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diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 482e48f2..644a35b5 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -163,17 +163,13 @@ <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> <div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="kw"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(fit)</a></code></pre></div> -<pre><code>## Warning in summary.mkinfit(object): Could not calculate correlation; no -## covariance matrix</code></pre> <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> -<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(fit)</a></code></pre></div> -<pre><code>## Warning in summary.mkinfit(fit): Could not calculate correlation; no -## covariance matrix</code></pre> +<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(fit)</a></code></pre></div> <pre><code>## mkin version used for fitting: 0.9.49.4 ## R version used for fitting: 3.6.0 -## Date of fit: Tue May 7 08:09:11 2019 -## Date of summary: Tue May 7 08:09:11 2019 +## Date of fit: Tue May 7 08:37:46 2019 +## Date of summary: Tue May 7 08:37:46 2019 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent @@ -181,7 +177,7 @@ ## ## Model predictions using solution type deSolve ## -## Fitted using 153 model solutions performed in 0.398 s +## Fitted using 389 model solutions performed in 1.018 s ## ## Error model: ## Constant variance @@ -207,25 +203,37 @@ ## m1_0 0 state ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 99.600 NA NA NA -## log_k_parent_sink -3.038 NA NA NA -## log_k_parent_m1 -2.980 NA NA NA -## log_k_m1_sink -5.248 NA NA NA -## sigma 3.126 NA NA NA +## Estimate Std. Error Lower Upper +## parent_0 99.600 1.57000 96.400 102.800 +## log_k_parent_sink -3.038 0.07626 -3.193 -2.883 +## log_k_parent_m1 -2.980 0.04033 -3.062 -2.898 +## log_k_m1_sink -5.248 0.13320 -5.518 -4.977 +## sigma 3.126 0.35850 2.396 3.855 ## ## Parameter correlation: -## No covariance matrix +## parent_0 log_k_parent_sink log_k_parent_m1 +## parent_0 1.000e+00 6.067e-01 -6.372e-02 +## log_k_parent_sink 6.067e-01 1.000e+00 -8.550e-02 +## log_k_parent_m1 -6.372e-02 -8.550e-02 1.000e+00 +## log_k_m1_sink -1.688e-01 -6.252e-01 4.731e-01 +## sigma 1.164e-09 -8.908e-10 1.652e-08 +## log_k_m1_sink sigma +## parent_0 -1.688e-01 1.164e-09 +## