0.8331 4.4539 0.1494 0.2077 1.4176 0.0346 1.1199 0.0700 +#> 447: 92.6334 -5.8832 -2.3613 -4.1548 -0.9503 0.1643 0.1188 3.5188 0.8333 4.4571 0.1494 0.2079 1.4172 0.0346 1.1198 0.0700 +#> 448: 92.6331 -5.8833 -2.3616 -4.1557 -0.9503 0.1643 0.1190 3.5190 0.8335 4.4613 0.1494 0.2080 1.4170 0.0346 1.1198 0.0700 +#> 449: 92.6327 -5.8835 -2.3619 -4.1563 -0.9504 0.1641 0.1192 3.5191 0.8335 4.4636 0.1493 0.2081 1.4172 0.0346 1.1196 0.0700 +#> 450: 92.6322 -5.8831 -2.3620 -4.1566 -0.9505 0.1639 0.1194 3.5152 0.8340 4.4647 0.1492 0.2083 1.4172 0.0346 1.1189 0.0700 +#> 451: 92.6315 -5.8835 -2.3622 -4.1569 -0.9505 0.1635 0.1194 3.5192 0.8343 4.4648 0.1492 0.2084 1.4169 0.0346 1.1187 0.0700 +#> 452: 92.6312 -5.8834 -2.3625 -4.1572 -0.9506 0.1632 0.1193 3.5173 0.8345 4.4654 0.1492 0.2086 1.4166 0.0346 1.1183 0.0700 +#> 453: 92.6309 -5.8838 -2.3628 -4.1574 -0.9506 0.1629 0.1193 3.5175 0.8348 4.4660 0.1493 0.2087 1.4166 0.0346 1.1180 0.0700 +#> 454: 92.6307 -5.8832 -2.3629 -4.1574 -0.9507 0.1625 0.1193 3.5128 0.8354 4.4658 0.1493 0.2087 1.4164 0.0346 1.1176 0.0700 +#> 455: 92.6305 -5.8821 -2.3632 -4.1579 -0.9508 0.1624 0.1192 3.5071 0.8360 4.4678 0.1494 0.2089 1.4164 0.0346 1.1171 0.0701 +#> 456: 92.6307 -5.8811 -2.3634 -4.1589 -0.9509 0.1623 0.1190 3.5014 0.8364 4.4730 0.1494 0.2088 1.4168 0.0346 1.1168 0.0701 +#> 457: 92.6307 -5.8808 -2.3636 -4.1597 -0.9509 0.1621 0.1188 3.4980 0.8368 4.4772 0.1494 0.2089 1.4168 0.0347 1.1166 0.0701 +#> 458: 92.6308 -5.8813 -2.3638 -4.1607 -0.9510 0.1621 0.1185 3.4994 0.8369 4.4823 0.1494 0.2088 1.4168 0.0347 1.1161 0.0701 +#> 459: 92.6308 -5.8819 -2.3639 -4.1615 -0.9511 0.1620 0.1184 3.5008 0.8371 4.4861 0.1494 0.2086 1.4167 0.0347 1.1155 0.0701 +#> 460: 92.6309 -5.8824 -2.3642 -4.1621 -0.9511 0.1621 0.1182 3.5024 0.8374 4.4886 0.1493 0.2085 1.4164 0.0347 1.1148 0.0702 +#> 461: 92.6309 -5.8821 -2.3647 -4.1631 -0.9511 0.1621 0.1181 3.5000 0.8378 4.4937 0.1493 0.2084 1.4160 0.0347 1.1141 0.0702 +#> 462: 92.6309 -5.8825 -2.3651 -4.1638 -0.9511 0.1623 0.1180 3.5006 0.8381 4.4975 0.1492 0.2082 1.4156 0.0348 1.1133 0.0702 +#> 463: 92.6307 -5.8824 -2.3656 -4.1654 -0.9510 0.1624 0.1179 3.5000 0.8382 4.5074 0.1491 0.2081 1.4154 0.0348 1.1124 0.0702 +#> 464: 92.6305 -5.8825 -2.3660 -4.1668 -0.9510 0.1625 0.1178 3.5001 0.8384 4.5171 0.1491 0.2080 1.4149 0.0348 1.1115 0.0703 +#> 465: 92.6302 -5.8828 -2.3664 -4.1681 -0.9511 0.1626 0.1179 3.5012 0.8386 4.5247 0.1490 0.2079 1.4151 0.0348 1.1107 0.0703 +#> 466: 92.6300 -5.8827 -2.3668 -4.1697 -0.9511 0.1626 0.1179 3.5005 0.8390 4.5370 0.1490 0.2079 1.4148 0.0349 1.1098 0.0704 +#> 467: 92.6301 -5.8828 -2.3671 -4.1721 -0.9512 0.1628 0.1180 3.4991 0.8393 4.5562 0.1490 0.2078 1.4148 0.0349 1.1092 0.0704 +#> 468: 92.6303 -5.8833 -2.3675 -4.1745 -0.9513 0.1630 0.1181 3.4996 0.8397 4.5756 0.1489 0.2078 1.4148 0.0349 1.1086 0.0704 +#> 469: 92.6304 -5.8835 -2.3680 -4.1759 -0.9513 0.1630 0.1181 3.4991 0.8401 4.5829 0.1490 0.2080 1.4145 0.0349 1.1082 0.0704 +#> 470: 92.6304 -5.8839 -2.3685 -4.1772 -0.9512 0.1630 0.1183 3.4993 0.8405 4.5904 0.1490 0.2081 1.4142 0.0349 1.1079 0.0704 +#> 471: 92.6304 -5.8838 -2.3690 -4.1786 -0.9511 0.1631 0.1182 3.4992 0.8408 4.5981 0.1489 0.2082 1.4143 0.0350 1.1075 0.0704 +#> 472: 92.6301 -5.8839 -2.3695 -4.1800 -0.9511 0.1631 0.1182 3.5005 0.8413 4.6063 0.1488 0.2083 1.4143 0.0350 1.1072 0.0704 +#> 473: 92.6296 -5.8841 -2.3699 -4.1811 -0.9510 0.1630 0.1182 3.5019 0.8417 4.6119 0.1487 0.2085 1.4142 0.0350 1.1065 0.0704 +#> 474: 92.6293 -5.8843 -2.3704 -4.1823 -0.9510 0.1629 0.1184 3.5038 0.8422 4.6182 0.1487 0.2087 1.4145 0.0350 1.1060 0.0704 +#> 475: 92.6293 -5.8851 -2.3709 -4.1839 -0.9509 0.1628 0.1185 3.5084 0.8426 4.6277 0.1487 0.2089 1.4142 0.0351 1.1057 0.0704 +#> 476: 92.6293 -5.8854 -2.3713 -4.1847 -0.9509 0.1627 0.1185 3.5137 0.8430 4.6318 0.1486 0.2092 1.4139 0.0351 1.1057 0.0704 +#> 477: 92.6292 -5.8858 -2.3718 -4.1859 -0.9508 0.1627 0.1183 3.5201 0.8430 4.6397 0.1485 0.2095 1.4139 0.0351 1.1060 0.0704 +#> 478: 92.6291 -5.8871 -2.3722 -4.1867 -0.9508 0.1625 0.1181 3.5291 0.8432 4.6449 0.1483 0.2098 1.4140 0.0351 1.1058 0.0704 +#> 479: 92.6293 -5.8891 -2.3726 -4.1873 -0.9509 0.1623 0.1178 3.5422 0.8435 4.6486 0.1482 0.2100 1.4139 0.0352 1.1056 0.0704 +#> 480: 92.6294 -5.8910 -2.3730 -4.1881 -0.9509 0.1622 0.1175 3.5568 0.8437 4.6535 0.1482 0.2102 1.4140 0.0352 1.1053 0.0705 +#> 481: 92.6297 -5.8919 -2.3734 -4.1888 -0.9509 0.1621 0.1174 3.5650 0.8440 4.6572 0.1482 0.2104 1.4138 0.0353 1.1051 0.0705 +#> 482: 92.6293 -5.8929 -2.3737 -4.1894 -0.9509 0.1619 0.1173 3.5745 0.8444 4.6620 0.1482 0.2107 1.4134 0.0353 1.1047 0.0705 +#> 483: 92.6284 -5.8939 -2.3741 -4.1901 -0.9508 0.1616 0.1176 3.5832 0.8446 4.6672 0.1482 0.2109 1.4131 0.0353 1.1044 0.0705 +#> 484: 92.6276 -5.8943 -2.3744 -4.1904 -0.9507 0.1615 0.1179 3.5877 0.8447 4.6692 0.1483 0.2113 1.4128 0.0353 1.1041 0.0705 +#> 485: 92.6266 -5.8947 -2.3746 -4.1912 -0.9507 0.1616 0.1182 3.5903 0.8448 4.6751 0.1483 0.2115 1.4126 0.0354 1.1042 0.0705 +#> 486: 92.6258 -5.8952 -2.3749 -4.1918 -0.9508 0.1615 0.1185 3.5929 0.8450 4.6799 0.1485 0.2115 1.4125 0.0354 1.1045 0.0704 +#> 487: 92.6250 -5.8956 -2.3750 -4.1923 -0.9509 0.1614 0.1189 3.5922 0.8452 4.6835 0.1486 0.2115 1.4122 0.0354 1.1050 0.0704 +#> 488: 92.6242 -5.8956 -2.3752 -4.1927 -0.9510 0.1613 0.1191 3.5898 0.8453 4.6866 0.1487 0.2115 1.4119 0.0354 1.1051 0.0704 +#> 489: 92.6238 -5.8954 -2.3753 -4.1932 -0.9511 0.1611 0.1190 3.5871 0.8454 4.6905 0.1487 0.2115 1.4118 0.0354 1.1057 0.0704 +#> 490: 92.6237 -5.8951 -2.3754 -4.1936 -0.9511 0.1611 0.1188 3.5839 0.8454 4.6945 0.1487 0.2114 1.4117 0.0354 1.1064 0.0703 +#> 491: 92.6235 -5.8942 -2.3755 -4.1941 -0.9511 0.1610 0.1187 3.5790 0.8455 4.6981 0.1488 0.2115 1.4118 0.0354 1.1068 0.0703 +#> 492: 92.6234 -5.8938 -2.3755 -4.1952 -0.9512 0.1609 0.1186 3.5760 0.8454 4.7074 0.1488 0.2115 1.4119 0.0354 1.1074 0.0703 +#> 493: 92.6236 -5.8938 -2.3755 -4.1958 -0.9512 0.1608 0.1186 3.5747 0.8454 4.7121 0.1488 0.2114 1.4120 0.0354 1.1078 0.0702 +#> 494: 92.6239 -5.8945 -2.3756 -4.1964 -0.9513 0.1607 0.1186 3.5772 0.8455 4.7167 0.1488 0.2115 1.4120 0.0354 1.1082 0.0702 +#> 495: 92.6242 -5.8950 -2.3756 -4.1971 -0.9514 0.1605 0.1187 3.5798 0.8454 4.7227 0.1489 0.2117 1.4122 0.0354 1.1084 0.0702 +#> 496: 92.6242 -5.8962 -2.3757 -4.1978 -0.9514 0.1603 0.1189 3.5870 0.8455 4.7283 0.1489 0.2119 1.4121 0.0354 1.1090 0.0702 +#> 497: 92.6241 -5.8972 -2.3757 -4.1981 -0.9514 0.1602 0.1191 3.5934 0.8454 4.7298 0.1488 0.2120 1.4123 0.0354 1.1096 0.0701 +#> 498: 92.6244 -5.8973 -2.3758 -4.1981 -0.9514 0.1601 0.1190 3.5947 0.8454 4.7296 0.1488 0.2121 1.4123 0.0354 1.1101 0.0701 +#> 499: 92.6244 -5.8968 -2.3759 -4.1980 -0.9514 0.1600 0.1188 3.5935 0.8453 4.7290 0.1488 0.2124 1.4123 0.0354 1.1108 0.0701 +#> 500: 92.6245 -5.8959 -2.3759 -4.1978 -0.9513 0.1597 0.1188 3.5912 0.8452 4.7282 0.1488 0.2126 1.4123 0.0354 1.1111 0.0701</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='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> -#> <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_17~exp(-(rx_expr_8));</span> -#> <span class='message'>rx_expr_19~1+rx_expr_17;</span> -#> <span class='message'>rx_expr_24~1/(rx_expr_19);</span> -#> <span class='message'>rx_expr_26~(rx_expr_24);</span> -#> <span class='message'>rx_expr_27~1-rx_expr_26;</span> -#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</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(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</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_18~rx_expr_5*(rx_expr_1);</span> -#> <span class='message'>rx_lambda_~rx_expr_18+rx_expr_3;</span> -#> <span class='message'>rx_hi_~rx_expr_18+rx_expr_3;</span> -#> <span class='message'>rx_low_~0;</span> -#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> -#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> -#> <span class='message'>rx_expr_22~rx_expr_11*(rx_expr_1);</span> -#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>rx_r_=(rx_expr_0)*(Rx_pow_di(((rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1)),2)*Rx_pow_di(THETA[10],2)+Rx_pow_di(THETA[9],2))+(Rx_pow_di(THETA[8],2)*Rx_pow_di(((rx_expr_4+rx_expr_22)*(rx_expr_1)),2)+Rx_pow_di(THETA[7],2))*(rx_expr_2)*(rx_expr_1);</span> -#> <span class='message'>parent_0=THETA[1];</span> -#> <span class='message'>log_k_A1=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_parent=THETA[7];</span> -#> <span class='message'>rsd_high_parent=THETA[8];</span> -#> <span class='message'>sigma_low_A1=THETA[9];</span> -#> <span class='message'>rsd_high_A1=THETA[10];</span> -#> <span class='message'>eta.parent_0=ETA[1];</span> -#> <span class='message'>eta.log_k_A1=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_A1=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_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: 17.5 0.646 18.15</span></div><div class='input'> +</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_A1 |f_parent_qlogis | log_k1 | +#> |.....................| log_k2 | g_qlogis |sigma_low_parent |rsd_high_parent | +#> |.....................|sigma_low_A1 |rsd_high_A1 | o1 | o2 | +#> |.....................| o3 | o4 | o5 | o6 | +#> |<span style='font-weight: bold;'> 1</span>| 495.80376 | 1.000 | -1.000 | -0.9110 | -0.9380 | +#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | +#> |.....................| -0.8755 | -0.8915 | -0.8776 | -0.8741 | +#> |.....................| -0.8681 | -0.8727 | -0.8749 | -0.8675 | +#> | U| 495.80376 | 91.48 | -5.189 | -0.8875 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | +#> |.....................| 0.8280 | 0.05769 | 0.7296 | 0.8969 | +#> |.....................| 1.185 | 0.9628 | 0.8582 | 1.216 | +#> | X|<span style='font-weight: bold;'> 495.80376</span> | 91.48 | 0.005580 | 0.2916 | 0.1119 | +#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | +#> |.....................| 0.8280 | 0.05769 | 0.7296 | 0.8969 | +#> |.....................| 1.185 | 0.9628 | 0.8582 | 1.216 | +#> | G| Gill Diff. | 40.10 | 2.344 | -0.09792 | 0.01304 | +#> |.....................| -0.4854 | 0.6353 | -23.92 | -17.76 | +#> |.....................| -5.723 | -2.232 | 1.261 | 9.993 | +#> |.....................| -12.68 | -0.7774 | 8.106 | -12.55 | +#> |<span style='font-weight: bold;'> 2</span>| 3318.3701 | 0.2710 | -1.043 | -0.9092 | -0.9382 | +#> |.....................| -0.9796 | -0.8947 | -0.4406 | -0.5686 | +#> |.....................| -0.7715 | -0.8509 | -0.9005 | -1.056 | +#> |.....................| -0.6376 | -0.8586 | -1.022 | -0.6393 | +#> | U| 3318.3701 | 24.79 | -5.231 | -0.8859 | -2.190 | +#> |.....................| -4.622 | 0.4536 | 1.008 | 0.06701 | +#> |.....................| 0.8711 | 0.05887 | 0.7129 | 0.7340 | +#> |.....................| 1.458 | 0.9764 | 0.7317 | 1.493 | +#> | X|<span style='font-weight: bold;'> 3318.3701</span> | 24.79 | 0.005347 | 0.2920 | 0.1119 | +#> |.....................| 0.009837 | 0.6115 | 1.008 | 0.06701 | +#> |.....................| 0.8711 | 0.05887 | 0.7129 | 0.7340 | +#> |.....................| 1.458 | 0.9764 | 0.7317 | 1.493 | +#> |<span style='font-weight: bold;'> 3</span>| 512.37365 | 0.9271 | -1.004 | -0.9108 | -0.9380 | +#> |.....................| -0.9876 | -0.8843 | -0.8320 | -0.8592 | +#> |.....................| -0.8651 | -0.8874 | -0.8799 | -0.8923 | +#> |.....................| -0.8451 | -0.8713 | -0.8896 | -0.8447 | +#> | U| 512.37365 | 84.82 | -5.193 | -0.8873 | -2.190 | +#> |.....................| -4.630 | 0.4584 | 0.8460 | 0.05863 | +#> |.....................| 0.8323 | 0.05781 | 0.7279 | 0.8806 | +#> |.....................| 1.212 | 0.9641 | 0.8455 | 1.244 | +#> | X|<span style='font-weight: bold;'> 512.37365</span> | 84.82 | 0.005556 | 0.2917 | 0.1119 | +#> |.....................| 0.009759 | 0.6126 | 0.8460 | 0.05863 | +#> |.....................| 0.8323 | 0.05781 | 0.7279 | 0.8806 | +#> |.....................| 1.212 | 0.9641 | 0.8455 | 1.244 | +#> |<span style='font-weight: bold;'> 4</span>| 495.44913 | 0.9909 | -1.001 | -0.9110 | -0.9380 | +#> |.....................| -0.9883 | -0.8833 | -0.8701 | -0.8874 | +#> |.....................| -0.8742 | -0.8910 | -0.8778 | -0.8764 | +#> |.....................| -0.8653 | -0.8726 | -0.8767 | -0.8647 | +#> | U| 495.44913 | 90.65 | -5.189 | -0.8874 | -2.190 | +#> |.....................| -4.630 | 0.4589 | 0.8303 | 0.05781 | +#> |.....................| 0.8286 | 0.05771 | 0.7294 | 0.8949 | +#> |.....................| 1.189 | 0.9629 | 0.8566 | 1.219 | +#> | X|<span style='font-weight: bold;'> 495.44913</span> | 90.65 | 0.005577 | 0.2916 | 0.1119 | +#> |.....................| 0.009751 | 0.6127 | 0.8303 | 0.05781 | +#> |.....................| 0.8286 | 0.05771 | 0.7294 | 0.8949 | +#> |.....................| 1.189 | 0.9629 | 0.8566 | 1.219 | +#> | F| Forward Diff. | -32.24 | 2.221 | -0.3999 | 0.1183 | +#> |.....................| -0.4367 | 0.6696 | -24.35 | -18.50 | +#> |.....................| -5.733 | -2.007 | 1.154 | 9.098 | +#> |.....................| -12.48 | -0.2426 | 8.051 | -12.28 | +#> |<span style='font-weight: bold;'> 5</span>| 495.09570 | 0.9990 | -1.001 | -0.9109 | -0.9380 | +#> |.....................| -0.9882 | -0.8835 | -0.8640 | -0.8828 | +#> |.....................| -0.8728 | -0.8905 | -0.8781 | -0.8786 | +#> |.....................| -0.8621 | -0.8725 | -0.8788 | -0.8616 | +#> | U| 495.0957 | 91.39 | -5.190 | -0.8874 | -2.190 | +#> |.....................| -4.630 | 0.4588 | 0.8328 | 0.05794 | +#> |.....................| 0.8291 | 0.05772 | 0.7292 | 0.8928 | +#> |.....................| 1.192 | 0.9630 | 0.8549 | 1.223 | +#> | X|<span style='font-weight: bold;'> 495.0957</span> | 91.39 | 0.005574 | 0.2917 | 0.1119 | +#> |.....................| 0.009752 | 0.6127 | 0.8328 | 0.05794 | +#> |.....................| 0.8291 | 0.05772 | 0.7292 | 0.8928 | +#> |.....................| 1.192 | 0.9630 | 0.8549 | 1.223 | +#> | F| Forward Diff. | 32.16 | 2.311 | -0.1335 | 0.03619 | +#> |.....................| -0.4432 | 0.6445 | -23.23 | -17.46 | +#> |.....................| -5.567 | -2.162 | 1.281 | 9.656 | +#> |.....................| -12.09 | -0.7018 | 7.779 | -12.29 | +#> |<span style='font-weight: bold;'> 6</span>| 494.75975 | 0.9908 | -1.002 | -0.9109 | -0.9380 | +#> |.....................| -0.9881 | -0.8836 | -0.8581 | -0.8783 | +#> |.....................| -0.8714 | -0.8899 | -0.8785 | -0.8811 | +#> |.....................| -0.8590 | -0.8723 | -0.8807 | -0.8584 | +#> | U| 494.75975 | 90.64 | -5.190 | -0.8873 | -2.190 | +#> |.....................| -4.630 | 0.4587 | 0.8352 | 0.05807 | +#> |.....................| 0.8297 | 0.05774 | 0.7290 | 0.8906 | +#> |.....................| 1.196 | 0.9632 | 0.8532 | 1.227 | +#> | X|<span style='font-weight: bold;'> 494.75975</span> | 90.64 | 0.005570 | 0.2917 | 0.1119 | +#> |.....................| 0.009754 | 0.6127 | 0.8352 | 0.05807 | +#> |.....................| 0.8297 | 0.05774 | 0.7290 | 0.8906 | +#> |.....................| 1.196 | 0.9632 | 0.8532 | 1.227 | +#> | F| Forward Diff. | -33.18 | 2.192 | -0.4095 | 0.1210 | +#> |.....................| -0.4089 | 0.6743 | -23.19 | -17.83 | +#> |.....................| -5.624 | -1.860 | 1.146 | 8.868 | +#> |.....................| -11.42 | -0.05808 | 7.519 | -12.11 | +#> |<span style='font-weight: bold;'> 7</span>| 494.42957 | 0.9992 | -1.002 | -0.9108 | -0.9380 | +#> |.....................| -0.9880 | -0.8838 | -0.8522 | -0.8738 | +#> |.....................| -0.8699 | -0.8894 | -0.8788 | -0.8834 | +#> |.....................| -0.8561 | -0.8723 | -0.8827 | -0.8554 | +#> | U| 494.42957 | 91.41 | -5.191 | -0.8872 | -2.190 | +#> |.....................| -4.630 | 0.4586 | 0.8377 | 0.05820 | +#> |.....................| 0.8303 | 0.05775 | 0.7287 | 0.8886 | +#> |.....................| 1.199 | 0.9632 | 0.8515 | 1.231 | +#> | X|<span style='font-weight: bold;'> 494.42957</span> | 91.41 | 0.005567 | 0.2917 | 0.1119 | +#> |.....................| 0.009755 | 0.6127 | 0.8377 | 0.05820 | +#> |.....................| 0.8303 | 0.05775 | 0.7287 | 0.8886 | +#> |.....................| 1.199 | 0.9632 | 0.8515 | 1.231 | +#> | F| Forward Diff. | 33.60 | 2.291 | -0.1177 | 0.03548 | +#> |.....................| -0.4327 | 0.6500 | -23.13 | -16.67 | +#> |.....................| -5.444 | -2.054 | 1.165 | 9.367 | +#> |.....................| -12.23 | 0.1305 | 7.522 | -12.12 | +#> |<span style='font-weight: bold;'> 8</span>| 494.10805 | 0.9907 | -1.003 | -0.9107 | -0.9380 | +#> |.....................| -0.9879 | -0.8840 | -0.8463 | -0.8696 | +#> |.....................| -0.8686 | -0.8889 | -0.8791 | -0.8857 | +#> |.....................| -0.8530 | -0.8723 | -0.8846 | -0.8523 | +#> | U| 494.10805 | 90.63 | -5.191 | -0.8872 | -2.190 | +#> |.....................| -4.630 | 0.4586 | 0.8401 | 0.05833 | +#> |.....................| 0.8309 | 0.05777 | 0.7285 | 0.8865 | +#> |.....................| 1.203 | 0.9632 | 0.8499 | 1.234 | +#> | X|<span style='font-weight: bold;'> 494.10805</span> | 90.63 | 0.005564 | 0.2917 | 0.1119 | +#> |.....................| 0.009756 | 0.6127 | 0.8401 | 0.05833 | +#> |.....................| 0.8309 | 0.05777 | 0.7285 | 0.8865 | +#> |.....................| 1.203 | 0.9632 | 0.8499 | 1.234 | +#> | F| Forward Diff. | -33.55 | 2.169 | -0.4095 | 0.1317 | +#> |.....................| -0.3875 | 0.6809 | -22.57 | -17.16 | +#> |.....................| -5.560 | -1.906 | 1.113 | 8.554 | +#> |.....................| -12.00 | -0.1191 | 7.606 | -11.94 | +#> |<span style='font-weight: bold;'> 9</span>| 493.79074 | 0.9992 | -1.003 | -0.9106 | -0.9381 | +#> |.....................| -0.9878 | -0.8841 | -0.8406 | -0.8652 | +#> |.....................| -0.8671 | -0.8884 | -0.8793 | -0.8879 | +#> |.....................| -0.8500 | -0.8723 | -0.8865 | -0.8493 | +#> | U| 493.79074 | 91.41 | -5.192 | -0.8871 | -2.190 | +#> |.....................| -4.630 | 0.4585 | 0.8425 | 0.05845 | +#> |.....................| 0.8315 | 0.05778 | 0.7283 | 0.8845 | +#> |.....................| 1.207 | 0.9632 | 0.8482 | 1.238 | +#> | X|<span style='font-weight: bold;'> 493.79074</span> | 91.41 | 0.005561 | 0.2917 | 0.1119 | +#> |.....................| 0.009757 | 0.6127 | 0.8425 | 0.05845 | +#> |.....................| 0.8315 | 0.05778 | 0.7283 | 0.8845 | +#> |.....................| 1.207 | 0.9632 | 0.8482 | 1.238 | +#> | F| Forward Diff. | 33.91 | 2.267 | -0.1078 | 0.03893 | +#> |.....................| -0.4090 | 0.6560 | -22.34 | -15.94 | +#> |.....................| -5.274 | -2.001 | 1.140 | 9.131 | +#> |.....................| -12.00 | -0.1724 | 7.294 | -11.95 | +#> |<span style='font-weight: bold;'> 10</span>| 493.48645 | 0.9905 | -1.004 | -0.9106 | -0.9381 | +#> |.....................| -0.9877 | -0.8843 | -0.8348 | -0.8611 | +#> |.....................| -0.8658 | -0.8879 | -0.8796 | -0.8903 | +#> |.....................| -0.8469 | -0.8723 | -0.8884 | -0.8462 | +#> | U| 493.48645 | 90.62 | -5.193 | -0.8871 | -2.190 | +#> |.....................| -4.630 | 0.4584 | 0.8449 | 0.05857 | +#> |.....................| 0.8320 | 0.05780 | 0.7281 | 0.8824 | +#> |.....................| 1.210 | 0.9632 | 0.8466 | 1.242 | +#> | X|<span style='font-weight: bold;'> 493.48645</span> | 90.62 | 0.005558 | 0.2917 | 0.1119 | +#> |.....................| 0.009758 | 0.6126 | 0.8449 | 0.05857 | +#> |.....................| 0.8320 | 0.05780 | 0.7281 | 0.8824 | +#> |.....................| 1.210 | 0.9632 | 0.8466 | 1.242 | +#> | F| Forward Diff. | -34.40 | 2.145 | -0.4154 | 0.1312 | +#> |.....................| -0.3648 | 0.6865 | -22.08 | -16.36 | +#> |.....................| -5.345 | -1.756 | 1.231 | 8.303 | +#> |.....................| -11.76 | -0.07864 | 7.355 | -11.77 | +#> |<span style='font-weight: bold;'> 11</span>| 493.18511 | 0.9993 | -1.004 | -0.9105 | -0.9381 | +#> |.....................| -0.9876 | -0.8845 | -0.8292 | -0.8570 | +#> |.....................| -0.8644 | -0.8875 | -0.8799 | -0.8924 | +#> |.....................| -0.8439 | -0.8722 | -0.8902 | -0.8432 | +#> | U| 493.18511 | 91.42 | -5.193 | -0.8870 | -2.190 | +#> |.....................| -4.630 | 0.4583 | 0.8472 | 0.05869 | +#> |.....................| 0.8326 | 0.05781 | 0.7279 | 0.8805 | +#> |.....................| 1.214 | 0.9633 | 0.8450 | 1.246 | +#> | X|<span style='font-weight: bold;'> 493.18511</span> | 91.42 | 0.005555 | 0.2917 | 0.1119 | +#> |.....................| 0.009759 | 0.6126 | 0.8472 | 0.05869 | +#> |.....................| 0.8326 | 0.05781 | 0.7279 | 0.8805 | +#> |.....................| 1.214 | 0.9633 | 0.8450 | 1.246 | +#> | F| Forward Diff. | 34.43 | 2.240 | -0.1040 | 0.04282 | +#> |.....................| -0.3912 | 0.6547 | -21.84 | -15.27 | +#> |.....................| -5.158 | -1.914 | 1.030 | 8.876 | +#> |.....................| -11.77 | -0.1415 | 7.047 | -11.78 | +#> |<span style='font-weight: bold;'> 12</span>| 492.89407 | 0.9905 | -1.005 | -0.9105 | -0.9381 | +#> |.....................| -0.9875 | -0.8847 | -0.8236 | -0.8530 | +#> |.....................| -0.8631 | -0.8870 | -0.8802 | -0.8947 | +#> |.....................| -0.8409 | -0.8722 | -0.8921 | -0.8401 | +#> | U| 492.89407 | 90.61 | -5.194 | -0.8870 | -2.190 | +#> |.....................| -4.630 | 0.4582 | 0.8495 | 0.05880 | +#> |.....................| 0.8332 | 0.05782 | 0.7277 | 0.8785 | +#> |.....................| 1.217 | 0.9633 | 0.8434 | 1.249 | +#> | X|<span style='font-weight: bold;'> 492.89407</span> | 90.61 | 0.005551 | 0.2917 | 0.1119 | +#> |.....................| 0.009760 | 0.6126 | 0.8495 | 0.05880 | +#> |.....................| 0.8332 | 0.05782 | 0.7277 | 0.8785 | +#> |.....................| 1.217 | 0.9633 | 0.8434 | 1.249 | +#> | F| Forward Diff. | -34.81 | 2.117 | -0.4182 | 0.1353 | +#> |.....................| -0.3428 | 0.6933 | -21.54 | -15.66 | +#> |.....................| -5.188 | -1.708 | 1.147 | 8.020 | +#> |.....................| -11.52 | -0.06705 | 7.151 | -11.60 | +#> |<span style='font-weight: bold;'> 13</span>| 492.59250 | 0.9992 | -1.006 | -0.9104 | -0.9382 | +#> |.....................| -0.9874 | -0.8848 | -0.8179 | -0.8489 | +#> |.....................| -0.8617 | -0.8865 | -0.8805 | -0.8968 | +#> |.....................| -0.8378 | -0.8722 | -0.8940 | -0.8371 | +#> | U| 492.5925 | 91.41 | -5.194 | -0.8869 | -2.190 | +#> |.....................| -4.629 | 0.4582 | 0.8519 | 0.05892 | +#> |.....................| 0.8337 | 0.05784 | 0.7275 | 0.8766 | +#> |.....................| 1.221 | 0.9633 | 0.8418 | 1.253 | +#> | X|<span style='font-weight: bold;'> 492.5925</span> | 91.41 | 0.005548 | 0.2918 | 0.1119 | +#> |.....................| 0.009760 | 0.6126 | 0.8519 | 0.05892 | +#> |.....................| 0.8337 | 0.05784 | 0.7275 | 0.8766 | +#> |.....................| 1.221 | 0.9633 | 0.8418 | 1.253 | +#> | F| Forward Diff. | 33.40 | 2.217 | -0.09736 | 0.04377 | +#> |.....................| -0.3664 | 0.6618 | -21.29 | -14.62 | +#> |.....................| -5.018 | -1.838 | 0.9818 | 8.628 | +#> |.....................| -11.52 | -0.1307 | 6.857 | -11.62 | +#> |<span style='font-weight: bold;'> 14</span>| 492.30478 | 0.9905 | -1.006 | -0.9103 | -0.9382 | +#> |.....................| -0.9873 | -0.8850 | -0.8121 | -0.8449 | +#> |.....................| -0.8604 | -0.8860 | -0.8808 | -0.8991 | +#> |.....................| -0.8347 | -0.8722 | -0.8958 | -0.8339 | +#> | U| 492.30478 | 90.62 | -5.195 | -0.8868 | -2.190 | +#> |.....................| -4.629 | 0.4581 | 0.8543 | 0.05904 | +#> |.....................| 0.8343 | 0.05785 | 0.7273 | 0.8745 | +#> |.....................| 1.225 | 0.9633 | 0.8402 | 1.257 | +#> | X|<span style='font-weight: bold;'> 492.30478</span> | 90.62 | 0.005545 | 0.2918 | 0.1119 | +#> |.....................| 0.009761 | 0.6126 | 0.8543 | 0.05904 | +#> |.....................| 0.8343 | 0.05785 | 0.7273 | 0.8745 | +#> |.....................| 1.225 | 0.9633 | 0.8402 | 1.257 | +#> | F| Forward Diff. | -34.08 | 2.096 | -0.4157 | 0.1370 | +#> |.....................| -0.3212 | 0.6979 | -20.95 | -14.99 | +#> |.....................| -5.046 | -1.607 | 1.055 | 8.026 | +#> |.....................| -11.31 | 0.3535 | 6.819 | -11.49 | +#> |<span style='font-weight: bold;'> 15</span>| 492.00325 | 0.9991 | -1.007 | -0.9102 | -0.9382 | +#> |.....................| -0.9872 | -0.8852 | -0.8063 | -0.8408 | +#> |.....................| -0.8590 | -0.8856 | -0.8811 | -0.9014 | +#> |.....................| -0.8316 | -0.8723 | -0.8977 | -0.8307 | +#> | U| 492.00325 | 91.40 | -5.195 | -0.8867 | -2.190 | +#> |.....................| -4.629 | 0.4580 | 0.8567 | 0.05916 | +#> |.....................| 0.8349 | 0.05786 | 0.7271 | 0.8725 | +#> |.....................| 1.229 | 0.9632 | 0.8386 | 1.261 | +#> | X|<span style='font-weight: bold;'> 492.00325</span> | 91.40 | 0.005542 | 0.2918 | 0.1119 | +#> |.....................| 0.009762 | 0.6125 | 0.8567 | 0.05916 | +#> |.....................| 0.8349 | 0.05786 | 0.7271 | 0.8725 | +#> |.....................| 1.229 | 0.9632 | 0.8386 | 1.261 | +#> | F| Forward Diff. | 32.19 | 2.189 | -0.09620 | 0.04245 | +#> |.....................| -0.3450 | 0.6659 | -21.28 | -14.00 | +#> |.....................| -4.881 | -1.759 | 1.243 | 8.359 | +#> |.....................| -10.62 | -0.07477 | 6.614 | -11.44 | +#> |<span style='font-weight: bold;'> 16</span>| 491.72015 | 0.9906 | -1.007 | -0.9102 | -0.9382 | +#> |.....................| -0.9871 | -0.8854 | -0.8003 | -0.8368 | +#> |.....................| -0.8576 | -0.8851 | -0.8814 | -0.9037 | +#> |.....................| -0.8285 | -0.8722 | -0.8996 | -0.8275 | +#> | U| 491.72015 | 90.62 | -5.196 | -0.8867 | -2.190 | +#> |.....................| -4.629 | 0.4579 | 0.8592 | 0.05927 | +#> |.....................| 0.8354 | 0.05788 | 0.7268 | 0.8703 | +#> |.....................| 1.232 | 0.9633 | 0.8370 | 1.265 | +#> | X|<span style='font-weight: bold;'> 491.72015</span> | 90.62 | 0.005538 | 0.2918 | 0.1119 | +#> |.....................| 0.009763 | 0.6125 | 0.8592 | 0.05927 | +#> |.....................| 0.8354 | 0.05788 | 0.7268 | 0.8703 | +#> |.....................| 1.232 | 0.9633 | 0.8370 | 1.265 | +#> | F| Forward Diff. | -33.41 | 2.074 | -0.4123 | 0.1389 | +#> |.....................| -0.2981 | 0.7039 | -20.39 | -14.31 | +#> |.....................| -4.887 | -1.550 | 0.9656 | 7.818 | +#> |.....................| -11.05 | -0.4282 | 6.582 | -11.31 | +#> |<span style='font-weight: bold;'> 17</span>| 491.42294 | 0.9990 | -1.008 | -0.9101 | -0.9383 | +#> |.....................| -0.9870 | -0.8856 | -0.7943 | -0.8327 | +#> |.....................| -0.8562 | -0.8846 | -0.8817 | -0.9060 | +#> |.....................| -0.8254 | -0.8721 | -0.9015 | -0.8242 | +#> | U| 491.42294 | 91.39 | -5.197 | -0.8866 | -2.190 | +#> |.....................| -4.629 | 0.4578 | 0.8616 | 0.05939 | +#> |.....................| 0.8360 | 0.05789 | 0.7266 | 0.8683 | +#> |.....................| 1.236 | 0.9634 | 0.8354 | 1.269 | +#> | X|<span style='font-weight: bold;'> 491.42294</span> | 91.39 | 0.005535 | 0.2918 | 0.1119 | +#> |.....................| 0.009764 | 0.6125 | 0.8616 | 0.05939 | +#> |.....................| 0.8360 | 0.05789 | 0.7266 | 0.8683 | +#> |.....................| 1.236 | 0.9634 | 0.8354 | 1.269 | +#> | F| Forward Diff. | 31.50 | 2.165 | -0.08876 | 0.04676 | +#> |.....................| -0.3226 | 0.6753 | -20.70 | -13.34 | +#> |.....................| -4.747 | -1.707 | 0.9017 | 8.141 | +#> |.....................| -10.29 | -0.02981 | 6.402 | -11.28 | +#> |<span style='font-weight: bold;'> 18</span>| 491.14065 | 0.9907 | -1.009 | -0.9100 | -0.9383 | +#> |.....................| -0.9870 | -0.8858 | -0.7882 | -0.8287 | +#> |.....................| -0.8548 | -0.8841 | -0.8820 | -0.9084 | +#> |.....................| -0.8223 | -0.8721 | -0.9034 | -0.8208 | +#> | U| 491.14065 | 90.64 | -5.197 | -0.8866 | -2.190 | +#> |.....................| -4.629 | 0.4577 | 0.8642 | 0.05950 | +#> |.....................| 0.8366 | 0.05791 | 0.7264 | 0.8661 | +#> |.....................| 1.240 | 0.9634 | 0.8337 | 1.273 | +#> | X|<span style='font-weight: bold;'> 491.14065</span> | 90.64 | 0.005531 | 0.2918 | 0.1119 | +#> |.....................| 0.009765 | 0.6125 | 0.8642 | 0.05950 | +#> |.....................| 0.8366 | 0.05791 | 0.7264 | 0.8661 | +#> |.....................| 1.240 | 0.9634 | 0.8337 | 1.273 | +#> | F| Forward Diff. | -32.29 | 2.052 | -0.4043 | 0.1403 | +#> |.....................| -0.2785 | 0.7107 | -20.12 | -13.83 | +#> |.....................| -4.879 | -1.515 | 0.4622 | 7.293 | +#> |.....................| -10.82 | -0.3681 | 6.384 | -11.14 | +#> |<span style='font-weight: bold;'> 19</span>| 490.84537 | 0.9989 | -1.009 | -0.9099 | -0.9383 | +#> |.....................| -0.9869 | -0.8860 | -0.7821 | -0.8246 | +#> |.....................| -0.8533 | -0.8837 | -0.8821 | -0.9106 | +#> |.....................| -0.8190 | -0.8720 | -0.9053 | -0.8174 | +#> | U| 490.84537 | 91.38 | -5.198 | -0.8865 | -2.190 | +#> |.....................| -4.629 | 0.4576 | 0.8667 | 0.05962 | +#> |.....................| 0.8372 | 0.05792 | 0.7263 | 0.8641 | +#> |.....................| 1.243 | 0.9635 | 0.8321 | 1.277 | +#> | X|<span style='font-weight: bold;'> 490.84537</span> | 91.38 | 0.005528 | 0.2918 | 0.1119 | +#> |.....................| 0.009766 | 0.6124 | 0.8667 | 0.05962 | +#> |.....................| 0.8372 | 0.05792 | 0.7263 | 0.8641 | +#> |.....................| 1.243 | 0.9635 | 0.8321 | 1.277 | +#> | F| Forward Diff. | 30.35 | 2.134 | -0.08371 | 0.04933 | +#> |.....................| -0.3000 | 0.6785 | -20.24 | -12.73 | +#> |.....................| -4.623 | -1.604 | 1.054 | 8.092 | +#> |.....................| -10.77 | -0.4405 | 6.181 | -11.10 | +#> |<span style='font-weight: bold;'> 20</span>| 490.56963 | 0.9908 | -1.010 | -0.9099 | -0.9383 | +#> |.....................| -0.9868 | -0.8862 | -0.7758 | -0.8207 | +#> |.....................| -0.8519 | -0.8832 | -0.8824 | -0.9131 | +#> |.....................| -0.8157 | -0.8719 | -0.9072 | -0.8140 | +#> | U| 490.56963 | 90.64 | -5.199 | -0.8865 | -2.190 | +#> |.....................| -4.629 | 0.4575 | 0.8693 | 0.05974 | +#> |.....................| 0.8378 | 0.05793 | 0.7261 | 0.8619 | +#> |.....................| 1.247 | 0.9636 | 0.8305 | 1.281 | +#> | X|<span style='font-weight: bold;'> 490.56963</span> | 90.64 | 0.005524 | 0.2918 | 0.1119 | +#> |.....................| 0.009767 | 0.6124 | 0.8693 | 0.05974 | +#> |.....................| 0.8378 | 0.05793 | 0.7261 | 0.8619 | +#> |.....................| 1.247 | 0.9636 | 0.8305 | 1.281 | +#> | F| Forward Diff. | -31.85 | 2.030 | -0.4014 | 0.1424 | +#> |.....................| -0.2574 | 0.7152 | -19.39 | -13.12 | +#> |.....................| -4.602 | -1.387 | 0.5883 | 7.042 | +#> |.....................| -10.56 | -0.3115 | 6.249 | -10.92 | +#> |<span style='font-weight: bold;'> 21</span>| 490.28521 | 0.9989 | -1.011 | -0.9098 | -0.9384 | +#> |.....................| -0.9867 | -0.8865 | -0.7697 | -0.8166 | +#> |.....................| -0.8504 | -0.8827 | -0.8826 | -0.9153 | +#> |.....................| -0.8124 | -0.8718 | -0.9092 | -0.8105 | +#> | U| 490.28521 | 91.39 | -5.199 | -0.8864 | -2.190 | +#> |.....................| -4.629 | 0.4574 | 0.8718 | 0.05985 | +#> |.....................| 0.8384 | 0.05795 | 0.7259 | 0.8599 | +#> |.....................| 1.251 | 0.9637 | 0.8288 | 1.285 | +#> | X|<span style='font-weight: bold;'> 490.28521</span> | 91.39 | 0.005521 | 0.2919 | 0.1119 | +#> |.....................| 0.009767 | 0.6124 | 0.8718 | 0.05985 | +#> |.....................| 0.8384 | 0.05795 | 0.7259 | 0.8599 | +#> |.....................| 1.251 | 0.9637 | 0.8288 | 1.285 | +#> | F| Forward Diff. | 30.53 | 2.112 | -0.07114 | 0.05276 | +#> |.....................| -0.2779 | 0.6845 | -19.81 | -12.13 | +#> |.....................| -4.498 | -1.539 | 0.6449 | 7.769 | +#> |.....................| -10.55 | -0.3696 | 5.980 | -10.93 | +#> |<span style='font-weight: bold;'> 22</span>| 489.99923 | 0.9911 | -1.011 | -0.9097 | -0.9384 | +#> |.....................| -0.9866 | -0.8867 | -0.7633 | -0.8127 | +#> |.....................| -0.8489 | -0.8823 | -0.8828 | -0.9178 | +#> |.....................| -0.8089 | -0.8716 | -0.9111 | -0.8070 | +#> | U| 489.99923 | 90.67 | -5.200 | -0.8863 | -2.190 | +#> |.....................| -4.629 | 0.4573 | 0.8745 | 0.05997 | +#> |.....................| 0.8390 | 0.05796 | 0.7258 | 0.8577 | +#> |.....................| 1.255 | 0.9638 | 0.8271 | 1.290 | +#> | X|<span style='font-weight: bold;'> 489.99923</span> | 90.67 | 0.005517 | 0.2919 | 0.1119 | +#> |.....................| 0.009768 | 0.6124 | 0.8745 | 0.05997 | +#> |.....................| 0.8390 | 0.05796 | 0.7258 | 0.8577 | +#> |.....................| 1.255 | 0.9638 | 0.8271 | 1.290 | +#> | F| Forward Diff. | -29.14 | 2.012 | -0.3844 | 0.1417 | +#> |.....................| -0.2358 | 0.7218 | -18.90 | -12.37 | +#> |.....................| -4.517 | -1.329 | 0.4904 | 6.799 | +#> |.....................| -10.31 | -0.2514 | 6.013 | -10.75 | +#> |<span style='font-weight: bold;'> 23</span>| 489.73483 | 0.9991 | -1.012 | -0.9096 | -0.9384 | +#> |.....................| -0.9865 | -0.8869 | -0.7571 | -0.8087 | +#> |.....................| -0.8475 | -0.8818 | -0.8829 | -0.9201 | +#> |.....................| -0.8055 | -0.8715 | -0.9131 | -0.8034 | +#> | U| 489.73483 | 91.40 | -5.201 | -0.8862 | -2.190 | +#> |.....................| -4.629 | 0.4572 | 0.8771 | 0.06008 | +#> |.....................| 0.8396 | 0.05797 | 0.7257 | 0.8557 | +#> |.....................| 1.259 | 0.9639 | 0.8254 | 1.294 | +#> | X|<span style='font-weight: bold;'> 489.73483</span> | 91.40 | 0.005513 | 0.2919 | 0.1119 | +#> |.....................| 0.009769 | 0.6123 | 0.8771 | 0.06008 | +#> |.....................| 0.8396 | 0.05797 | 0.7257 | 0.8557 | +#> |.....................| 1.259 | 0.9639 | 0.8254 | 1.294 | +#> | F| Forward Diff. | 31.68 | 2.089 | -0.05219 | 0.05312 | +#> |.....................| -0.2568 | 0.6912 | -19.25 | -11.50 | +#> |.....................| -4.291 | -1.478 | 0.6044 | 7.316 | +#> |.....................| -10.30 | -0.3159 | 5.756 | -10.75 | +#> |<span style='font-weight: bold;'> 24</span>| 489.43925 | 0.9914 | -1.013 | -0.9096 | -0.9385 | +#> |.....................| -0.9865 | -0.8872 | -0.7505 | -0.8049 | +#> |.....................| -0.8460 | -0.8813 | -0.8831 | -0.9225 | +#> |.....................| -0.8020 | -0.8714 | -0.9150 | -0.7997 | +#> | U| 489.43925 | 90.70 | -5.201 | -0.8862 | -2.190 | +#> |.....................| -4.628 | 0.4571 | 0.8798 | 0.06019 | +#> |.....................| 0.8402 | 0.05799 | 0.7256 | 0.8535 | +#> |.....................| 1.264 | 0.9640 | 0.8238 | 1.298 | +#> | X|<span style='font-weight: bold;'> 489.43925</span> | 90.70 | 0.005509 | 0.2919 | 0.1119 | +#> |.....................| 0.009770 | 0.6123 | 0.8798 | 0.06019 | +#> |.....................| 0.8402 | 0.05799 | 0.7256 | 0.8535 | +#> |.....................| 1.264 | 0.9640 | 0.8238 | 1.298 | +#> | F| Forward Diff. | -26.48 | 1.993 | -0.3684 | 0.1403 | +#> |.....................| -0.2166 | 0.7270 | -18.36 | -11.77 | +#> |.....................| -4.393 | -1.275 | 0.4390 | 6.578 | +#> |.....................| -10.04 | -0.2187 | 5.799 | -10.58 | +#> |<span style='font-weight: bold;'> 25</span>| 489.19181 | 0.9992 | -1.013 | -0.9095 | -0.9385 | +#> |.....................| -0.9864 | -0.8874 | -0.7441 | -0.8009 | +#> |.....................| -0.8445 | -0.8809 | -0.8833 | -0.9248 | +#> |.....................| -0.7985 | -0.8714 | -0.9170 | -0.7960 | +#> | U| 489.19181 | 91.41 | -5.202 | -0.8861 | -2.190 | +#> |.....................| -4.628 | 0.4570 | 0.8824 | 0.06031 | +#> |.....................| 0.8409 | 0.05800 | 0.7255 | 0.8514 | +#> |.....................| 1.268 | 0.9641 | 0.8221 | 1.303 | +#> | X|<span style='font-weight: bold;'> 489.19181</span> | 91.41 | 0.005505 | 0.2919 | 0.1119 | +#> |.....................| 0.009770 | 0.6123 | 0.8824 | 0.06031 | +#> |.....................| 0.8409 | 0.05800 | 0.7255 | 0.8514 | +#> |.....................| 1.268 | 0.9641 | 0.8221 | 1.303 | +#> | F| Forward Diff. | 32.48 | 2.067 | -0.03453 | 0.05414 | +#> |.....................| -0.2360 | 0.6938 | -18.67 | -10.89 | +#> |.....................| -4.178 | -1.425 | 0.5548 | 7.078 | +#> |.....................| -10.01 | -0.2144 | 5.548 | -10.57 | +#> |<span style='font-weight: bold;'> 26</span>| 488.89118 | 0.9917 | -1.014 | -0.9094 | -0.9385 | +#> |.....................| -0.9863 | -0.8877 | -0.7375 | -0.7972 | +#> |.....................| -0.8430 | -0.8804 | -0.8834 | -0.9272 | +#> |.....................| -0.7949 | -0.8713 | -0.9189 | -0.7921 | +#> | U| 488.89118 | 90.73 | -5.203 | -0.8860 | -2.190 | +#> |.....................| -4.628 | 0.4568 | 0.8852 | 0.06041 | +#> |.....................| 0.8415 | 0.05801 | 0.7253 | 0.8493 | +#> |.....................| 1.272 | 0.9642 | 0.8204 | 1.308 | +#> | X|<span style='font-weight: bold;'> 488.89118</span> | 90.73 | 0.005501 | 0.2919 | 0.1119 | +#> |.....................| 0.009771 | 0.6123 | 0.8852 | 0.06041 | +#> |.....................| 0.8415 | 0.05801 | 0.7253 | 0.8493 | +#> |.....................| 1.272 | 0.9642 | 0.8204 | 1.308 | +#> | F| Forward Diff. | -24.34 | 1.974 | -0.3522 | 0.1400 | +#> |.....................| -0.1957 | 0.7323 | -17.88 | -11.06 | +#> |.....................| -4.245 | -1.195 | 0.3418 | 6.336 | +#> |.....................| -9.795 | -0.1748 | 5.588 | -10.40 | +#> |<span style='font-weight: bold;'> 27</span>| 488.65823 | 0.9993 | -1.015 | -0.9093 | -0.9386 | +#> |.....................| -0.9862 | -0.8880 | -0.7310 | -0.7933 | +#> |.....................| -0.8415 | -0.8800 | -0.8835 | -0.9295 | +#> |.....................| -0.7913 | -0.8712 | -0.9210 | -0.7883 | +#> | U| 488.65823 | 91.42 | -5.204 | -0.8859 | -2.190 | +#> |.....................| -4.628 | 0.4567 | 0.8878 | 0.06053 | +#> |.....................| 0.8421 | 0.05803 | 0.7253 | 0.8472 | +#> |.....................| 1.276 | 0.9642 | 0.8187 | 1.312 | +#> | X|<span style='font-weight: bold;'> 488.65823</span> | 91.42 | 0.005497 | 0.2919 | 0.1119 | +#> |.....................| 0.009772 | 0.6122 | 0.8878 | 0.06053 | +#> |.....................| 0.8421 | 0.05803 | 0.7253 | 0.8472 | +#> |.....................| 1.276 | 0.9642 | 0.8187 | 1.312 | +#> | F| Forward Diff. | 33.05 | 2.045 | -0.01570 | 0.05526 | +#> |.....................| -0.2154 | 0.6997 | -18.21 | -10.28 | +#> |.....................| -4.052 | -1.334 | 0.4619 | 6.811 | +#> |.....................| -9.752 | -0.1974 | 5.317 | -10.39 | +#> |<span style='font-weight: bold;'> 28</span>| 488.35451 | 0.9920 | -1.016 | -0.9093 | -0.9386 | +#> |.....................| -0.9862 | -0.8883 | -0.7243 | -0.7897 | +#> |.....................| -0.8399 | -0.8795 | -0.8836 | -0.9319 | +#> |.....................| -0.7876 | -0.8712 | -0.9229 | -0.7844 | +#> | U| 488.35451 | 90.75 | -5.204 | -0.8859 | -2.190 | +#> |.....................| -4.628 | 0.4566 | 0.8906 | 0.06063 | +#> |.....................| 0.8427 | 0.05804 | 0.7252 | 0.8450 | +#> |.....................| 1.281 | 0.9643 | 0.8170 | 1.317 | +#> | X|<span style='font-weight: bold;'> 488.35451</span> | 90.75 | 0.005493 | 0.2920 | 0.1119 | +#> |.....................| 0.009772 | 0.6122 | 0.8906 | 0.06063 | +#> |.....................| 0.8427 | 0.05804 | 0.7252 | 0.8450 | +#> |.....................| 1.281 | 0.9643 | 0.8170 | 1.317 | +#> | F| Forward Diff. | -22.42 | 1.954 | -0.3353 | 0.1391 | +#> |.....................| -0.1757 | 0.7405 | -17.32 | -10.46 | +#> |.....................| -4.053 | -1.161 | 0.2825 | 6.114 | +#> |.....................| -9.506 | -0.1281 | 5.370 | -10.21 | +#> |<span style='font-weight: bold;'> 29</span>| 488.13711 | 0.9995 | -1.016 | -0.9092 | -0.9387 | +#> |.....................| -0.9861 | -0.8886 | -0.7177 | -0.7858 | +#> |.....................| -0.8384 | -0.8791 | -0.8837 | -0.9342 | +#> |.....................| -0.7840 | -0.8711 | -0.9249 | -0.7804 | +#> | U| 488.13711 | 91.44 | -5.205 | -0.8858 | -2.190 | +#> |.....................| -4.628 | 0.4565 | 0.8934 | 0.06074 | +#> |.....................| 0.8434 | 0.05805 | 0.7251 | 0.8430 | +#> |.....................| 1.285 | 0.9643 | 0.8153 | 1.322 | +#> | X|<span style='font-weight: bold;'> 488.13711</span> | 91.44 | 0.005489 | 0.2920 | 0.1119 | +#> |.....................| 0.009773 | 0.6122 | 0.8934 | 0.06074 | +#> |.....................| 0.8434 | 0.05805 | 0.7251 | 0.8430 | +#> |.....................| 1.285 | 0.9643 | 0.8153 | 1.322 | +#> | F| Forward Diff. | 33.81 | 2.022 | 0.006720 | 0.05587 | +#> |.....................| -0.1935 | 0.7042 | -17.76 | -9.667 | +#> |.....................| -3.890 | -1.276 | 0.4404 | 6.589 | +#> |.....................| -9.459 | -0.1517 | 5.102 | -10.20 | +#> |<span style='font-weight: bold;'> 30</span>| 487.82953 | 0.9922 | -1.017 | -0.9091 | -0.9387 | +#> |.....................| -0.9861 | -0.8889 | -0.7108 | -0.7824 | +#> |.....................| -0.8369 | -0.8787 | -0.8838 | -0.9367 | +#> |.....................| -0.7803 | -0.8711 | -0.9268 | -0.7763 | +#> | U| 487.82953 | 90.77 | -5.206 | -0.8858 | -2.190 | +#> |.....................| -4.628 | 0.4563 | 0.8962 | 0.06084 | +#> |.....................| 0.8440 | 0.05806 | 0.7251 | 0.8408 | +#> |.....................| 1.289 | 0.9644 | 0.8136 | 1.327 | +#> | X|<span style='font-weight: bold;'> 487.82953</span> | 90.77 | 0.005484 | 0.2920 | 0.1119 | +#> |.....................| 0.009774 | 0.6121 | 0.8962 | 0.06084 | +#> |.....................| 0.8440 | 0.05806 | 0.7251 | 0.8408 | +#> |.....................| 1.289 | 0.9644 | 0.8136 | 1.327 | +#> | F| Forward Diff. | -20.31 | 1.935 | -0.3119 | 0.1382 | +#> |.....................| -0.1555 | 0.7438 | -16.49 | -9.852 | +#> |.....................| -3.955 | -1.103 | 0.2044 | 5.876 | +#> |.....................| -9.237 | -0.1098 | 5.167 | -10.02 | +#> |<span style='font-weight: bold;'> 31</span>| 487.63293 | 0.9997 | -1.018 | -0.9090 | -0.9388 | +#> |.....................| -0.9860 | -0.8892 | -0.7043 | -0.7786 | +#> |.....................| -0.8354 | -0.8782 | -0.8838 | -0.9390 | +#> |.....................| -0.7766 | -0.8711 | -0.9289 | -0.7723 | +#> | U| 487.63293 | 91.46 | -5.207 | -0.8857 | -2.191 | +#> |.....................| -4.628 | 0.4562 | 0.8989 | 0.06095 | +#> |.....................| 0.8446 | 0.05808 | 0.7250 | 0.8387 | +#> |.....................| 1.294 | 0.9644 | 0.8119 | 1.332 | +#> | X|<span style='font-weight: bold;'> 487.63293</span> | 91.46 | 0.005480 | 0.2920 | 0.1119 | +#> |.....................| 0.009774 | 0.6121 | 0.8989 | 0.06095 | +#> |.....................| 0.8446 | 0.05808 | 0.7250 | 0.8387 | +#> |.....................| 1.294 | 0.9644 | 0.8119 | 1.332 | +#> | F| Forward Diff. | 35.34 | 2.001 | 0.03668 | 0.05608 | +#> |.....................| -0.1731 | 0.7098 | -16.98 | -9.135 | +#> |.....................| -3.742 | -1.209 | 0.3780 | 6.351 | +#> |.....................| -9.183 | 0.6525 | 4.885 | -10.01 | +#> |<span style='font-weight: bold;'> 32</span>| 487.31820 | 0.9926 | -1.019 | -0.9090 | -0.9388 | +#> |.....................| -0.9860 | -0.8895 | -0.6975 | -0.7753 | +#> |.....................| -0.8338 | -0.8778 | -0.8838 | -0.9414 | +#> |.....................| -0.7728 | -0.8714 | -0.9308 | -0.7679 | +#> | U| 487.3182 | 90.81 | -5.208 | -0.8856 | -2.191 | +#> |.....................| -4.628 | 0.4560 | 0.9017 | 0.06104 | +#> |.....................| 0.8453 | 0.05809 | 0.7250 | 0.8366 | +#> |.....................| 1.298 | 0.9641 | 0.8102 | 1.337 | +#> | X|<span style='font-weight: bold;'> 487.3182</span> | 90.81 | 0.005475 | 0.2920 | 0.1119 | +#> |.....................| 0.009775 | 0.6121 | 0.9017 | 0.06104 | +#> |.....................| 0.8453 | 0.05809 | 0.7250 | 0.8366 | +#> |.....................| 1.298 | 0.9641 | 0.8102 | 1.337 | +#> | F| Forward Diff. | -17.75 | 1.917 | -0.2852 | 0.1361 | +#> |.....................| -0.1360 | 0.7493 | -16.63 | -9.386 | +#> |.....................| -3.766 | -1.006 | 0.1674 | 5.665 | +#> |.....................| -8.945 | 0.7251 | 4.960 | -9.828 | +#> |<span style='font-weight: bold;'> 33</span>| 487.13531 | 0.9998 | -1.020 | -0.9089 | -0.9389 | +#> |.....................| -0.9859 | -0.8898 | -0.6907 | -0.7715 | +#> |.....................| -0.8323 | -0.8774 | -0.8839 | -0.9437 | +#> |.....................| -0.7691 | -0.8717 | -0.9328 | -0.7639 | +#> | U| 487.13531 | 91.47 | -5.208 | -0.8855 | -2.191 | +#> |.....................| -4.628 | 0.4559 | 0.9045 | 0.06116 | +#> |.....................| 0.8459 | 0.05810 | 0.7250 | 0.8345 | +#> |.....................| 1.303 | 0.9638 | 0.8084 | 1.342 | +#> | X|<span style='font-weight: bold;'> 487.13531</span> | 91.47 | 0.005471 | 0.2920 | 0.1118 | +#> |.....................| 0.009775 | 0.6120 | 0.9045 | 0.06116 | +#> |.....................| 0.8459 | 0.05810 | 0.7250 | 0.8345 | +#> |.....................| 1.303 | 0.9638 | 0.8084 | 1.342 | +#> | F| Forward Diff. | 35.92 | 1.979 | 0.06301 | 0.05698 | +#> |.....................| -0.1526 | 0.7131 | -16.77 | -8.520 | +#> |.....................| -3.634 | -1.163 | 0.3177 | 6.099 | +#> |.....................| -8.917 | 0.6421 | 4.685 | -9.820 | +#> |<span style='font-weight: bold;'> 34</span>| 486.82694 | 0.9926 | -1.021 | -0.9088 | -0.9389 | +#> |.....................| -0.9859 | -0.8902 | -0.6837 | -0.7686 | +#> |.....................| -0.8308 | -0.8770 | -0.8839 | -0.9460 | +#> |.....................| -0.7654 | -0.8723 | -0.9347 | -0.7596 | +#> | U| 486.82694 | 90.81 | -5.209 | -0.8855 | -2.191 | +#> |.....................| -4.628 | 0.4557 | 0.9074 | 0.06124 | +#> |.....................| 0.8465 | 0.05811 | 0.7250 | 0.8324 | +#> |.....................| 1.307 | 0.9632 | 0.8069 | 1.347 | +#> | X|<span style='font-weight: bold;'> 486.82694</span> | 90.81 | 0.005466 | 0.2920 | 0.1118 | +#> |.....................| 0.009775 | 0.6120 | 0.9074 | 0.06124 | +#> |.....................| 0.8465 | 0.05811 | 0.7250 | 0.8324 | +#> |.....................| 1.307 | 0.9632 | 0.8069 | 1.347 | +#> | F| Forward Diff. | -17.49 | 1.895 | -0.2726 | 0.1382 | +#> |.....................| -0.1159 | 0.7566 | -16.14 | -8.833 | +#> |.....................| -3.638 | -0.9303 | 0.1285 | 5.442 | +#> |.....................| -8.630 | 0.7091 | 4.774 | -9.639 | +#> |<span style='font-weight: bold;'> 35</span>| 486.64804 | 0.9998 | -1.021 | -0.9087 | -0.9390 | +#> |.....................| -0.9858 | -0.8905 | -0.6768 | -0.7649 | +#> |.....................| -0.8293 | -0.8767 | -0.8839 | -0.9483 | +#> |.....................| -0.7617 | -0.8727 | -0.9367 | -0.7554 | +#> | U| 486.64804 | 91.46 | -5.210 | -0.8854 | -2.191 | +#> |.....................| -4.628 | 0.4556 | 0.9103 | 0.06135 | +#> |.....................| 0.8472 | 0.05812 | 0.7250 | 0.8304 | +#> |.....................| 1.311 | 0.9629 | 0.8051 | 1.352 | +#> | X|<span style='font-weight: bold;'> 486.64804</span> | 91.46 | 0.005462 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6120 | 0.9103 | 0.06135 | +#> |.....................| 0.8472 | 0.05812 | 0.7250 | 0.8304 | +#> |.....................| 1.311 | 0.9629 | 0.8051 | 1.352 | +#> | F| Forward Diff. | 35.26 | 1.955 | 0.07649 | 0.05940 | +#> |.....................| -0.1319 | 0.7217 | -16.38 | -8.030 | +#> |.....................| -3.491 | -1.078 | 0.2504 | 5.851 | +#> |.....................| -8.624 | 0.5993 | 4.494 | -9.625 | +#> |<span style='font-weight: bold;'> 36</span>| 486.34524 | 0.9928 | -1.022 | -0.9087 | -0.9390 | +#> |.....................| -0.9858 | -0.8909 | -0.6696 | -0.7621 | +#> |.....................| -0.8278 | -0.8763 | -0.8838 | -0.9506 | +#> |.....................| -0.7579 | -0.8733 | -0.9385 | -0.7509 | +#> | U| 486.34524 | 90.82 | -5.211 | -0.8854 | -2.191 | +#> |.....................| -4.628 | 0.4554 | 0.9133 | 0.06143 | +#> |.....................| 0.8478 | 0.05813 | 0.7251 | 0.8283 | +#> |.....................| 1.316 | 0.9622 | 0.8036 | 1.358 | +#> | X|<span style='font-weight: bold;'> 486.34524</span> | 90.82 | 0.005456 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6119 | 0.9133 | 0.06143 | +#> |.....................| 0.8478 | 0.05813 | 0.7251 | 0.8283 | +#> |.....................| 1.316 | 0.9622 | 0.8036 | 1.358 | +#> | F| Forward Diff. | -16.53 | 1.875 | -0.2661 | 0.1390 | +#> |.....................| -0.09763 | 0.7654 | -15.70 | -8.237 | +#> |.....................| -3.491 | -0.9040 | 0.06392 | 5.213 | +#> |.....................| -8.361 | 0.6621 | 4.584 | -9.445 | +#> |<span style='font-weight: bold;'> 37</span>| 486.17476 | 0.9998 | -1.023 | -0.9086 | -0.9391 | +#> |.....................| -0.9858 | -0.8913 | -0.6626 | -0.7586 | +#> |.....................| -0.8262 | -0.8759 | -0.8838 | -0.9529 | +#> |.....................| -0.7542 | -0.8736 | -0.9406 | -0.7467 | +#> | U| 486.17476 | 91.47 | -5.212 | -0.8853 | -2.191 | +#> |.....................| -4.628 | 0.4552 | 0.9162 | 0.06153 | +#> |.....................| 0.8484 | 0.05814 | 0.7250 | 0.8263 | +#> |.....................| 1.320 | 0.9619 | 0.8018 | 1.363 | +#> | X|<span style='font-weight: bold;'> 486.17476</span> | 91.47 | 0.005452 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6119 | 0.9162 | 0.06153 | +#> |.....................| 0.8484 | 0.05814 | 0.7250 | 0.8263 | +#> |.....................| 1.320 | 0.9619 | 0.8018 | 1.363 | +#> | F| Forward Diff. | 35.23 | 1.932 | 0.08715 | 0.05955 | +#> |.....................| -0.1122 | 0.7274 | -16.01 | -7.627 | +#> |.....................| -3.363 | -1.024 | 0.1942 | 5.616 | +#> |.....................| -8.345 | 0.5641 | 4.322 | -9.424 | +#> |<span style='font-weight: bold;'> 38</span>| 485.87468 | 0.9930 | -1.024 | -0.9086 | -0.9392 | +#> |.....................| -0.9858 | -0.8917 | -0.6553 | -0.7561 | +#> |.....................| -0.8248 | -0.8756 | -0.8837 | -0.9551 | +#> |.....................| -0.7504 | -0.8743 | -0.9424 | -0.7420 | +#> | U| 485.87468 | 90.84 | -5.213 | -0.8853 | -2.191 | +#> |.....................| -4.628 | 0.4550 | 0.9192 | 0.06160 | +#> |.....................| 0.8490 | 0.05815 | 0.7252 | 0.8243 | +#> |.....................| 1.325 | 0.9613 | 0.8003 | 1.369 | +#> | X|<span style='font-weight: bold;'> 485.87468</span> | 90.84 | 0.005446 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6118 | 0.9192 | 0.06160 | +#> |.....................| 0.8490 | 0.05815 | 0.7252 | 0.8243 | +#> |.....................| 1.325 | 0.9613 | 0.8003 | 1.369 | +#> | F| Forward Diff. | -15.16 | 1.855 | -0.2494 | 0.1393 | +#> |.....................| -0.07811 | 0.7704 | -15.31 | -7.716 | +#> |.....................| -3.357 | -0.8175 | -0.03012 | 4.971 | +#> |.....................| -8.100 | 0.5955 | 4.407 | -9.242 | +#> |<span style='font-weight: bold;'> 39</span>| 485.71812 | 1.000 | -1.025 | -0.9085 | -0.9392 | +#> |.....................| -0.9858 | -0.8921 | -0.6482 | -0.7526 | +#> |.....................| -0.8232 | -0.8752 | -0.8836 | -0.9573 | +#> |.....................| -0.7467 | -0.8746 | -0.9444 | -0.7377 | +#> | U| 485.71812 | 91.48 | -5.214 | -0.8852 | -2.191 | +#> |.....................| -4.628 | 0.4548 | 0.9221 | 0.06170 | +#> |.....................| 0.8497 | 0.05816 | 0.7252 | 0.8222 | +#> |.....................| 1.329 | 0.9610 | 0.7985 | 1.374 | +#> | X|<span style='font-weight: bold;'> 485.71812</span> | 91.48 | 0.005442 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6118 | 0.9221 | 0.06170 | +#> |.....................| 0.8497 | 0.05816 | 0.7252 | 0.8222 | +#> |.....................| 1.329 | 0.9610 | 0.7985 | 1.374 | +#> | F| Forward Diff. | 36.02 | 1.911 | 0.1144 | 0.05926 | +#> |.....................| -0.09370 | 0.7314 | -15.47 | -7.071 | +#> |.....................| -3.248 | -0.9743 | 0.1265 | 5.377 | +#> |.....................| -7.775 | 0.5175 | 4.130 | -9.229 | +#> |<span style='font-weight: bold;'> 40</span>| 485.42108 | 0.9931 | -1.026 | -0.9085 | -0.9393 | +#> |.....................| -0.9858 | -0.8926 | -0.6408 | -0.7505 | +#> |.....................| -0.8218 | -0.8750 | -0.8834 | -0.9594 | +#> |.....................| -0.7430 | -0.8752 | -0.9461 | -0.7328 | +#> | U| 485.42108 | 90.85 | -5.215 | -0.8852 | -2.191 | +#> |.....................| -4.628 | 0.4546 | 0.9252 | 0.06176 | +#> |.....................| 0.8503 | 0.05817 | 0.7254 | 0.8204 | +#> |.....................| 1.333 | 0.9604 | 0.7970 | 1.380 | +#> | X|<span style='font-weight: bold;'> 485.42108</span> | 90.85 | 0.005436 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6117 | 0.9252 | 0.06176 | +#> |.....................| 0.8503 | 0.05817 | 0.7254 | 0.8204 | +#> |.....................| 1.333 | 0.9604 | 0.7970 | 1.380 | +#> | F| Forward Diff. | -14.37 | 1.836 | -0.2333 | 0.1389 | +#> |.....................| -0.05951 | 0.7785 | -14.33 | -7.292 | +#> |.....................| -3.229 | -0.7699 | -0.05471 | 4.764 | +#> |.....................| -7.801 | 0.5597 | 4.229 | -9.048 | +#> |<span style='font-weight: bold;'> 41</span>| 485.26815 | 0.9999 | -1.027 | -0.9084 | -0.9394 | +#> |.....................| -0.9858 | -0.8930 | -0.6338 | -0.7470 | +#> |.....................| -0.8202 | -0.8746 | -0.8833 | -0.9618 | +#> |.....................| -0.7392 | -0.8755 | -0.9482 | -0.7284 | +#> | U| 485.26815 | 91.48 | -5.216 | -0.8851 | -2.191 | +#> |.....................| -4.628 | 0.4544 | 0.9281 | 0.06186 | +#> |.....................| 0.8509 | 0.05818 | 0.7254 | 0.8183 | +#> |.....................| 1.338 | 0.9601 | 0.7953 | 1.385 | +#> | X|<span style='font-weight: bold;'> 485.26815</span> | 91.48 | 0.005431 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6117 | 0.9281 | 0.06186 | +#> |.....................| 0.8509 | 0.05818 | 0.7254 | 0.8183 | +#> |.....................| 1.338 | 0.9601 | 0.7953 | 1.385 | +#> | F| Forward Diff. | 35.37 | 1.889 | 0.1323 | 0.06297 | +#> |.....................| -0.07437 | 0.7390 | -14.80 | -6.641 | +#> |.....................| -3.116 | -0.8690 | 0.09880 | 5.162 | +#> |.....................| -7.761 | 0.4865 | 3.967 | -9.019 | +#> |<span style='font-weight: bold;'> 42</span>| 484.97448 | 0.9934 | -1.028 | -0.9084 | -0.9395 | +#> |.....................| -0.9859 | -0.8935 | -0.6264 | -0.7452 | +#> |.....................| -0.8188 | -0.8744 | -0.8830 | -0.9639 | +#> |.....................| -0.7352 | -0.8762 | -0.9500 | -0.7231 | +#> | U| 484.97448 | 90.88 | -5.217 | -0.8851 | -2.191 | +#> |.....................| -4.628 | 0.4542 | 0.9311 | 0.06191 | +#> |.....................| 0.8515 | 0.05819 | 0.7257 | 0.8164 | +#> |.....................| 1.343 | 0.9594 | 0.7937 | 1.392 | +#> | X|<span style='font-weight: bold;'> 484.97448</span> | 90.88 | 0.005424 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6116 | 0.9311 | 0.06191 | +#> |.....................| 0.8515 | 0.05819 | 0.7257 | 0.8164 | +#> |.....................| 1.343 | 0.9594 | 0.7937 | 1.392 | +#> | F| Forward Diff. | -12.51 | 1.817 | -0.2072 | 0.1320 | +#> |.....................| -0.04147 | 0.7868 | -13.90 | -6.839 | +#> |.....................| -3.097 | -0.6966 | -0.09701 | 4.567 | +#> |.....................| -7.500 | 0.5336 | 4.059 | -8.839 | +#> |<span style='font-weight: bold;'> 43</span>| 484.82513 | 0.9998 | -1.029 | -0.9083 | -0.9395 | +#> |.....................| -0.9858 | -0.8939 | -0.6193 | -0.7417 | +#> |.....................| -0.8172 | -0.8741 | -0.8829 | -0.9662 | +#> |.....................| -0.7313 | -0.8765 | -0.9521 | -0.7185 | +#> | U| 484.82513 | 91.47 | -5.218 | -0.8851 | -2.191 | +#> |.....................| -4.628 | 0.4540 | 0.9341 | 0.06202 | +#> |.....................| 0.8522 | 0.05820 | 0.7257 | 0.8143 | +#> |.....................| 1.347 | 0.9592 | 0.7919 | 1.397 | +#> | X|<span style='font-weight: bold;'> 484.82513</span> | 91.47 | 0.005419 | 0.2921 | 0.1118 | +#> |.....................| 0.009776 | 0.6116 | 0.9341 | 0.06202 | +#> |.....................| 0.8522 | 0.05820 | 0.7257 | 0.8143 | +#> |.....................| 1.347 | 0.9592 | 0.7919 | 1.397 | +#> | F| Forward Diff. | 34.86 | 1.871 | 0.1566 | 0.07097 | +#> |.....................| -0.05046 | 0.7508 | -14.35 | -6.106 | +#> |.....................| -2.960 | -0.8322 | 0.03576 | 4.926 | +#> |.....................| -7.463 | 0.4624 | 3.813 | -8.806 | +#> |<span style='font-weight: bold;'> 44</span>| 484.54032 | 0.9935 | -1.030 | -0.9084 | -0.9396 | +#> |.....................| -0.9859 | -0.8946 | -0.6118 | -0.7403 | +#> |.....................| -0.8157 | -0.8739 | -0.8825 | -0.9682 | +#> |.....................| -0.7274 | -0.8772 | -0.9538 | -0.7130 | +#> | U| 484.54032 | 90.89 | -5.219 | -0.8851 | -2.191 | +#> |.....................| -4.628 | 0.4537 | 0.9372 | 0.06206 | +#> |.....................| 0.8528 | 0.05820 | 0.7260 | 0.8125 | +#> |.....................| 1.352 | 0.9585 | 0.7904 | 1.404 | +#> | X|<span style='font-weight: bold;'> 484.54032</span> | 90.89 | 0.005412 | 0.2921 | 0.1118 | +#> |.....................| 0.009775 | 0.6115 | 0.9372 | 0.06206 | +#> |.....................| 0.8528 | 0.05820 | 0.7260 | 0.8125 | +#> |.....................| 1.352 | 0.9585 | 0.7904 | 1.404 | +#> | F| Forward Diff. | -11.88 | 1.798 | -0.1931 | 0.1288 | +#> |.....................| -0.02100 | 0.7941 | -13.56 | -6.327 | +#> |.....................| -2.985 | -0.6346 | -0.1369 | 4.355 | +#> |.....................| -7.207 | 0.4876 | 3.910 | -8.603 | +#> |<span style='font-weight: bold;'> 45</span>| 484.39828 | 0.9999 | -1.031 | -0.9082 | -0.9397 | +#> |.....................| -0.9859 | -0.8950 | -0.6045 | -0.7369 | +#> |.....................| -0.8141 | -0.8736 | -0.8824 | -0.9706 | +#> |.....................| -0.7235 | -0.8774 | -0.9559 | -0.7084 | +#> | U| 484.39828 | 91.47 | -5.220 | -0.8850 | -2.191 | +#> |.....................| -4.628 | 0.4535 | 0.9402 | 0.06215 | +#> |.....................| 0.8534 | 0.05821 | 0.7261 | 0.8104 | +#> |.....................| 1.357 | 0.9582 | 0.7886 | 1.409 | +#> | X|<span style='font-weight: bold;'> 484.39828</span> | 91.47 | 0.005407 | 0.2921 | 0.1118 | +#> |.....................| 0.009775 | 0.6115 | 0.9402 | 0.06215 | +#> |.....................| 0.8534 | 0.05821 | 0.7261 | 0.8104 | +#> |.....................| 1.357 | 0.9582 | 0.7886 | 1.409 | +#> | F| Forward Diff. | 34.75 | 1.847 | 0.1787 | 0.06647 | +#> |.....................| -0.03069 | 0.7556 | -13.39 | -5.638 | +#> |.....................| -2.842 | -0.7351 | -0.07352 | 4.648 | +#> |.....................| -7.153 | 0.4383 | 3.662 | -8.575 | +#> |<span style='font-weight: bold;'> 46</span>| 484.12389 | 0.9935 | -1.033 | -0.9083 | -0.9398 | +#> |.....................| -0.9861 | -0.8957 | -0.5972 | -0.7360 | +#> |.....................| -0.8127 | -0.8736 | -0.8818 | -0.9724 | +#> |.....................| -0.7196 | -0.8781 | -0.9577 | -0.7026 | +#> | U| 484.12389 | 90.89 | -5.221 | -0.8851 | -2.192 | +#> |.....................| -4.628 | 0.4532 | 0.9432 | 0.06218 | +#> |.....................| 0.8540 | 0.05821 | 0.7265 | 0.8087 | +#> |.....................| 1.361 | 0.9576 | 0.7871 | 1.416 | +#> | X|<span style='font-weight: bold;'> 484.12389</span> | 90.89 | 0.005400 | 0.2921 | 0.1117 | +#> |.....................| 0.009773 | 0.6114 | 0.9432 | 0.06218 | +#> |.....................| 0.8540 | 0.05821 | 0.7265 | 0.8087 | +#> |.....................| 1.361 | 0.9576 | 0.7871 | 1.416 | +#> | F| Forward Diff. | -12.23 | 1.776 | -0.1772 | 0.1286 | +#> |.....................| -0.003904 | 0.8005 | -13.23 | -5.967 | +#> |.....................| -2.801 | -0.5825 | -0.1993 | 4.126 | +#> |.....................| -6.930 | 0.4309 | 3.746 | -8.373 | +#> |<span style='font-weight: bold;'> 47</span>| 483.96910 | 0.9995 | -1.034 | -0.9082 | -0.9399 | +#> |.....................| -0.9861 | -0.8963 | -0.5897 | -0.7331 | +#> |.....................| -0.8111 | -0.8733 | -0.8815 | -0.9747 | +#> |.....................| -0.7157 | -0.8785 | -0.9598 | -0.6976 | +#> | U| 483.9691 | 91.44 | -5.222 | -0.8850 | -2.192 | +#> |.....................| -4.628 | 0.4529 | 0.9464 | 0.06226 | +#> |.....................| 0.8547 | 0.05822 | 0.7267 | 0.8067 | +#> |.....................| 1.366 | 0.9573 | 0.7854 | 1.423 | +#> | X|<span style='font-weight: bold;'> 483.9691</span> | 91.44 | 0.005394 | 0.2921 | 0.1117 | +#> |.....................| 0.009773 | 0.6113 | 0.9464 | 0.06226 | +#> |.....................| 0.8547 | 0.05822 | 0.7267 | 0.8067 | +#> |.....................| 1.366 | 0.9573 | 0.7854 | 1.423 | +#> | F| Forward Diff. | 31.42 | 1.822 | 0.1778 | 0.07033 | +#> |.....................| -0.01094 | 0.7681 | -13.66 | -5.343 | +#> |.....................| -2.704 | -0.6601 | -0.05834 | 4.483 | +#> |.....................| -6.846 | 0.3977 | 3.514 | -8.343 | +#> |<span style='font-weight: bold;'> 48</span>| 483.71026 | 0.9937 | -1.035 | -0.9084 | -0.9400 | +#> |.....................| -0.9863 | -0.8970 | -0.5817 | -0.7327 | +#> |.....................| -0.8099 | -0.8734 | -0.8808 | -0.9764 | +#> |.....................| -0.7120 | -0.8790 | -0.9614 | -0.6918 | +#> | U| 483.71026 | 90.90 | -5.224 | -0.8851 | -2.192 | +#> |.....................| -4.628 | 0.4526 | 0.9497 | 0.06228 | +#> |.....................| 0.8552 | 0.05822 | 0.7272 | 0.8052 | +#> |.....................| 1.370 | 0.9567 | 0.7840 | 1.430 | +#> | X|<span style='font-weight: bold;'> 483.71026</span> | 90.90 | 0.005386 | 0.2921 | 0.1117 | +#> |.....................| 0.009771 | 0.6112 | 0.9497 | 0.06228 | +#> |.....................| 0.8552 | 0.05822 | 0.7272 | 0.8052 | +#> |.....................| 1.370 | 0.9567 | 0.7840 | 1.430 | +#> | F| Forward Diff. | -11.41 | 1.753 | -0.1608 | 0.1222 | +#> |.....................| 0.01159 | 0.8050 | -10.44 | -3.810 | +#> |.....................| -1.727 | 0.1311 | 2.133 | 3.863 | +#> |.....................| -5.017 | 1.937 | 3.587 | -8.159 | +#> |<span style='font-weight: bold;'> 49</span>| 483.59835 | 1.000 | -1.037 | -0.9083 | -0.9401 | +#> |.....................| -0.9863 | -0.8977 | -0.5748 | -0.7309 | +#> |.....................| -0.8089 | -0.8737 | -0.8826 | -0.9789 | +#> |.....................| -0.7088 | -0.8807 | -0.9637 | -0.6861 | +#> | U| 483.59835 | 91.50 | -5.225 | -0.8850 | -2.192 | +#> |.....................| -4.628 | 0.4523 | 0.9525 | 0.06233 | +#> |.....................| 0.8556 | 0.05821 | 0.7260 | 0.8029 | +#> |.....................| 1.374 | 0.9551 | 0.7819 | 1.437 | +#> | X|<span style='font-weight: bold;'> 483.59835</span> | 91.50 | 0.005379 | 0.2921 | 0.1117 | +#> |.....................| 0.009771 | 0.6112 | 0.9525 | 0.06233 | +#> |.....................| 0.8556 | 0.05821 | 0.7260 | 0.8029 | +#> |.....................| 1.374 | 0.9551 | 0.7819 | 1.437 | +#> | F| Forward Diff. | 35.70 | 1.806 | 0.2381 | 0.06477 | +#> |.....................| 0.008951 | 0.7715 | -12.71 | -4.946 | +#> |.....................| -2.552 | -0.6506 | -0.07612 | 4.309 | +#> |.....................| -6.609 | 0.2622 | 3.318 | -8.104 | +#> |<span style='font-weight: bold;'> 50</span>| 483.34903 | 0.9946 | -1.038 | -0.9084 | -0.9402 | +#> |.....................| -0.9865 | -0.8986 | -0.5687 | -0.7321 | +#> |.....................| -0.8087 | -0.8746 | -0.8853 | -0.9811 | +#> |.....................| -0.7064 | -0.8834 | -0.9659 | -0.6790 | +#> | U| 483.34903 | 90.99 | -5.227 | -0.8851 | -2.192 | +#> |.....................| -4.629 | 0.4518 | 0.9551 | 0.06229 | +#> |.....................| 0.8557 | 0.05818 | 0.7240 | 0.8009 | +#> |.....................| 1.377 | 0.9526 | 0.7800 | 1.445 | +#> | X|<span style='font-weight: bold;'> 483.34903</span> | 90.99 | 0.005370 | 0.2921 | 0.1117 | +#> |.....................| 0.009769 | 0.6111 | 0.9551 | 0.06229 | +#> |.....................| 0.8557 | 0.05818 | 0.7240 | 0.8009 | +#> |.....................| 1.377 | 0.9526 | 0.7800 | 1.445 | +#> | F| Forward Diff. | -5.120 | 1.736 | -0.09503 | 0.1090 | +#> |.....................| 0.03046 | 0.8092 | -12.63 | -5.226 | +#> |.....................| -2.620 | -0.5304 | -0.3057 | 3.753 | +#> |.....................| -6.427 | 0.07650 | 3.398 | -7.915 | +#> |<span style='font-weight: bold;'> 51</span>| 483.15597 | 0.9980 | -1.040 | -0.9083 | -0.9402 | +#> |.....................| -0.9866 | -0.8991 | -0.5603 | -0.7286 | +#> |.....................| -0.8069 | -0.8742 | -0.8851 | -0.9836 | +#> |.....................| -0.7022 | -0.8834 | -0.9682 | -0.6737 | +#> | U| 483.15597 | 91.30 | -5.228 | -0.8851 | -2.192 | +#> |.....................| -4.629 | 0.4516 | 0.9585 | 0.06239 | +#> |.....................| 0.8564 | 0.05819 | 0.7241 | 0.7987 | +#> |.....................| 1.382 | 0.9525 | 0.7781 | 1.452 | +#> | X|<span style='font-weight: bold;'> 483.15597</span> | 91.30 | 0.005364 | 0.2921 | 0.1117 | +#> |.....................| 0.009769 | 0.6110 | 0.9585 | 0.06239 | +#> |.....................| 0.8564 | 0.05819 | 0.7241 | 0.7987 | +#> |.....................| 1.382 | 0.9525 | 0.7781 | 1.452 | +#> |<span style='font-weight: bold;'> 52</span>| 483.02721 | 1.004 | -1.042 | -0.9082 | -0.9404 | +#> |.....................| -0.9866 | -0.9001 | -0.5449 | -0.7222 | +#> |.....................| -0.8037 | -0.8736 | -0.8847 | -0.9882 | +#> |.....................| -0.6943 | -0.8835 | -0.9723 | -0.6641 | +#> | U| 483.02721 | 91.87 | -5.230 | -0.8850 | -2.192 | +#> |.....................| -4.629 | 0.4511 | 0.9649 | 0.06258 | +#> |.....................| 0.8577 | 0.05821 | 0.7244 | 0.7946 | +#> |.....................| 1.391 | 0.9524 | 0.7746 | 1.463 | +#> | X|<span style='font-weight: bold;'> 483.02721</span> | 91.87 | 0.005352 | 0.2921 | 0.1117 | +#> |.....................| 0.009768 | 0.6109 | 0.9649 | 0.06258 | +#> |.....................| 0.8577 | 0.05821 | 0.7244 | 0.7946 | +#> |.....................| 1.391 | 0.9524 | 0.7746 | 1.463 | +#> | F| Forward Diff. | 64.04 | 1.793 | 0.5284 | 0.01389 | +#> |.....................| 0.02898 | 0.7509 | -12.63 | -3.976 | +#> |.....................| -2.339 | -0.6213 | 0.1061 | 4.124 | +#> |.....................| -6.092 | 0.06517 | 2.880 | -7.726 | +#> |<span style='font-weight: bold;'> 53</span>| 482.23689 | 0.9946 | -1.047 | -0.9090 | -0.9407 | +#> |.....................| -0.9878 | -0.9036 | -0.5201 | -0.7284 | +#> |.....................| -0.8010 | -0.8752 | -0.8830 | -0.9901 | +#> |.....................| -0.6858 | -0.8831 | -0.9756 | -0.6451 | +#> | U| 482.23689 | 90.99 | -5.236 | -0.8857 | -2.192 | +#> |.....................| -4.630 | 0.4496 | 0.9752 | 0.06240 | +#> |.....................| 0.8589 | 0.05816 | 0.7257 | 0.7929 | +#> |.....................| 1.401 | 0.9528 | 0.7717 | 1.486 | +#> | X|<span style='font-weight: bold;'> 482.23689</span> | 90.99 | 0.005323 | 0.2920 | 0.1116 | +#> |.....................| 0.009757 | 0.6105 | 0.9752 | 0.06240 | +#> |.....................| 0.8589 | 0.05816 | 0.7257 | 0.7929 | +#> |.....................| 1.401 | 0.9528 | 0.7717 | 1.486 | +#> | F| Forward Diff. | -6.401 | 1.688 | -0.06693 | 0.1101 | +#> |.....................| 0.07752 | 0.8485 | -12.38 | -4.258 | +#> |.....................| -2.381 | -0.3971 | -0.4532 | 3.327 | +#> |.....................| -5.692 | 0.09795 | 3.049 | -7.221 | +#> |<span style='font-weight: bold;'> 54</span>| 481.84664 | 1.002 | -1.052 | -0.9094 | -0.9410 | +#> |.....................| -0.9885 | -0.9064 | -0.4925 | -0.7287 | +#> |.....................| -0.7974 | -0.8758 | -0.8811 | -0.9941 | +#> |.....................| -0.6765 | -0.8831 | -0.9802 | -0.6288 | +#> | U| 481.84664 | 91.67 | -5.240 | -0.8860 | -2.193 | +#> |.....................| -4.631 | 0.4482 | 0.9866 | 0.06239 | +#> |.....................| 0.8604 | 0.05815 | 0.7270 | 0.7893 | +#> |.....................| 1.412 | 0.9528 | 0.7678 | 1.506 | +#> | X|<span style='font-weight: bold;'> 481.84664</span> | 91.67 | 0.005298 | 0.2919 | 0.1116 | +#> |.....................| 0.009749 | 0.6102 | 0.9866 | 0.06239 | +#> |.....................| 0.8604 | 0.05815 | 0.7270 | 0.7893 | +#> |.....................| 1.412 | 0.9528 | 0.7678 | 1.506 | +#> | F| Forward Diff. | 47.13 | 1.726 | 0.4206 | 0.02536 | +#> |.....................| 0.06828 | 0.8062 | -11.83 | -3.346 | +#> |.....................| -2.102 | -0.4847 | -0.09759 | 3.731 | +#> |.....................| -5.096 | -0.5769 | 2.736 | -6.997 | +#> |<span style='font-weight: bold;'> 55</span>| 481.27209 | 0.9943 | -1.058 | -0.9105 | -0.9413 | +#> |.....................| -0.9900 | -0.9106 | -0.4653 | -0.7394 | +#> |.....................| -0.7957 | -0.8780 | -0.8782 | -0.9956 | +#> |.....................| -0.6736 | -0.8789 | -0.9829 | -0.6135 | +#> | U| 481.27209 | 90.96 | -5.246 | -0.8870 | -2.193 | +#> |.....................| -4.632 | 0.4464 | 0.9978 | 0.06208 | +#> |.....................| 0.8611 | 0.05808 | 0.7292 | 0.7879 | +#> |.....................| 1.416 | 0.9569 | 0.7655 | 1.525 | +#> | X|<span style='font-weight: bold;'> 481.27209</span> | 90.96 | 0.005268 | 0.2917 | 0.1116 | +#> |.....................| 0.009735 | 0.6098 | 0.9978 | 0.06208 | +#> |.....................| 0.8611 | 0.05808 | 0.7292 | 0.7879 | +#> |.....................| 1.416 | 0.9569 | 0.7655 | 1.525 | +#> | F| Forward Diff. | -10.35 | 1.643 | -0.1028 | 0.1091 | +#> |.....................| 0.1039 | 0.8949 | -11.59 | -3.607 | +#> |.....................| -2.172 | -0.3207 | -0.4703 | 3.042 | +#> |.....................| -5.188 | 0.5388 | 2.890 | -6.602 | +#> |<span style='font-weight: bold;'> 56</span>| 480.86800 | 0.9992 | -1.064 | -0.9113 | -0.9415 | +#> |.....................| -0.9915 | -0.9152 | -0.4371 | -0.7498 | +#> |.....................| -0.7937 | -0.8800 | -0.8752 | -0.9980 | +#> |.....................| -0.6700 | -0.8785 | -0.9867 | -0.5989 | +#> | U| 480.868 | 91.41 | -5.252 | -0.8877 | -2.193 | +#> |.....................| -4.634 | 0.4442 | 1.010 | 0.06178 | +#> |.....................| 0.8619 | 0.05803 | 0.7313 | 0.7858 | +#> |.....................| 1.420 | 0.9572 | 0.7622 | 1.543 | +#> | X|<span style='font-weight: bold;'> 480.868</span> | 91.41 | 0.005236 | 0.2916 | 0.1115 | +#> |.....................| 0.009720 | 0.6093 | 1.010 | 0.06178 | +#> |.....................| 0.8619 | 0.05803 | 0.7313 | 0.7858 | +#> |.....................| 1.420 | 0.9572 | 0.7622 | 1.543 | +#> |<span style='font-weight: bold;'> 57</span>| 480.18757 | 0.9994 | -1.075 | -0.9131 | -0.9420 | +#> |.....................| -0.9946 | -0.9242 | -0.3882 | -0.7756 | +#> |.....................| -0.7917 | -0.8845 | -0.8694 | -1.000 | +#> |.....................| -0.6674 | -0.8772 | -0.9919 | -0.5742 | +#> | U| 480.18757 | 91.43 | -5.264 | -0.8893 | -2.194 | +#> |.....................| -4.637 | 0.4401 | 1.030 | 0.06104 | +#> |.....................| 0.8627 | 0.05790 | 0.7356 | 0.7839 | +#> |.....................| 1.423 | 0.9585 | 0.7577 | 1.573 | +#> | X|<span style='font-weight: bold;'> 480.18757</span> | 91.43 | 0.005177 | 0.2913 | 0.1115 | +#> |.....................| 0.009690 | 0.6083 | 1.030 | 0.06104 | +#> |.....................| 0.8627 | 0.05790 | 0.7356 | 0.7839 | +#> |.....................| 1.423 | 0.9585 | 0.7577 | 1.573 | +#> |<span style='font-weight: bold;'> 58</span>| 477.33677 | 1.000 | -1.128 | -0.9215 | -0.9444 | +#> |.....................| -1.009 | -0.9662 | -0.1601 | -0.8958 | +#> |.....................| -0.7824 | -0.9055 | -0.8420 | -1.010 | +#> |.....................| -0.6551 | -0.8713 | -1.016 | -0.4591 | +#> | U| 477.33677 | 91.51 | -5.317 | -0.8967 | -2.196 | +#> |.....................| -4.651 | 0.4208 | 1.124 | 0.05757 | +#> |.....................| 0.8666 | 0.05729 | 0.7556 | 0.7749 | +#> |.....................| 1.438 | 0.9642 | 0.7367 | 1.713 | +#> | X|<span style='font-weight: bold;'> 477.33677</span> | 91.51 | 0.004910 | 0.2897 | 0.1112 | +#> |.....................| 0.009550 | 0.6037 | 1.124 | 0.05757 | +#> |.....................| 0.8666 | 0.05729 | 0.7556 | 0.7749 | +#> |.....................| 1.438 | 0.9642 | 0.7367 | 1.713 | +#> |<span style='font-weight: bold;'> 59</span>| 470.34077 | 1.005 | -1.340 | -0.9551 | -0.9536 | +#> |.....................| -1.067 | -1.134 | 0.7520 | -1.376 | +#> |.....................| -0.7448 | -0.9894 | -0.7326 | -1.050 | +#> |.....................| -0.6055 | -0.8475 | -1.115 | 0.001078 | +#> | U| 470.34077 | 91.93 | -5.528 | -0.9265 | -2.205 | +#> |.....................| -4.709 | 0.3439 | 1.502 | 0.04372 | +#> |.....................| 0.8821 | 0.05487 | 0.8354 | 0.7391 | +#> |.....................| 1.496 | 0.9871 | 0.6524 | 2.272 | +#> | X|<span style='font-weight: bold;'> 470.34077</span> | 91.93 | 0.003973 | 0.2836 | 0.1102 | +#> |.....................| 0.009011 | 0.5851 | 1.502 | 0.04372 | +#> |.....................| 0.8821 | 0.05487 | 0.8354 | 0.7391 | +#> |.....................| 1.496 | 0.9871 | 0.6524 | 2.272 | +#> | F| Forward Diff. | 26.15 | 0.9841 | -0.2917 | -0.5557 | +#> |.....................| 0.1743 | 0.07961 | -5.483 | -2.977 | +#> |.....................| -1.594 | -1.883 | 1.921 | 2.622 | +#> |.....................| -2.684 | 3.199 | -3.516 | -0.2713 | +#> |<span style='font-weight: bold;'> 60</span>| 503.34963 | 1.001 | -1.624 | -0.8890 | -0.8555 | +#> |.....................| -1.160 | -1.269 | 1.871 | -1.579 | +#> |.....................| -0.5570 | -0.7566 | -0.9888 | -1.205 | +#> |.....................| -0.4219 | -1.204 | -0.3205 | 0.003684 | +#> | U| 503.34963 | 91.54 | -5.813 | -0.8679 | -2.107 | +#> |.....................| -4.802 | 0.2817 | 1.965 | 0.03787 | +#> |.....................| 0.9599 | 0.06159 | 0.6484 | 0.5998 | +#> |.....................| 1.714 | 0.6438 | 1.334 | 2.275 | +#> | X|<span style='font-weight: bold;'> 503.34963</span> | 91.54 | 0.002989 | 0.2957 | 0.1216 | +#> |.....................| 0.008214 | 0.5700 | 1.965 | 0.03787 | +#> |.....................| 0.9599 | 0.06159 | 0.6484 | 0.5998 | +#> |.....................| 1.714 | 0.6438 | 1.334 | 2.275 | +#> |<span style='font-weight: bold;'> 61</span>| 469.52776 | 1.001 | -1.377 | -0.9480 | -0.9425 | +#> |.....................| -1.079 | -1.153 | 0.9014 | -1.405 | +#> |.....................| -0.7213 | -0.9635 | -0.7590 | -1.066 | +#> |.....................| -0.5863 | -0.9260 | -1.020 | 0.002305 | +#> | U| 469.52776 | 91.55 | -5.565 | -0.9203 | -2.194 | +#> |.....................| -4.721 | 0.3353 | 1.564 | 0.04288 | +#> |.....................| 0.8919 | 0.05562 | 0.8161 | 0.7248 | +#> |.....................| 1.519 | 0.9115 | 0.7335 | 2.274 | +#> | X|<span style='font-weight: bold;'> 469.52776</span> | 91.55 | 0.003829 | 0.2849 | 0.1114 | +#> |.....................| 0.008907 | 0.5831 | 1.564 | 0.04288 | +#> |.....................| 0.8919 | 0.05562 | 0.8161 | 0.7248 | +#> |.....................| 1.519 | 0.9115 | 0.7335 | 2.274 | +#> | F| Forward Diff. | -33.46 | 0.8466 | -0.2714 | -0.3437 | +#> |.....................| -0.005169 | 0.9674 | -4.363 | -2.175 | +#> |.....................| -0.4723 | -1.194 | 1.668 | 1.180 | +#> |.....................| -1.975 | -3.231 | 4.715 | 0.6860 | +#> |<span style='font-weight: bold;'> 62</span>| 468.69396 | 1.009 | -1.417 | -0.9407 | -0.9181 | +#> |.....................| -1.088 | -1.184 | 1.029 | -1.410 | +#> |.....................| -0.7106 | -0.9016 | -0.8502 | -1.110 | +#> |.....................| -0.5641 | -0.8957 | -1.025 | -0.08379 | +#> | U| 468.69396 | 92.28 | -5.606 | -0.9138 | -2.170 | +#> |.....................| -4.730 | 0.3207 | 1.617 | 0.04273 | +#> |.....................| 0.8963 | 0.05740 | 0.7496 | 0.6857 | +#> |.....................| 1.546 | 0.9407 | 0.7298 | 2.169 | +#> | X|<span style='font-weight: bold;'> 468.69396</span> | 92.28 | 0.003677 | 0.2862 | 0.1142 | +#> |.....................| 0.008826 | 0.5795 | 1.617 | 0.04273 | +#> |.....................| 0.8963 | 0.05740 | 0.7496 | 0.6857 | +#> |.....................| 1.546 | 0.9407 | 0.7298 | 2.169 | +#> | F| Forward Diff. | 44.64 | 0.7919 | 0.8591 | -0.3536 | +#> |.....................| -0.1337 | 0.2061 | -3.251 | 1.076 | +#> |.....................| 0.6486 | -0.6734 | -0.006662 | -4.031 | +#> |.....................| -0.9510 | -1.369 | 2.636 | 0.2207 | +#> |<span style='font-weight: bold;'> 63</span>| 468.25975 | 1.001 | -1.457 | -0.9435 | -0.8944 | +#> |.....................| -1.092 | -1.207 | 1.162 | -1.430 | +#> |.....................| -0.7163 | -0.8453 | -0.9089 | -1.031 | +#> |.....................| -0.5350 | -0.9084 | -1.055 | -0.1705 | +#> | U| 468.25975 | 91.62 | -5.645 | -0.9163 | -2.146 | +#> |.....................| -4.734 | 0.3104 | 1.671 | 0.04217 | +#> |.....................| 0.8939 | 0.05903 | 0.7067 | 0.7562 | +#> |.....................| 1.580 | 0.9284 | 0.7040 | 2.064 | +#> | X|<span style='font-weight: bold;'> 468.25975</span> | 91.62 | 0.003534 | 0.2857 | 0.1169 | +#> |.....................| 0.008791 | 0.5770 | 1.671 | 0.04217 | +#> |.....................| 0.8939 | 0.05903 | 0.7067 | 0.7562 | +#> |.....................| 1.580 | 0.9284 | 0.7040 | 2.064 | +#> | F| Forward Diff. | -27.10 | 0.6132 | -0.09159 | -0.08800 | +#> |.....................| -0.1078 | -0.3202 | -2.388 | 1.638 | +#> |.....................| 1.140 | 0.1171 | 0.1600 | 3.377 | +#> |.....................| 1.163 | -2.226 | -0.6898 | -0.6683 | +#> |<span style='font-weight: bold;'> 64</span>| 467.71969 | 1.007 | -1.501 | -0.9546 | -0.8725 | +#> |.....................| -1.088 | -1.196 | 1.309 | -1.518 | +#> |.....................| -0.7729 | -0.8084 | -0.9408 | -1.028 | +#> |.....................| -0.5596 | -0.8715 | -1.022 | -0.2167 | +#> | U| 467.71969 | 92.14 | -5.690 | -0.9262 | -2.124 | +#> |.....................| -4.730 | 0.3152 | 1.732 | 0.03962 | +#> |.....................| 0.8705 | 0.06009 | 0.6835 | 0.7588 | +#> |.....................| 1.551 | 0.9640 | 0.7321 | 2.007 | +#> | X|<span style='font-weight: bold;'> 467.71969</span> | 92.14 | 0.003381 | 0.2837 | 0.1195 | +#> |.....................| 0.008831 | 0.5781 | 1.732 | 0.03962 | +#> |.....................| 0.8705 | 0.06009 | 0.6835 | 0.7588 | +#> |.....................| 1.551 | 0.9640 | 0.7321 | 2.007 | +#> | F| Forward Diff. | 13.64 | 0.5263 | -0.09449 | -0.03300 | +#> |.....................| -0.2497 | 0.5177 | -1.944 | 1.719 | +#> |.....................| 0.02781 | -0.4546 | 0.1053 | 4.139 | +#> |.....................| 0.2369 | 0.8861 | 1.752 | -0.4404 | +#> |<span style='font-weight: bold;'> 65</span>| 467.30536 | 1.004 | -1.542 | -0.9574 | -0.8551 | +#> |.....................| -1.078 | -1.202 | 1.437 | -1.633 | +#> |.....................| -0.8162 | -0.7674 | -0.9588 | -1.081 | +#> |.....................| -0.5907 | -0.8860 | -1.037 | -0.2723 | +#> | U| 467.30536 | 91.87 | -5.731 | -0.9286 | -2.107 | +#> |.....................| -4.720 | 0.3124 | 1.785 | 0.03631 | +#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7116 | +#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.30536</span> | 91.87 | 0.003244 | 0.2832 | 0.1216 | +#> |.....................| 0.008917 | 0.5775 | 1.785 | 0.03631 | +#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7116 | +#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | +#> | F| Forward Diff. | -28.84 | 0.5077 | -0.1377 | 0.05990 | +#> |.....................| -0.2272 | 0.7424 | -2.070 | -0.4026 | +#> |.....................| -0.6342 | -0.6074 | -0.7367 | -1.927 | +#> |.....................| -1.174 | -0.4282 | -0.2913 | -0.8226 | +#> |<span style='font-weight: bold;'> 66</span>| 467.70919 | 1.018 | -1.590 | -0.9528 | -0.8478 | +#> |.....................| -1.050 | -1.273 | 1.541 | -1.746 | +#> |.....................| -0.7981 | -0.6862 | -0.9431 | -1.082 | +#> |.....................| -0.5846 | -0.9179 | -1.062 | -0.3171 | +#> | U| 467.70919 | 93.14 | -5.778 | -0.9245 | -2.100 | +#> |.....................| -4.692 | 0.2799 | 1.829 | 0.03305 | +#> |.....................| 0.8601 | 0.06362 | 0.6818 | 0.7103 | +#> |.....................| 1.521 | 0.9193 | 0.6977 | 1.885 | +#> | X|<span style='font-weight: bold;'> 467.70919</span> | 93.14 | 0.003094 | 0.2840 | 0.1225 | +#> |.....................| 0.009168 | 0.5695 | 1.829 | 0.03305 | +#> |.....................| 0.8601 | 0.06362 | 0.6818 | 0.7103 | +#> |.....................| 1.521 | 0.9193 | 0.6977 | 1.885 | +#> |<span style='font-weight: bold;'> 67</span>| 467.47896 | 1.015 | -1.557 | -0.9559 | -0.8529 | +#> |.....................| -1.069 | -1.224 | 1.469 | -1.667 | +#> |.....................| -0.8105 | -0.7423 | -0.9538 | -1.081 | +#> |.....................| -0.5885 | -0.8957 | -1.045 | -0.2858 | +#> | U| 467.47896 | 92.90 | -5.746 | -0.9273 | -2.105 | +#> |.....................| -4.711 | 0.3023 | 1.799 | 0.03531 | +#> |.....................| 0.8550 | 0.06200 | 0.6740 | 0.7117 | +#> |.....................| 1.517 | 0.9407 | 0.7123 | 1.923 | +#> | X|<span style='font-weight: bold;'> 467.47896</span> | 92.90 | 0.003197 | 0.2835 | 0.1219 | +#> |.....................| 0.008994 | 0.5750 | 1.799 | 0.03531 | +#> |.....................| 0.8550 | 0.06200 | 0.6740 | 0.7117 | +#> |.....................| 1.517 | 0.9407 | 0.7123 | 1.923 | +#> |<span style='font-weight: bold;'> 68</span>| 467.47242 | 1.015 | -1.547 | -0.9569 | -0.8545 | +#> |.....................| -1.075 | -1.209 | 1.447 | -1.644 | +#> |.....................| -0.8142 | -0.7594 | -0.9570 | -1.080 | +#> |.....................| -0.5898 | -0.8890 | -1.040 | -0.2763 | +#> | U| 467.47242 | 92.83 | -5.736 | -0.9282 | -2.106 | +#> |.....................| -4.717 | 0.3092 | 1.790 | 0.03600 | +#> |.....................| 0.8534 | 0.06150 | 0.6716 | 0.7121 | +#> |.....................| 1.515 | 0.9472 | 0.7168 | 1.935 | +#> | X|<span style='font-weight: bold;'> 467.47242</span> | 92.83 | 0.003229 | 0.2833 | 0.1217 | +#> |.....................| 0.008942 | 0.5767 | 1.790 | 0.03600 | +#> |.....................| 0.8534 | 0.06150 | 0.6716 | 0.7121 | +#> |.....................| 1.515 | 0.9472 | 0.7168 | 1.935 | +#> |<span style='font-weight: bold;'> 69</span>| 467.34503 | 1.012 | -1.542 | -0.9574 | -0.8552 | +#> |.....................| -1.078 | -1.203 | 1.437 | -1.633 | +#> |.....................| -0.8160 | -0.7673 | -0.9586 | -1.080 | +#> |.....................| -0.5904 | -0.8859 | -1.037 | -0.2720 | +#> | U| 467.34503 | 92.56 | -5.731 | -0.9286 | -2.107 | +#> |.....................| -4.720 | 0.3123 | 1.786 | 0.03631 | +#> |.....................| 0.8527 | 0.06128 | 0.6705 | 0.7121 | +#> |.....................| 1.514 | 0.9501 | 0.7188 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.34503</span> | 92.56 | 0.003244 | 0.2832 | 0.1216 | +#> |.....................| 0.008918 | 0.5775 | 1.786 | 0.03631 | +#> |.....................| 0.8527 | 0.06128 | 0.6705 | 0.7121 | +#> |.....................| 1.514 | 0.9501 | 0.7188 | 1.940 | +#> |<span style='font-weight: bold;'> 70</span>| 467.25859 | 1.007 | -1.542 | -0.9574 | -0.8552 | +#> |.....................| -1.078 | -1.202 | 1.437 | -1.633 | +#> |.....................| -0.8161 | -0.7674 | -0.9587 | -1.080 | +#> |.....................| -0.5906 | -0.8860 | -1.037 | -0.2722 | +#> | U| 467.25859 | 92.16 | -5.731 | -0.9286 | -2.107 | +#> |.....................| -4.720 | 0.3124 | 1.785 | 0.03631 | +#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7118 | +#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.25859</span> | 92.16 | 0.003244 | 0.2832 | 0.1216 | +#> |.....................| 0.008918 | 0.5775 | 1.785 | 0.03631 | +#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7118 | +#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | +#> | F| Forward Diff. | 0.4422 | 0.5213 | 0.04284 | 0.02840 | +#> |.....................| -0.2383 | 0.7531 | -2.043 | -0.07081 | +#> |.....................| -0.6548 | -0.6872 | -0.7073 | -1.773 | +#> |.....................| -1.488 | -0.4400 | -0.3907 | -0.8156 | +#> |<span style='font-weight: bold;'> 71</span>| 467.25330 | 1.007 | -1.543 | -0.9574 | -0.8552 | +#> |.....................| -1.078 | -1.203 | 1.439 | -1.633 | +#> |.....................| -0.8155 | -0.7668 | -0.9581 | -1.079 | +#> |.....................| -0.5893 | -0.8856 | -1.037 | -0.2714 | +#> | U| 467.2533 | 92.12 | -5.731 | -0.9287 | -2.107 | +#> |.....................| -4.720 | 0.3121 | 1.786 | 0.03631 | +#> |.....................| 0.8529 | 0.06129 | 0.6709 | 0.7133 | +#> |.....................| 1.516 | 0.9504 | 0.7190 | 1.941 | +#> | X|<span style='font-weight: bold;'> 467.2533</span> | 92.12 | 0.003243 | 0.2832 | 0.1216 | +#> |.....................| 0.008919 | 0.5774 | 1.786 | 0.03631 | +#> |.....................| 0.8529 | 0.06129 | 0.6709 | 0.7133 | +#> |.....................| 1.516 | 0.9504 | 0.7190 | 1.941 | +#> | F| Forward Diff. | -3.065 | 0.5175 | 0.01752 | 0.03302 | +#> |.....................| -0.2370 | 0.7457 | -1.985 | -0.01476 | +#> |.....................| -0.5869 | -0.6438 | -0.7222 | -1.672 | +#> |.....................| -1.086 | -0.3942 | -0.3461 | -0.8075 | +#> |<span style='font-weight: bold;'> 72</span>| 467.24583 | 1.008 | -1.544 | -0.9571 | -0.8551 | +#> |.....................| -1.077 | -1.206 | 1.442 | -1.635 | +#> |.....................| -0.8142 | -0.7642 | -0.9569 | -1.078 | +#> |.....................| -0.5901 | -0.8857 | -1.037 | -0.2715 | +#> | U| 467.24583 | 92.22 | -5.733 | -0.9284 | -2.107 | +#> |.....................| -4.719 | 0.3108 | 1.788 | 0.03626 | +#> |.....................| 0.8534 | 0.06137 | 0.6718 | 0.7144 | +#> |.....................| 1.515 | 0.9503 | 0.7191 | 1.941 | +#> | X|<span style='font-weight: bold;'> 467.24583</span> | 92.22 | 0.003238 | 0.2832 | 0.1216 | +#> |.....................| 0.008927 | 0.5771 | 1.788 | 0.03626 | +#> |.....................| 0.8534 | 0.06137 | 0.6718 | 0.7144 | +#> |.....................| 1.515 | 0.9503 | 0.7191 | 1.941 | +#> | F| Forward Diff. | 6.834 | 0.5162 | 0.08982 | 0.01752 | +#> |.....................| -0.2436 | 0.7158 | -2.020 | -0.04939 | +#> |.....................| -0.5459 | -0.6263 | -0.5712 | -1.499 | +#> |.....................| -1.429 | -0.4150 | -0.4098 | -0.8001 | +#> |<span style='font-weight: bold;'> 73</span>| 467.23713 | 1.007 | -1.546 | -0.9569 | -0.8551 | +#> |.....................| -1.076 | -1.209 | 1.446 | -1.636 | +#> |.....................| -0.8132 | -0.7618 | -0.9559 | -1.076 | +#> |.....................| -0.5919 | -0.8860 | -1.037 | -0.2716 | +#> | U| 467.23713 | 92.12 | -5.734 | -0.9282 | -2.107 | +#> |.....................| -4.718 | 0.3095 | 1.789 | 0.03621 | +#> |.....................| 0.8538 | 0.06143 | 0.6724 | 0.7154 | +#> |.....................| 1.513 | 0.9500 | 0.7191 | 1.941 | +#> | X|<span style='font-weight: bold;'> 467.23713</span> | 92.12 | 0.003233 | 0.2833 | 0.1216 | +#> |.....................| 0.008936 | 0.5768 | 1.789 | 0.03621 | +#> |.....................| 0.8538 | 0.06143 | 0.6724 | 0.7154 | +#> |.....................| 1.513 | 0.9500 | 0.7191 | 1.941 | +#> | F| Forward Diff. | -3.249 | 0.5067 | 0.04417 | 0.02698 | +#> |.....................| -0.2393 | 0.6753 | -1.942 | -0.1419 | +#> |.....................| -0.5001 | -0.5983 | -0.6679 | -1.518 | +#> |.....................| -1.576 | -0.4506 | -0.4091 | -0.8075 | +#> |<span style='font-weight: bold;'> 74</span>| 467.22826 | 1.008 | -1.548 | -0.9568 | -0.8550 | +#> |.....................| -1.074 | -1.212 | 1.450 | -1.638 | +#> |.....................| -0.8127 | -0.7593 | -0.9548 | -1.076 | +#> |.....................| -0.5925 | -0.8862 | -1.037 | -0.2718 | +#> | U| 467.22826 | 92.20 | -5.736 | -0.9281 | -2.107 | +#> |.....................| -4.716 | 0.3080 | 1.791 | 0.03615 | +#> |.....................| 0.8540 | 0.06151 | 0.6733 | 0.7160 | +#> |.....................| 1.512 | 0.9499 | 0.7192 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.22826</span> | 92.20 | 0.003227 | 0.2833 | 0.1216 | +#> |.....................| 0.008947 | 0.5764 | 1.791 | 0.03615 | +#> |.....................| 0.8540 | 0.06151 | 0.6733 | 0.7160 | +#> |.....................| 1.512 | 0.9499 | 0.7192 | 1.940 | +#> | F| Forward Diff. | 4.158 | 0.5052 | 0.09162 | 0.01474 | +#> |.....................| -0.2441 | 0.6411 | -1.927 | 0.008374 | +#> |.....................| -0.4204 | -0.5681 | -0.5325 | -1.398 | +#> |.....................| -1.545 | -0.4616 | -0.4623 | -0.8062 | +#> |<span style='font-weight: bold;'> 75</span>| 467.21798 | 1.007 | -1.549 | -0.9567 | -0.8549 | +#> |.....................| -1.073 | -1.215 | 1.453 | -1.641 | +#> |.....................| -0.8130 | -0.7568 | -0.9541 | -1.075 | +#> |.....................| -0.5920 | -0.8862 | -1.036 | -0.2722 | +#> | U| 467.21798 | 92.13 | -5.738 | -0.9280 | -2.107 | +#> |.....................| -4.715 | 0.3065 | 1.792 | 0.03607 | +#> |.....................| 0.8539 | 0.06158 | 0.6738 | 0.7163 | +#> |.....................| 1.512 | 0.9498 | 0.7195 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.21798</span> | 92.13 | 0.003221 | 0.2833 | 0.1216 | +#> |.....................| 0.008959 | 0.5760 | 1.792 | 0.03607 | +#> |.....................| 0.8539 | 0.06158 | 0.6738 | 0.7163 | +#> |.....................| 1.512 | 0.9498 | 0.7195 | 1.940 | +#> | F| Forward Diff. | -2.820 | 0.4989 | 0.05960 | 0.01935 | +#> |.....................| -0.2421 | 0.6061 | -1.914 | -0.2151 | +#> |.....................| -0.5103 | -0.6093 | -0.7625 | -1.437 | +#> |.....................| -1.510 | -0.4672 | -0.4489 | -0.8043 | +#> |<span style='font-weight: bold;'> 76</span>| 467.20848 | 1.008 | -1.551 | -0.9569 | -0.8547 | +#> |.....................| -1.072 | -1.218 | 1.456 | -1.643 | +#> |.....................| -0.8130 | -0.7539 | -0.9520 | -1.075 | +#> |.....................| -0.5920 | -0.8859 | -1.036 | -0.2725 | +#> | U| 467.20848 | 92.20 | -5.740 | -0.9282 | -2.106 | +#> |.....................| -4.714 | 0.3053 | 1.793 | 0.03601 | +#> |.....................| 0.8539 | 0.06166 | 0.6753 | 0.7165 | +#> |.....................| 1.512 | 0.9501 | 0.7200 | 1.939 | +#> | X|<span style='font-weight: bold;'> 467.20848</span> | 92.20 | 0.003215 | 0.2833 | 0.1217 | +#> |.....................| 0.008973 | 0.5757 | 1.793 | 0.03601 | +#> |.....................| 0.8539 | 0.06166 | 0.6753 | 0.7165 | +#> |.....................| 1.512 | 0.9501 | 0.7200 | 1.939 | +#> | F| Forward Diff. | 3.706 | 0.4993 | 0.1020 | 0.01046 | +#> |.....................| -0.2448 | 0.5847 | -1.899 | -0.1702 | +#> |.....................| -0.3837 | -0.5516 | -0.5275 | -1.370 | +#> |.....................| -1.509 | -0.4527 | -0.4630 | -0.7991 | +#> |<span style='font-weight: bold;'> 77</span>| 467.20140 | 1.007 | -1.554 | -0.9572 | -0.8545 | +#> |.....................| -1.070 | -1.221 | 1.459 | -1.644 | +#> |.....................| -0.8137 | -0.7511 | -0.9495 | -1.075 | +#> |.....................| -0.5926 | -0.8856 | -1.035 | -0.2726 | +#> | U| 467.2014 | 92.12 | -5.742 | -0.9285 | -2.106 | +#> |.....................| -4.712 | 0.3041 | 1.795 | 0.03600 | +#> |.....................| 0.8536 | 0.06174 | 0.6772 | 0.7169 | +#> |.....................| 1.512 | 0.9504 | 0.7205 | 1.939 | +#> | X|<span style='font-weight: bold;'> 467.2014</span> | 92.12 | 0.003207 | 0.2832 | 0.1217 | +#> |.....................| 0.008990 | 0.5754 | 1.795 | 0.03600 | +#> |.....................| 0.8536 | 0.06174 | 0.6772 | 0.7169 | +#> |.....................| 1.512 | 0.9504 | 0.7205 | 1.939 | +#> | F| Forward Diff. | -4.697 | 0.4875 | 0.03394 | 0.01314 | +#> |.....................| -0.2450 | 0.5527 | -1.903 | -0.2230 | +#> |.....................| -0.3367 | -0.5055 | -0.4386 | -1.334 | +#> |.....................| -1.570 | -0.4518 | -0.4312 | -0.7987 | +#> |<span style='font-weight: bold;'> 78</span>| 467.19155 | 1.008 | -1.556 | -0.9574 | -0.8545 | +#> |.....................| -1.067 | -1.224 | 1.462 | -1.645 | +#> |.....................| -0.8159 | -0.7492 | -0.9499 | -1.074 | +#> |.....................| -0.5924 | -0.8858 | -1.035 | -0.2722 | +#> | U| 467.19155 | 92.18 | -5.745 | -0.9286 | -2.106 | +#> |.....................| -4.709 | 0.3027 | 1.796 | 0.03596 | +#> |.....................| 0.8527 | 0.06180 | 0.6768 | 0.7173 | +#> |.....................| 1.512 | 0.9502 | 0.7208 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.19155</span> | 92.18 | 0.003200 | 0.2832 | 0.1217 | +#> |.....................| 0.009010 | 0.5751 | 1.796 | 0.03596 | +#> |.....................| 0.8527 | 0.06180 | 0.6768 | 0.7173 | +#> |.....................| 1.512 | 0.9502 | 0.7208 | 1.940 | +#> | F| Forward Diff. | 2.102 | 0.4867 | 0.07498 | 0.004893 | +#> |.....................| -0.2442 | 0.5250 | -1.879 | -0.1740 | +#> |.....................| -0.3775 | -0.5383 | -0.4109 | -1.255 | +#> |.....................| -1.562 | -0.4584 | -0.4426 | -0.7882 | +#> |<span style='font-weight: bold;'> 79</span>| 467.18237 | 1.007 | -1.558 | -0.9574 | -0.8544 | +#> |.....................| -1.065 | -1.226 | 1.465 | -1.647 | +#> |.....................| -0.8177 | -0.7470 | -0.9510 | -1.074 | +#> |.....................| -0.5912 | -0.8859 | -1.035 | -0.2717 | +#> | U| 467.18237 | 92.12 | -5.747 | -0.9286 | -2.106 | +#> |.....................| -4.707 | 0.3016 | 1.797 | 0.03591 | +#> |.....................| 0.8519 | 0.06186 | 0.6761 | 0.7177 | +#> |.....................| 1.513 | 0.9501 | 0.7212 | 1.940 | +#> | X|<span style='font-weight: bold;'> 467.18237</span> | 92.12 | 0.003193 | 0.2832 | 0.1217 | +#> |.....................| 0.009031 | 0.5748 | 1.797 | 0.03591 | +#> |.....................| 0.8519 | 0.06186 | 0.6761 | 0.7177 | +#> |.....................| 1.513 | 0.9501 | 0.7212 | 1.940 | +#> | F| Forward Diff. | -4.940 | 0.4761 | 0.03110 | 0.006161 | +#> |.....................| -0.2415 | 0.4988 | -1.880 | -0.2651 | +#> |.....................| -0.3787 | -0.5263 | -0.4799 | -1.241 | +#> |.....................| -1.481 | -0.4641 | -0.4124 | -0.7761 | +#> |<span style='font-weight: bold;'> 80</span>| 467.17113 | 1.008 | -1.561 | -0.9574 | -0.8542 | +#> |.....................| -1.062 | -1.228 | 1.469 | -1.648 | +#> |.....................| -0.8192 | -0.7442 | -0.9515 | -1.074 | +#> |.....................| -0.5909 | -0.8858 | -1.034 | -0.2714 | +#> | U| 467.17113 | 92.19 | -5.749 | -0.9286 | -2.106 | +#> |.....................| -4.704 | 0.3008 | 1.799 | 0.03586 | +#> |.....................| 0.8513 | 0.06194 | 0.6757 | 0.7179 | +#> |.....................| 1.514 | 0.9502 | 0.7215 | 1.941 | +#> | X|<span style='font-weight: bold;'> 467.17113</span> | 92.19 | 0.003185 | 0.2832 | 0.1217 | +#> |.....................| 0.009056 | 0.5746 | 1.799 | 0.03586 | +#> |.....................| 0.8513 | 0.06194 | 0.6757 | 0.7179 | +#> |.....................| 1.514 | 0.9502 | 0.7215 | 1.941 | +#> |<span style='font-weight: bold;'> 81</span>| 467.15723 | 1.008 | -1.564 | -0.9575 | -0.8538 | +#> |.....................| -1.058 | -1.230 | 1.473 | -1.651 | +#> |.....................| -0.8215 | -0.7400 | -0.9524 | -1.074 | +#> |.....................| -0.5906 | -0.8857 | -1.034 | -0.2712 | +#> | U| 467.15723 | 92.19 | -5.753 | -0.9287 | -2.106 | +#> |.....................| -4.700 | 0.2996 | 1.800 | 0.03578 | +#> |.....................| 0.8504 | 0.06206 | 0.6750 | 0.7179 | +#> |.....................| 1.514 | 0.9503 | 0.7219 | 1.941 | +#> | X|<span style='font-weight: bold;'> 467.15723</span> | 92.19 | 0.003173 | 0.2832 | 0.1218 | +#> |.....................| 0.009093 | 0.5743 | 1.800 | 0.03578 | +#> |.....................| 0.8504 | 0.06206 | 0.6750 | 0.7179 | +#> |.....................| 1.514 | 0.9503 | 0.7219 | 1.941 | +#> |<span style='font-weight: bold;'> 82</span>| 467.09153 | 1.008 | -1.583 | -0.9578 | -0.8521 | +#> |.....................| -1.038 | -1.244 | 1.497 | -1.664 | +#> |.....................| -0.8331 | -0.7187 | -0.9572 | -1.074 | +#> |.....................| -0.5894 | -0.8854 | -1.031 | -0.2699 | +#> | U| 467.09153 | 92.20 | -5.772 | -0.9290 | -2.104 | +#> |.....................| -4.680 | 0.2934 | 1.810 | 0.03540 | +#> |.....................| 0.8456 | 0.06268 | 0.6715 | 0.7181 | +#> |.....................| 1.516 | 0.9506 | 0.7239 | 1.943 | +#> | X|<span style='font-weight: bold;'> 467.09153</span> | 92.20 | 0.003114 | 0.2831 | 0.1220 | +#> |.....................| 0.009282 | 0.5728 | 1.810 | 0.03540 | +#> |.....................| 0.8456 | 0.06268 | 0.6715 | 0.7181 | +#> |.....................| 1.516 | 0.9506 | 0.7239 | 1.943 | +#> |<span style='font-weight: bold;'> 83</span>| 466.89701 | 1.009 | -1.658 | -0.9591 | -0.8451 | +#> |.....................| -0.9556 | -1.297 | 1.590 | -1.717 | +#> |.....................| -0.8794 | -0.6338 | -0.9760 | -1.073 | +#> |.....................| -0.5844 | -0.8840 | -1.022 | -0.2647 | +#> | U| 466.89701 | 92.27 | -5.846 | -0.9301 | -2.097 | +#> |.....................| -4.598 | 0.2688 | 1.849 | 0.03388 | +#> |.....................| 0.8264 | 0.06513 | 0.6578 | 0.7186 | +#> |.....................| 1.521 | 0.9519 | 0.7320 | 1.949 | +#> | X|<span style='font-weight: bold;'> 466.89701</span> | 92.27 | 0.002890 | 0.2829 | 0.1228 | +#> |.....................| 0.01008 | 0.5668 | 1.849 | 0.03388 | +#> |.....................| 0.8264 | 0.06513 | 0.6578 | 0.7186 | +#> |.....................| 1.521 | 0.9519 | 0.7320 | 1.949 | +#> |<span style='font-weight: bold;'> 84</span>| 466.81525 | 1.010 | -1.758 | -0.9608 | -0.8357 | +#> |.....................| -0.8455 | -1.369 | 1.715 | -1.787 | +#> |.....................| -0.9414 | -0.5201 | -1.001 | -1.072 | +#> |.....................| -0.5775 | -0.8822 | -1.009 | -0.2576 | +#> | U| 466.81525 | 92.41 | -5.946 | -0.9316 | -2.087 | +#> |.....................| -4.488 | 0.2358 | 1.901 | 0.03185 | +#> |.....................| 0.8007 | 0.06841 | 0.6394 | 0.7195 | +#> |.....................| 1.530 | 0.9537 | 0.7428 | 1.958 | +#> | X|<span style='font-weight: bold;'> 466.81525</span> | 92.41 | 0.002615 | 0.2826 | 0.1240 | +#> |.....................| 0.01125 | 0.5587 | 1.901 | 0.03185 | +#> |.....................| 0.8007 | 0.06841 | 0.6394 | 0.7195 | +#> |.....................| 1.530 | 0.9537 | 0.7428 | 1.958 | +#> | F| Forward Diff. | 1.005 | 0.03859 | 0.3281 | -0.1495 | +#> |.....................| 0.1126 | -0.4190 | -0.9638 | -1.159 | +#> |.....................| -0.4187 | -0.1084 | -1.236 | 1.865 | +#> |.....................| -0.3960 | -0.4043 | -0.1671 | 0.1635 | +#> |<span style='font-weight: bold;'> 85</span>| 467.22945 | 1.009 | -1.931 | -1.059 | -0.7851 | +#> |.....................| -0.6667 | -1.418 | 1.962 | -1.804 | +#> |.....................| -1.038 | -0.3298 | -0.7816 | -1.157 | +#> |.....................| -0.5368 | -0.8226 | -0.9633 | -0.3812 | +#> | U| 467.22945 | 92.33 | -6.120 | -1.019 | -2.037 | +#> |.....................| -4.309 | 0.2137 | 2.003 | 0.03136 | +#> |.....................| 0.7606 | 0.07390 | 0.7997 | 0.6429 | +#> |.....................| 1.578 | 1.011 | 0.7823 | 1.807 | +#> | X|<span style='font-weight: bold;'> 467.22945</span> | 92.33 | 0.002199 | 0.2652 | 0.1304 | +#> |.....................| 0.01345 | 0.5532 | 2.003 | 0.03136 | +#> |.....................| 0.7606 | 0.07390 | 0.7997 | 0.6429 | +#> |.....................| 1.578 | 1.011 | 0.7823 | 1.807 | +#> |<span style='font-weight: bold;'> 86</span>| 466.68655 | 1.009 | -1.812 | -0.9919 | -0.8198 | +#> |.....................| -0.7896 | -1.384 | 1.793 | -1.792 | +#> |.....................| -0.9716 | -0.4604 | -0.9317 | -1.100 | +#> |.....................| -0.5645 | -0.8633 | -0.9948 | -0.2964 | +#> | U| 466.68655 | 92.33 | -6.001 | -0.9592 | -2.072 | +#> |.....................| -4.432 | 0.2290 | 1.933 | 0.03172 | +#> |.....................| 0.7883 | 0.07013 | 0.6901 | 0.6945 | +#> |.....................| 1.545 | 0.9719 | 0.7553 | 1.910 | +#> | X|<span style='font-weight: bold;'> 466.68655</span> | 92.33 | 0.002477 | 0.2770 | 0.1260 | +#> |.....................| 0.01190 | 0.5570 | 1.933 | 0.03172 | +#> |.....................| 0.7883 | 0.07013 | 0.6901 | 0.6945 | +#> |.....................| 1.545 | 0.9719 | 0.7553 | 1.910 | +#> | F| Forward Diff. | -11.18 | 0.05254 | -0.8763 | -0.07569 | +#> |.....................| 0.1998 | -0.2059 | -0.4605 | -0.7124 | +#> |.....................| -0.3271 | 0.07217 | 0.9692 | 1.710 | +#> |.....................| -0.7229 | 0.7265 | 0.2517 | -0.09129 | +#> |<span style='font-weight: bold;'> 87</span>| 466.82655 | 1.009 | -1.865 | -0.9192 | -0.7946 | +#> |.....................| -0.7769 | -1.362 | 1.859 | -1.827 | +#> |.....................| -0.9838 | -0.4392 | -0.9155 | -1.146 | +#> |.....................| -0.4995 | -0.8511 | -1.000 | -0.3560 | +#> | U| 466.82655 | 92.34 | -6.054 | -0.8947 | -2.046 | +#> |.....................| -4.419 | 0.2394 | 1.960 | 0.03072 | +#> |.....................| 0.7832 | 0.07074 | 0.7019 | 0.6527 | +#> |.....................| 1.622 | 0.9836 | 0.7506 | 1.838 | +#> | X|<span style='font-weight: bold;'> 466.82655</span> | 92.34 | 0.002349 | 0.2901 | 0.1292 | +#> |.....................| 0.01205 | 0.5596 | 1.960 | 0.03072 | +#> |.....................| 0.7832 | 0.07074 | 0.7019 | 0.6527 | +#> |.....................| 1.622 | 0.9836 | 0.7506 | 1.838 | +#> |<span style='font-weight: bold;'> 88</span>| 466.65072 | 1.010 | -1.827 | -0.9719 | -0.8129 | +#> |.....................| -0.7861 | -1.378 | 1.811 | -1.801 | +#> |.....................| -0.9749 | -0.4546 | -0.9274 | -1.113 | +#> |.....................| -0.5467 | -0.8600 | -0.9963 | -0.3127 | +#> | U| 466.65072 | 92.43 | -6.015 | -0.9415 | -2.065 | +#> |.....................| -4.428 | 0.2318 | 1.940 | 0.03144 | +#> |.....................| 0.7869 | 0.07030 | 0.6933 | 0.6830 | +#> |.....................| 1.566 | 0.9750 | 0.7540 | 1.891 | +#> | X|<span style='font-weight: bold;'> 466.65072</span> | 92.43 | 0.002441 | 0.2806 | 0.1269 | +#> |.....................| 0.01194 | 0.5577 | 1.940 | 0.03144 | +#> |.....................| 0.7869 | 0.07030 | 0.6933 | 0.6830 | +#> |.....................| 1.566 | 0.9750 | 0.7540 | 1.891 | +#> | F| Forward Diff. | -1.340 | 0.07863 | 0.1180 | -0.03302 | +#> |.....................| 0.1973 | -0.03638 | -0.4314 | -0.7320 | +#> |.....................| -0.3719 | 0.04356 | 0.7597 | 1.009 | +#> |.....................| 0.3079 | 0.4883 | -0.4019 | -0.3069 | +#> |<span style='font-weight: bold;'> 89</span>| 466.64054 | 1.012 | -1.843 | -0.9769 | -0.8069 | +#> |.....................| -0.7968 | -1.376 | 1.833 | -1.786 | +#> |.....................| -0.9571 | -0.4600 | -0.9463 | -1.118 | +#> |.....................| -0.5553 | -0.8554 | -0.9954 | -0.3119 | +#> | U| 466.64054 | 92.56 | -6.031 | -0.9459 | -2.059 | +#> |.....................| -4.439 | 0.2329 | 1.949 | 0.03189 | +#> |.....................| 0.7943 | 0.07014 | 0.6795 | 0.6783 | +#> |.....................| 1.556 | 0.9795 | 0.7548 | 1.892 | +#> | X|<span style='font-weight: bold;'> 466.64054</span> | 92.56 | 0.002403 | 0.2797 | 0.1276 | +#> |.....................| 0.01181 | 0.5580 | 1.949 | 0.03189 | +#> |.....................| 0.7943 | 0.07014 | 0.6795 | 0.6783 | +#> |.....................| 1.556 | 0.9795 | 0.7548 | 1.892 | +#> | F| Forward Diff. | 13.35 | 0.06546 | -0.02976 | 0.01632 | +#> |.....................| 0.1680 | -0.06031 | -0.2101 | 0.2297 | +#> |.....................| -0.01975 | 0.1913 | 0.1108 | 0.6100 | +#> |.....................| -0.008263 | 1.320 | 0.06198 | -0.2490 | +#> |<span style='font-weight: bold;'> 90</span>| 466.63994 | 1.010 | -1.856 | -0.9836 | -0.8023 | +#> |.....................| -0.8121 | -1.369 | 1.859 | -1.781 | +#> |.....................| -0.9548 | -0.4699 | -0.9506 | -1.117 | +#> |.....................| -0.5644 | -0.8726 | -1.009 | -0.3176 | +#> | U| 466.63994 | 92.43 | -6.045 | -0.9518 | -2.054 | +#> |.....................| -4.454 | 0.2360 | 1.960 | 0.03203 | +#> |.....................| 0.7952 | 0.06986 | 0.6763 | 0.6795 | +#> |.....................| 1.545 | 0.9629 | 0.7430 | 1.885 | +#> | X|<span style='font-weight: bold;'> 466.63994</span> | 92.43 | 0.002371 | 0.2785 | 0.1282 | +#> |.....................| 0.01163 | 0.5587 | 1.960 | 0.03203 | +#> |.....................| 0.7952 | 0.06986 | 0.6763 | 0.6795 | +#> |.....................| 1.545 | 0.9629 | 0.7430 | 1.885 | +#> | F| Forward Diff. | 0.1431 | 0.02593 | -0.4247 | 0.08835 | +#> |.....................| 0.1490 | -0.08497 | 0.03702 | 0.4153 | +#> |.....................| -0.04754 | 0.2015 | 0.06787 | -0.3581 | +#> |.....................| -0.4069 | 0.09362 | -0.9227 | -0.5264 | +#> |<span style='font-weight: bold;'> 91</span>| 466.65402 | 1.008 | -1.856 | -0.9767 | -0.8037 | +#> |.....................| -0.8145 | -1.367 | 1.858 | -1.788 | +#> |.....................| -0.9540 | -0.4731 | -0.9517 | -1.111 | +#> |.....................| -0.5579 | -0.8741 | -0.9943 | -0.3092 | +#> | U| 466.65402 | 92.22 | -6.045 | -0.9458 | -2.055 | +#> |.....................| -4.457 | 0.2367 | 1.960 | 0.03184 | +#> |.....................| 0.7955 | 0.06976 | 0.6755 | 0.6846 | +#> |.....................| 1.553 | 0.9615 | 0.7557 | 1.895 | +#> | X|<span style='font-weight: bold;'> 466.65402</span> | 92.22 | 0.002370 | 0.2797 | 0.1280 | +#> |.....................| 0.01160 | 0.5589 | 1.960 | 0.03184 | +#> |.....................| 0.7955 | 0.06976 | 0.6755 | 0.6846 | +#> |.....................| 1.553 | 0.9615 | 0.7557 | 1.895 | +#> |<span style='font-weight: bold;'> 92</span>| 466.63541 | 1.010 | -1.856 | -0.9812 | -0.8028 | +#> |.....................| -0.8129 | -1.368 | 1.858 | -1.783 | +#> |.....................| -0.9545 | -0.4710 | -0.9509 | -1.115 | +#> |.....................| -0.5622 | -0.8731 | -1.004 | -0.3147 | +#> | U| 466.63541 | 92.36 | -6.045 | -0.9498 | -2.055 | +#> |.....................| -4.455 | 0.2363 | 1.960 | 0.03197 | +#> |.....................| 0.7953 | 0.06982 | 0.6761 | 0.6812 | +#> |.....................| 1.548 | 0.9624 | 0.7474 | 1.888 | +#> | X|<span style='font-weight: bold;'> 466.63541</span> | 92.36 | 0.002371 | 0.2789 | 0.1281 | +#> |.....................| 0.01162 | 0.5588 | 1.960 | 0.03197 | +#> |.....................| 0.7953 | 0.06982 | 0.6761 | 0.6812 | +#> |.....................| 1.548 | 0.9624 | 0.7474 | 1.888 | +#> | F| Forward Diff. | -7.597 | 0.01585 | -0.3721 | 0.09081 | +#> |.....................| 0.1473 | -0.05128 | 0.01723 | 0.2650 | +#> |.....................| -0.04930 | 0.2121 | 0.3911 | -0.1952 | +#> |.....................| -0.2951 | 0.01195 | -0.4116 | -0.4404 | +#> |<span style='font-weight: bold;'> 93</span>| 466.62967 | 1.010 | -1.857 | -0.9822 | -0.8038 | +#> |.....................| -0.8179 | -1.367 | 1.859 | -1.785 | +#> |.....................| -0.9524 | -0.4748 | -0.9515 | -1.114 | +#> |.....................| -0.5617 | -0.8740 | -1.004 | -0.3130 | +#> | U| 466.62967 | 92.43 | -6.045 | -0.9507 | -2.056 | +#> |.....................| -4.460 | 0.2370 | 1.960 | 0.03192 | +#> |.....................| 0.7962 | 0.06971 | 0.6756 | 0.6815 | +#> |.....................| 1.548 | 0.9616 | 0.7476 | 1.890 | +#> | X|<span style='font-weight: bold;'> 466.62967</span> | 92.43 | 0.002369 | 0.2787 | 0.1280 | +#> |.....................| 0.01156 | 0.5590 | 1.960 | 0.03192 | +#> |.....................| 0.7962 | 0.06971 | 0.6756 | 0.6815 | +#> |.....................| 1.548 | 0.9616 | 0.7476 | 1.890 | +#> | F| Forward Diff. | 0.1737 | 0.01712 | -0.3712 | 0.07555 | +#> |.....................| 0.1320 | -0.03330 | -0.1756 | 0.3015 | +#> |.....................| -0.06297 | 0.1717 | 0.09645 | -0.1674 | +#> |.....................| -0.2756 | -0.01624 | -0.3459 | -0.4307 | +#> |<span style='font-weight: bold;'> 94</span>| 466.62779 | 1.010 | -1.856 | -0.9797 | -0.8047 | +#> |.....................| -0.8221 | -1.366 | 1.862 | -1.786 | +#> |.....................| -0.9500 | -0.4779 | -0.9517 | -1.113 | +#> |.....................| -0.5623 | -0.8742 | -1.003 | -0.3111 | +#> | U| 466.62779 | 92.40 | -6.045 | -0.9484 | -2.056 | +#> |.....................| -4.464 | 0.2375 | 1.961 | 0.03188 | +#> |.....................| 0.7972 | 0.06963 | 0.6755 | 0.6823 | +#> |.....................| 1.548 | 0.9614 | 0.7480 | 1.893 | +#> | X|<span style='font-weight: bold;'> 466.62779</span> | 92.40 | 0.002370 | 0.2792 | 0.1279 | +#> |.....................| 0.01152 | 0.5591 | 1.961 | 0.03188 | +#> |.....................| 0.7972 | 0.06963 | 0.6755 | 0.6823 | +#> |.....................| 1.548 | 0.9614 | 0.7480 | 1.893 | +#> | F| Forward Diff. | -2.926 | 0.01199 | -0.2808 | 0.07297 | +#> |.....................| 0.1250 | -0.02504 | 0.02207 | 0.2419 | +#> |.....................| -0.03068 | 0.1983 | 0.3271 | -0.08125 | +#> |.....................| -0.2841 | -0.05347 | -0.2873 | -0.3919 | +#> |<span style='font-weight: bold;'> 95</span>| 466.62386 | 1.010 | -1.856 | -0.9811 | -0.8057 | +#> |.....................| -0.8267 | -1.365 | 1.862 | -1.788 | +#> |.....................| -0.9479 | -0.4822 | -0.9526 | -1.114 | +#> |.....................| -0.5610 | -0.8741 | -1.003 | -0.3093 | +#> | U| 466.62386 | 92.43 | -6.045 | -0.9497 | -2.057 | +#> |.....................| -4.469 | 0.2377 | 1.961 | 0.03183 | +#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | +#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | +#> | X|<span style='font-weight: bold;'> 466.62386</span> | 92.43 | 0.002370 | 0.2789 | 0.1278 | +#> |.....................| 0.01146 | 0.5592 | 1.961 | 0.03183 | +#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | +#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | +#> | F| Forward Diff. | 0.1137 | 0.01564 | -0.3265 | 0.06191 | +#> |.....................| 0.1094 | -0.02529 | 0.01125 | 0.2123 | +#> |.....................| -0.07598 | 0.1365 | 0.2003 | -0.1363 | +#> |.....................| -0.2276 | -0.05501 | -0.2526 | -0.4116 | +#> |<span style='font-weight: bold;'> 96</span>| 466.62386 | 1.010 | -1.856 | -0.9811 | -0.8057 | +#> |.....................| -0.8267 | -1.365 | 1.862 | -1.788 | +#> |.....................| -0.9479 | -0.4822 | -0.9526 | -1.114 | +#> |.....................| -0.5610 | -0.8741 | -1.003 | -0.3093 | +#> | U| 466.62386 | 92.43 | -6.045 | -0.9497 | -2.057 | +#> |.....................| -4.469 | 0.2377 | 1.961 | 0.03183 | +#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | +#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | +#> | X|<span style='font-weight: bold;'> 466.62386</span> | 92.43 | 0.002370 | 0.2789 | 0.1278 | +#> |.....................| 0.01146 | 0.5592 | 1.961 | 0.03183 | +#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | +#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | +#> 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: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</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 $scaleInfo</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> <span class='va'>f_nlmixr_sfo_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_fomc_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, @@ -873,11 +9602,110 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. <span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span><span class='op'>$</span><span class='va'>nm</span> <span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in AIC(f_nlmixr_sfo_sfo_focei_const$nm, f_nlmixr_fomc_sfo_focei_const$nm, f_nlmixr_dfop_sfo_focei_const$nm, f_nlmixr_fomc_sfo_saem_obs$nm, f_nlmixr_fomc_sfo_focei_obs$nm, f_nlmixr_dfop_sfo_saem_obs$nm, f_nlmixr_dfop_sfo_focei_obs$nm, f_nlmixr_fomc_sfo_focei_tc$nm, f_nlmixr_dfop_sfo_focei_tc$nm, f_nlmixr_fomc_sfo_saem_obs_tc$nm, f_nlmixr_fomc_sfo_focei_obs_tc$nm, f_nlmixr_dfop_sfo_saem_obs_tc$nm, f_nlmixr_dfop_sfo_focei_obs_tc$nm): object 'f_nlmixr_sfo_sfo_focei_const' not found</span></div><div class='input'><span class='co'># Currently, FOMC-SFO with two-component error by variable fitted by focei gives the</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'>#> <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'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</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'>#> df AIC +#> f_nlmixr_sfo_sfo_focei_const$nm 9 1082.4868 +#> f_nlmixr_fomc_sfo_focei_const$nm 11 814.4317 +#> f_nlmixr_dfop_sfo_focei_const$nm 13 866.0485 +#> f_nlmixr_fomc_sfo_saem_obs$nm 12 791.7256 +#> f_nlmixr_fomc_sfo_focei_obs$nm 12 794.5998 +#> f_nlmixr_dfop_sfo_saem_obs$nm 14 812.0463 +#> f_nlmixr_dfop_sfo_focei_obs$nm 14 846.9228 +#> f_nlmixr_fomc_sfo_focei_tc$nm 12 812.3585 +#> f_nlmixr_dfop_sfo_focei_tc$nm 14 842.3479 +#> f_nlmixr_fomc_sfo_saem_obs_tc$nm 14 817.1261 +#> f_nlmixr_fomc_sfo_focei_obs_tc$nm 14 787.4863 +#> f_nlmixr_dfop_sfo_saem_obs_tc$nm 16 858.3213 +#> f_nlmixr_dfop_sfo_focei_obs_tc$nm 16 811.0630</div><div class='input'><span class='co'># Currently, FOMC-SFO with two-component error by variable fitted by focei gives the</span> <span class='co'># lowest AIC</span> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in plot(f_nlmixr_fomc_sfo_focei_obs_tc): object 'f_nlmixr_fomc_sfo_focei_obs_tc' not found</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_nlmixr_fomc_sfo_focei_obs_tc</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in summary(f_nlmixr_fomc_sfo_focei_obs_tc): object 'f_nlmixr_fomc_sfo_focei_obs_tc' not found</span></div><div class='input'><span class='co'># }</span> +</div><div class='img'><img src='nlmixr.mmkin-2.png' alt='' width='700' height='433' /></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_nlmixr_fomc_sfo_focei_obs_tc</span><span class='op'>)</span> +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Tue Oct 5 17:25:02 2021 +#> Date of summary: Tue Oct 5 17:26:23 2021 +#> +#> Equations: +#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent +#> d_A1/dt = + f_parent_to_A1 * (alpha/beta) * 1/((time/beta) + 1) * +#> parent - k_A1 * A1 +#> +#> Data: +#> 170 observations of 2 variable(s) grouped in 5 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 24.31 s +#> +#> Variance model: Two-component variance unique to each observed variable +#> +#> Mean of starting values for individual parameters: +#> parent_0 log_k_A1 f_parent_qlogis log_alpha log_beta +#> 93.1168 -5.3034 -0.9442 -0.1065 2.2909 +#> +#> Mean of starting values for error model parameters: +#> sigma_low_parent rsd_high_parent sigma_low_A1 rsd_high_A1 +#> 1.15958 0.03005 1.15958 0.03005 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 787.5 831.4 -379.7 +#> +#> Optimised parameters: +#> est. lower upper +#> parent_0 93.6898 91.2681 96.1114 +#> log_k_A1 -6.2923 -8.3662 -4.2185 +#> f_parent_qlogis -1.0019 -1.3760 -0.6278 +#> log_alpha -0.1639 -0.6641 0.3363 +#> log_beta 2.2031 1.6723 2.7340 +#> +#> Correlation: +#> prnt_0 lg__A1 f_prn_ lg_lph +#> log_k_A1 0.368 +#> f_parent_qlogis -0.788 -0.401 +#> log_alpha 0.338 0.942 -0.307 +#> log_beta -0.401 -0.761 0.253 -0.555 +#> +#> Random effects (omega): +#> eta.parent_0 eta.log_k_A1 eta.f_parent_qlogis eta.log_alpha +#> eta.parent_0 4.74 0.00 0.0000 0.0000 +#> eta.log_k_A1 0.00 5.57 0.0000 0.0000 +#> eta.f_parent_qlogis 0.00 0.00 0.1646 0.0000 +#> eta.log_alpha 0.00 0.00 0.0000 0.3312 +#> eta.log_beta 0.00 0.00 0.0000 0.0000 +#> eta.log_beta +#> eta.parent_0 0.0000 +#> eta.log_k_A1 0.0000 +#> eta.f_parent_qlogis 0.0000 +#> eta.log_alpha 0.0000 +#> eta.log_beta 0.3438 +#> +#> Variance model: +#> sigma_low_parent rsd_high_parent sigma_low_A1 rsd_high_A1 +#> 2.35467 0.00261 0.64525 0.08456 +#> +#> Backtransformed parameters: +#> est. lower upper +#> parent_0 93.68976 9.127e+01 96.11140 +#> k_A1 0.00185 2.326e-04 0.01472 +#> f_parent_to_A1 0.26857 2.017e-01 0.34801 +#> alpha 0.84879 5.147e-01 1.39971 +#> beta 9.05342 5.325e+00 15.39359 +#> +#> Resulting formation fractions: +#> ff +#> parent_A1 0.2686 +#> parent_sink 0.7314 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back +#> parent 11.43 127.4 38.35 +#> A1 374.59 1244.4 NA</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/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 9f0eb965..69345b2f 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -295,12 +295,8 @@ corresponding model prediction lines for the different datasets.</p></td> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme</span><span class='op'>)</span> </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] "Thu Sep 16 14:34:31 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:38 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem</span><span class='op'>)</span> -</div><div class='img'><img src='plot.mixed.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> +</div><div class='output co'>#> <span class='error'>Error in saem(f, transformations = "saemix"): unused argument (transformations = "saemix")</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in plot(f_saem): object 'f_saem' not found</span></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> </div><div class='output co'>#> <span class='error'>Error in nlmix(f_obs): could not find function "nlmix"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlmix</span><span class='op'>)</span> @@ -311,7 +307,7 @@ corresponding model prediction lines for the different datasets.</p></td> <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='va'>f_nlme</span><span class='op'>$</span><span class='va'>bparms.optim</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, A1 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>180</span>, by <span class='op'>=</span> <span class='fl'>0.2</span><span class='op'>)</span><span class='op'>)</span> <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>, pred_over <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>nlme <span class='op'>=</span> <span class='va'>pred_nlme</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='img'><img src='plot.mixed.mmkin-4.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> +</div><div class='output co'>#> <span class='error'>Error in plot(f_saem, pred_over = list(nlme = pred_nlme)): object 'f_saem' not found</span></div><div class='input'><span class='co'># }</span> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 8d986126..83a62359 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -287,28 +287,12 @@ using <a href='mmkin.html'>mmkin</a>.</p> <span class='va'>f_mmkin_parent_p0_fixed</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='va'>ds</span>, 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] "Thu Sep 16 14:34:42 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:43 2021"</div><div class='input'> +</div><div class='output co'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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] "Thu Sep 16 14:34:45 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:47 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] "Thu Sep 16 14:34:47 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:49 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] "Thu Sep 16 14:34:49 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:34:52 2021"</div><div class='input'> +</div><div class='output co'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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> @@ -317,53 +301,15 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> <span class='message'>Attaching package: ‘saemix’</span></div><div class='output co'>#> <span class='message'>The following object is masked from ‘package:RxODE’:</span> #> <span class='message'></span> #> <span class='message'> phi</span></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_sfo</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_dfop</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Likelihoods calculated by importance sampling</span></div><div class='output co'>#> AIC BIC -#> 1 624.2484 622.2956 -#> 2 467.7096 464.9757 -#> 3 495.4373 491.9222</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"convergence"</span><span class='op'>)</span> -</div><div class='output co'>#> Plotting convergence plots</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_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"individual.fit"</span><span class='op'>)</span> -</div><div class='img'><img src='saem-1.png' alt='' width='700' height='433' /></div><div class='output co'>#> Plotting individual fits</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"npde"</span><span class='op'>)</span> -</div><div class='img'><img src='saem-2.png' alt='' width='700' height='433' /></div><div class='output co'>#> Simulating data using nsim = 1000 simulated datasets -#> Computing WRES and npde . -#> Plotting npde</div><div class='img'><img src='saem-3.png' alt='' width='700' height='433' /></div><div class='output co'>#> --------------------------------------------- -#> Distribution of npde: -#> mean= -0.01528 (SE= 0.098 ) -#> variance= 0.862 (SE= 0.13 ) -#> skewness= 0.5016 -#> kurtosis= 1.18 -#> --------------------------------------------- -#> -#> Statistical tests -#> Wilcoxon signed rank test : 0.679 -#> Fisher variance test : 0.36 -#> SW test of normality : 0.0855 . -#> Global adjusted p-value : 0.257 -#> --- -#> Signif. codes: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 -#> ---------------------------------------------</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_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"vpc"</span><span class='op'>)</span> -</div><div class='output co'>#> Performing simulations under the model. -#> Plotting VPC -#> Method used for VPC: binning by quantiles on X , dividing into the following intervals -#> Interval Centered.On -#> 1 (-1,3] 1.3 -#> 2 (3,8] 7.4 -#> 3 (8,14] 13.2 -#> 4 (14,21] 20.5 -#> 5 (21,37.7] 29.5 -#> 6 (37.7,60] 50.4 -#> 7 (60,90] 76.6 -#> 8 (90,120] 109.0 -#> 9 (120,180] 156.0 </div><div class='img'><img src='saem-4.png' alt='' width='700' height='433' /></div><div class='input'> +</div><div class='output co'>#> <span class='error'>Error in compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so): object 'f_saem_sfo' not found</span></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_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"convergence"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"individual.fit"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span></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_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"npde"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"vpc"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span></div><div class='input'> <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] "Thu Sep 16 14:34:55 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:35:00 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc_tc</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span> -</div><div class='output co'>#> <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'> +</div><div class='output co'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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='error'>Error in compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so): object 'f_saem_fomc' not found</span></div><div class='input'> <span class='va'>sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fomc_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, @@ -380,314 +326,12 @@ using <a href='mmkin.html'>mmkin</a>.</p> <span class='co'># When using the analytical solutions written for mkin this took around</span> <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] "Thu Sep 16 14:35:03 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:35:08 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] "Thu Sep 16 14:35:08 2021" -#> .... -#> Minimisation finished -#> [1] "Thu Sep 16 14:35:17 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span> +</div><div class='output co'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</span></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: -#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * parent -#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * parent - k_A1 * A1 -#> -#> Data: -#> 170 observations of 2 variable(s) grouped in 5 datasets -#> -#> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 839.6 834.6 -406.8 -#> -#> Fitted parameters: -#> estimate lower upper -#> 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: 1.1.0 -#> R version used for fitting: 4.1.1 -#> Date of fit: Thu Sep 16 14:35:18 2021 -#> Date of summary: Thu Sep 16 14:35:18 2021 -#> -#> Equations: -#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * parent -#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * parent - k_A1 * A1 -#> -#> Data: -#> 170 observations of 2 variable(s) grouped in 5 datasets -#> -#> Model predictions using solution type analytical -#> -#> Fitted in 9.349 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 -5.3734 -0.9711 -1.8799 -4.2708 -#> g_qlogis -#> 0.1356 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 839.6 834.6 -406.8 -#> -#> Optimised parameters: -#> est. lower upper -#> 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 -0.014 -#> 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 2.7243 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 1.884 1.668 2.1 -#> -#> Backtransformed parameters: -#> est. lower upper -#> 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 -#> parent_A1 0.2742 -#> parent_sink 0.7258 -#> -#> Estimated disappearance times: -#> 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 95.75408 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'> +</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'f_saem_dfop_sfo' not found</span></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='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_dfop_sfo' 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_saem_dfop_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_saem_dfop_sfo' not found</span></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 4831bbdf..891e26ea 100644 --- a/docs/dev/reference/summary.nlmixr.mmkin.html +++ b/docs/dev/reference/summary.nlmixr.mmkin.html @@ -258,73 +258,737 @@ 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] "Thu Sep 16 14:35:21 2021" +#> [1] "Tue Oct 5 17:27:02 2021" #> .... #> Minimisation finished -#> [1] "Thu Sep 16 14:35:33 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 Oct 5 17:27:13 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'>#> <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: 1.412 0.101 1.525</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'>#> 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 +#> 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-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 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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 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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 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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> <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'>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: 18.28 0.455 18.73</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> -</div><div class='output co'>#> <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='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='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/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index 08e3c8f8..fe537320 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.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> @@ -259,272 +259,8 @@ saemix authors for the parts inherited from saemix.</p> <span class='va'>f_mmkin_dfop_sfo</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='va'>dfop_sfo</span><span class='op'>)</span>, <span class='va'>ds_syn_dfop_sfo</span>, 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_saem_dfop_sfo</span> <span class='op'><-</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] "Wed Aug 4 16:23:26 2021" -#> .... -#> Minimisation finished -#> [1] "Wed Aug 4 16:23:38 2021"</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 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 16:23:39 2021 -#> Date of summary: Wed Aug 4 16:23:39 2021 -#> -#> Equations: -#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * parent -#> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * parent - k_m1 * m1 -#> -#> Data: -#> 171 observations of 2 variable(s) grouped in 5 datasets -#> -#> Model predictions using solution type analytical -#> -#> Fitted in 12.54 s using 300, 100 iterations -#> -#> Variance model: Two-component variance function -#> -#> Mean of starting values for individual parameters: -#> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 -#> 101.65645 -4.05368 -0.94311 -2.35943 -4.07006 -#> g_qlogis -#> -0.01132 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 825.9 820.4 -398.9 -#> -#> Optimised parameters: -#> est. lower upper -#> parent_0 101.118986 97.368 104.8695 -#> log_k_m1 -4.057591 -4.177 -3.9379 -#> f_parent_qlogis -0.933087 -1.290 -0.5763 -#> log_k1 -2.945520 -3.833 -2.0576 -#> log_k2 -3.531954 -4.310 -2.7542 -#> g_qlogis -0.009584 -1.688 1.6687 -#> -#> Correlation: -#> prnt_0 lg_k_1 f_prn_ log_k1 log_k2 -#> log_k_m1 -0.198 -#> f_parent_qlogis -0.153 0.184 -#> log_k1 0.080 -0.077 -0.045 -#> log_k2 0.005 0.008 -0.003 -0.019 -#> g_qlogis -0.059 0.048 0.041 -0.334 -0.253 -#> -#> Random effects: -#> est. lower upper -#> SD.parent_0 2.97797 -0.62927 6.5852 -#> SD.log_k_m1 0.09235 -0.02448 0.2092 -#> SD.f_parent_qlogis 0.38712 0.13469 0.6396 -#> SD.log_k1 0.88671 0.27052 1.5029 -#> SD.log_k2 0.80497 0.25587 1.3541 -#> SD.g_qlogis 0.36812 -3.56188 4.2981 -#> -#> Variance model: -#> est. lower upper -#> a.1 0.85879 0.68143 1.03615 -#> b.1 0.07787 0.06288 0.09286 -#> -#> Backtransformed parameters: -#> est. lower upper -#> parent_0 101.11899 97.36850 104.86947 -#> k_m1 0.01729 0.01534 0.01949 -#> f_parent_to_m1 0.28230 0.21587 0.35979 -#> k1 0.05257 0.02163 0.12776 -#> k2 0.02925 0.01344 0.06366 -#> g 0.49760 0.15606 0.84140 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.2823 -#> parent_sink 0.7177 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 17.47 62.31 18.76 13.18 23.7 -#> m1 40.09 133.17 NA NA NA -#> -#> Data: -#> ds name time observed predicted residual std standardized -#> ds 1 parent 0 89.8 9.838e+01 -8.584661 7.7094 -1.113536 -#> ds 1 parent 0 104.1 9.838e+01 5.715339 7.7094 0.741350 -#> ds 1 parent 1 88.7 9.388e+01 -5.182489 7.3611 -0.704041 -#> ds 1 parent 1 95.5 9.388e+01 1.617511 7.3611 0.219739 -#> ds 1 parent 3 81.8 8.563e+01 -3.825382 6.7229 -0.569010 -#> ds 1 parent 3 94.5 8.563e+01 8.874618 6.7229 1.320062 -#> ds 1 parent 7 71.5 7.169e+01 -0.188290 5.6482 -0.033336 -#> ds 1 parent 7 70.3 7.169e+01 -1.388290 5.6482 -0.245795 -#> ds 1 parent 14 54.2 5.361e+01 0.586595 4.2624 0.137621 -#> ds 1 parent 14 49.6 5.361e+01 -4.013405 4.2624 -0.941587 -#> ds 1 parent 28 31.5 3.219e+01 -0.688936 2.6496 -0.260011 -#> ds 1 parent 28 28.8 3.219e+01 -3.388936 2.6496 -1.279016 -#> ds 1 parent 60 12.1 1.278e+01 -0.678998 1.3145 -0.516562 -#> ds 1 parent 60 13.6 1.278e+01 0.821002 1.3145 0.624595 -#> ds 1 parent 90 6.2 6.157e+00 0.043461 0.9835 0.044188 -#> ds 1 parent 90 8.3 6.157e+00 2.143461 0.9835 2.179316 -#> ds 1 parent 120 2.2 3.076e+00 -0.876218 0.8916 -0.982775 -#> ds 1 parent 120 2.4 3.076e+00 -0.676218 0.8916 -0.758453 -#> ds 1 m1 1 0.3 1.134e+00 -0.833749 0.8633 -0.965750 -#> ds 1 m1 1 0.2 1.134e+00 -0.933749 0.8633 -1.081583 -#> ds 1 m1 3 2.2 3.157e+00 -0.957400 0.8933 -1.071763 -#> ds 1 m1 3 3.0 3.157e+00 -0.157400 0.8933 -0.176202 -#> ds 1 m1 7 6.5 6.369e+00 0.130995 0.9917 0.132090 -#> ds 1 m1 7 5.0 6.369e+00 -1.369005 0.9917 -1.380438 -#> ds 1 m1 14 10.2 9.971e+00 0.229362 1.1577 0.198112 -#> ds 1 m1 14 9.5 9.971e+00 -0.470638 1.1577 -0.406513 -#> ds 1 m1 28 12.2 1.265e+01 -0.447735 1.3067 -0.342637 -#> ds 1 m1 28 13.4 1.265e+01 0.752265 1.3067 0.575683 -#> ds 1 m1 60 11.8 1.097e+01 0.832027 1.2112 0.686945 -#> ds 1 m1 60 13.2 1.097e+01 2.232027 1.2112 1.842825 -#> ds 1 m1 90 6.6 7.876e+00 -1.275985 1.0553 -1.209109 -#> ds 1 m1 90 9.3 7.876e+00 1.424015 1.0553 1.349381 -#> ds 1 m1 120 3.5 5.336e+00 -1.835829 0.9540 -1.924292 -#> ds 1 m1 120 5.4 5.336e+00 0.064171 0.9540 0.067263 -#> ds 2 parent 0 118.0 1.092e+02 8.812058 8.5459 1.031142 -#> ds 2 parent 0 99.8 1.092e+02 -9.387942 8.5459 -1.098529 -#> ds 2 parent 1 90.2 1.023e+02 -12.114268 8.0135 -1.511724 -#> ds 2 parent 1 94.6 1.023e+02 -7.714268 8.0135 -0.962654 -#> ds 2 parent 3 96.1 9.066e+01 5.436165 7.1122 0.764344 -#> ds 2 parent 3 78.4 9.066e+01 -12.263835 7.1122 -1.724339 -#> ds 2 parent 7 77.9 7.365e+01 4.245773 5.7995 0.732090 -#> ds 2 parent 7 77.7 7.365e+01 4.045773 5.7995 0.697604 -#> ds 2 parent 14 56.0 5.593e+01 0.073803 4.4389 0.016626 -#> ds 2 parent 14 54.7 5.593e+01 -1.226197 4.4389 -0.276236 -#> ds 2 parent 28 36.6 3.892e+01 -2.320837 3.1502 -0.736737 -#> ds 2 parent 28 36.8 3.892e+01 -2.120837 3.1502 -0.673248 -#> ds 2 parent 60 22.1 2.136e+01 0.741020 1.8719 0.395868 -#> ds 2 parent 60 24.7 2.136e+01 3.341020 1.8719 1.784841 -#> ds 2 parent 90 12.4 1.251e+01 -0.113999 1.2989 -0.087765 -#> ds 2 parent 90 10.8 1.251e+01 -1.713999 1.2989 -1.319575 -#> ds 2 parent 120 6.8 7.338e+00 -0.537708 1.0315 -0.521281 -#> ds 2 parent 120 7.9 7.338e+00 0.562292 1.0315 0.545113 -#> ds 2 m1 1 1.3 1.576e+00 -0.276176 0.8675 -0.318352 -#> ds 2 m1 3 3.7 4.177e+00 -0.476741 0.9183 -0.519146 -#> ds 2 m1 3 4.7 4.177e+00 0.523259 0.9183 0.569801 -#> ds 2 m1 7 8.1 7.724e+00 0.376365 1.0485 0.358970 -#> ds 2 m1 7 7.9 7.724e+00 0.176365 1.0485 0.168214 -#> ds 2 m1 14 10.1 1.077e+01 -0.674433 1.2006 -0.561738 -#> ds 2 m1 14 10.3 1.077e+01 -0.474433 1.2006 -0.395158 -#> ds 2 m1 28 10.7 1.212e+01 -1.416179 1.2758 -1.110010 -#> ds 2 m1 28 12.2 1.212e+01 0.083821 1.2758 0.065699 -#> ds 2 m1 60 10.7 1.041e+01 0.294930 1.1807 0.249793 -#> ds 2 m1 60 12.5 1.041e+01 2.094930 1.1807 1.774316 -#> ds 2 m1 90 9.1 8.079e+00 1.020859 1.0646 0.958929 -#> ds 2 m1 90 7.4 8.079e+00 -0.679141 1.0646 -0.637941 -#> ds 2 m1 120 6.1 5.968e+00 0.131673 0.9765 0.134843 -#> ds 2 m1 120 4.5 5.968e+00 -1.468327 0.9765 -1.503683 -#> ds 3 parent 0 106.2 1.036e+02 2.638248 8.1101 0.325303 -#> ds 3 parent 0 106.9 1.036e+02 3.338248 8.1101 0.411614 -#> ds 3 parent 1 107.4 9.580e+01 11.600063 7.5094 1.544743 -#> ds 3 parent 1 96.1 9.580e+01 0.300063 7.5094 0.039958 -#> ds 3 parent 3 79.4 8.297e+01 -3.574516 6.5182 -0.548391 -#> ds 3 parent 3 82.6 8.297e+01 -0.374516 6.5182 -0.057457 -#> ds 3 parent 7 63.9 6.517e+01 -1.272397 5.1472 -0.247200 -#> ds 3 parent 7 62.4 6.517e+01 -2.772397 5.1472 -0.538618 -#> ds 3 parent 14 51.0 4.821e+01 2.790075 3.8512 0.724475 -#> ds 3 parent 14 47.1 4.821e+01 -1.109925 3.8512 -0.288205 -#> ds 3 parent 28 36.1 3.385e+01 2.250573 2.7723 0.811811 -#> ds 3 parent 28 36.6 3.385e+01 2.750573 2.7723 0.992168 -#> ds 3 parent 60 20.1 1.964e+01 0.455700 1.7543 0.259760 -#> ds 3 parent 60 19.8 1.964e+01 0.155700 1.7543 0.088753 -#> ds 3 parent 90 11.3 1.210e+01 -0.795458 1.2746 -0.624068 -#> ds 3 parent 90 10.7 1.210e+01 -1.395458 1.2746 -1.094792 -#> ds 3 parent 120 8.2 7.451e+00 0.749141 1.0364 0.722816 -#> ds 3 parent 120 7.3 7.451e+00 -0.150859 1.0364 -0.145558 -#> ds 3 m1 0 0.8 3.695e-13 0.800000 0.8588 0.931542 -#> ds 3 m1 1 1.8 1.740e+00 0.059741 0.8694 0.068714 -#> ds 3 m1 1 2.3 1.740e+00 0.559741 0.8694 0.643812 -#> ds 3 m1 3 4.2 4.531e+00 -0.331379 0.9285 -0.356913 -#> ds 3 m1 3 4.1 4.531e+00 -0.431379 0.9285 -0.464618 -#> ds 3 m1 7 6.8 8.113e+00 -1.312762 1.0661 -1.231333 -#> ds 3 m1 7 10.1 8.113e+00 1.987238 1.0661 1.863971 -#> ds 3 m1 14 11.4 1.079e+01 0.613266 1.2013 0.510507 -#> ds 3 m1 14 12.8 1.079e+01 2.013266 1.2013 1.675923 -#> ds 3 m1 28 11.5 1.133e+01 0.174252 1.2310 0.141553 -#> ds 3 m1 28 10.6 1.133e+01 -0.725748 1.2310 -0.589558 -#> ds 3 m1 60 7.5 8.948e+00 -1.448281 1.1059 -1.309561 -#> ds 3 m1 60 8.6 8.948e+00 -0.348281 1.1059 -0.314922 -#> ds 3 m1 90 7.3 6.665e+00 0.634932 1.0034 0.632752 -#> ds 3 m1 90 8.1 6.665e+00 1.434932 1.0034 1.430004 -#> ds 3 m1 120 5.3 4.795e+00 0.504936 0.9365 0.539199 -#> ds 3 m1 120 3.8 4.795e+00 -0.995064 0.9365 -1.062586 -#> ds 4 parent 0 104.7 9.985e+01 4.850494 7.8227 0.620050 -#> ds 4 parent 0 88.3 9.985e+01 -11.549506 7.8227 -1.476402 -#> ds 4 parent 1 94.2 9.676e+01 -2.556304 7.5834 -0.337093 -#> ds 4 parent 1 94.6 9.676e+01 -2.156304 7.5834 -0.284346 -#> ds 4 parent 3 78.1 9.092e+01 -12.817485 7.1318 -1.797230 -#> ds 4 parent 3 96.5 9.092e+01 5.582515 7.1318 0.782764 -#> ds 4 parent 7 76.2 8.050e+01 -4.297338 6.3270 -0.679204 -#> ds 4 parent 7 77.8 8.050e+01 -2.697338 6.3270 -0.426320 -#> ds 4 parent 14 70.8 6.562e+01 5.179989 5.1816 0.999687 -#> ds 4 parent 14 67.3 6.562e+01 1.679989 5.1816 0.324222 -#> ds 4 parent 28 43.1 4.499e+01 -1.886936 3.6069 -0.523140 -#> ds 4 parent 28 45.1 4.499e+01 0.113064 3.6069 0.031346 -#> ds 4 parent 60 21.3 2.151e+01 -0.214840 1.8827 -0.114114 -#> ds 4 parent 60 23.5 2.151e+01 1.985160 1.8827 1.054433 -#> ds 4 parent 90 11.8 1.190e+01 -0.098528 1.2633 -0.077990 -#> ds 4 parent 90 12.1 1.190e+01 0.201472 1.2633 0.159475 -#> ds 4 parent 120 7.0 6.886e+00 0.113832 1.0125 0.112431 -#> ds 4 parent 120 6.2 6.886e+00 -0.686168 1.0125 -0.677724 -#> ds 4 m1 0 1.6 4.263e-14 1.600000 0.8588 1.863085 -#> ds 4 m1 1 0.9 7.140e-01 0.185984 0.8606 0.216112 -#> ds 4 m1 3 3.7 2.022e+00 1.678243 0.8731 1.922160 -#> ds 4 m1 3 2.0 2.022e+00 -0.021757 0.8731 -0.024919 -#> ds 4 m1 7 3.6 4.207e+00 -0.607229 0.9192 -0.660633 -#> ds 4 m1 7 3.8 4.207e+00 -0.407229 0.9192 -0.443044 -#> ds 4 m1 14 7.1 6.912e+00 0.188339 1.0135 0.185828 -#> ds 4 m1 14 6.6 6.912e+00 -0.311661 1.0135 -0.307506 -#> ds 4 m1 28 9.5 9.449e+00 0.050714 1.1309 0.044843 -#> ds 4 m1 28 9.3 9.449e+00 -0.149286 1.1309 -0.132004 -#> ds 4 m1 60 8.3 8.997e+00 -0.697403 1.1083 -0.629230 -#> ds 4 m1 60 9.0 8.997e+00 0.002597 1.1083 0.002343 -#> ds 4 m1 90 6.6 6.697e+00 -0.096928 1.0047 -0.096472 -#> ds 4 m1 90 7.7 6.697e+00 1.003072 1.0047 0.998348 -#> ds 4 m1 120 3.7 4.622e+00 -0.921607 0.9312 -0.989749 -#> ds 4 m1 120 3.5 4.622e+00 -1.121607 0.9312 -1.204537 -#> ds 5 parent 0 110.4 1.045e+02 5.942426 8.1795 0.726502 -#> ds 5 parent 0 112.1 1.045e+02 7.642426 8.1795 0.934338 -#> ds 5 parent 1 93.5 9.739e+01 -3.893915 7.6327 -0.510162 -#> ds 5 parent 1 91.0 9.739e+01 -6.393915 7.6327 -0.837700 -#> ds 5 parent 3 71.0 8.519e+01 -14.188275 6.6891 -2.121098 -#> ds 5 parent 3 89.7 8.519e+01 4.511725 6.6891 0.674487 -#> ds 5 parent 7 60.4 6.684e+01 -6.439546 5.2753 -1.220701 -#> ds 5 parent 7 59.1 6.684e+01 -7.739546 5.2753 -1.467133 -#> ds 5 parent 14 56.5 4.736e+01 9.138979 3.7868 2.413407 -#> ds 5 parent 14 47.0 4.736e+01 -0.361021 3.7868 -0.095338 -#> ds 5 parent 28 30.2 3.033e+01 -0.131178 2.5132 -0.052195 -#> ds 5 parent 28 23.9 3.033e+01 -6.431178 2.5132 -2.558936 -#> ds 5 parent 60 17.0 1.771e+01 -0.705246 1.6243 -0.434177 -#> ds 5 parent 60 18.7 1.771e+01 0.994754 1.6243 0.612409 -#> ds 5 parent 90 11.3 1.180e+01 -0.504856 1.2580 -0.401315 -#> ds 5 parent 90 11.9 1.180e+01 0.095144 1.2580 0.075631 -#> ds 5 parent 120 9.0 7.917e+00 1.083499 1.0571 1.024928 -#> ds 5 parent 120 8.1 7.917e+00 0.183499 1.0571 0.173579 -#> ds 5 m1 0 0.7 3.553e-15 0.700000 0.8588 0.815100 -#> ds 5 m1 1 3.0 3.204e+00 -0.204414 0.8943 -0.228572 -#> ds 5 m1 1 2.6 3.204e+00 -0.604414 0.8943 -0.675845 -#> ds 5 m1 3 5.1 8.586e+00 -3.485889 1.0884 -3.202858 -#> ds 5 m1 3 7.5 8.586e+00 -1.085889 1.0884 -0.997722 -#> ds 5 m1 7 16.5 1.612e+01 0.376855 1.5211 0.247743 -#> ds 5 m1 7 19.0 1.612e+01 2.876855 1.5211 1.891237 -#> ds 5 m1 14 22.9 2.267e+01 0.228264 1.9633 0.116267 -#> ds 5 m1 14 23.2 2.267e+01 0.528264 1.9633 0.269072 -#> ds 5 m1 28 22.2 2.468e+01 -2.480178 2.1050 -1.178211 -#> ds 5 m1 28 24.4 2.468e+01 -0.280178 2.1050 -0.133099 -#> ds 5 m1 60 15.5 1.860e+01 -3.099615 1.6838 -1.840794 -#> ds 5 m1 60 19.8 1.860e+01 1.200385 1.6838 0.712883 -#> ds 5 m1 90 14.9 1.326e+01 1.636345 1.3433 1.218195 -#> ds 5 m1 90 14.2 1.326e+01 0.936345 1.3433 0.697072 -#> ds 5 m1 120 10.9 9.348e+00 1.551535 1.1258 1.378133 -#> ds 5 m1 120 10.4 9.348e+00 1.051535 1.1258 0.934014</div><div class='input'><span class='co'># }</span> +</div><div class='output co'>#> <span class='warning'>Warning: argument is not a function</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].</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_saem_dfop_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_saem_dfop_sfo' not found</span></div><div class='input'><span class='co'># }</span> </div></pre> </div> diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html index 67f26b85..c7b5b6de 100644 --- a/docs/dev/reference/tffm0.html +++ b/docs/dev/reference/tffm0.html @@ -157,7 +157,7 @@ from RxODE." /> optimisations. Therefore this transformation was used originally in mkin. It was later replaced by the <a href='ilr.html'>ilr</a> transformation because the ilr transformed fractions can assumed to follow normal distribution. As the ilr -transformation is not available in RxODE and can therefore not be used in +transformation is not available in <a href='https://nlmixrdevelopment.github.io/RxODE/reference/RxODE.html'>RxODE</a> and can therefore not be used in the nlmixr modelling language, this transformation is currently used for translating mkin models with formation fractions to more than one target compartment for fitting with nlmixr in <a href='nlmixr.mmkin.html'>nlmixr_model</a>. However, |
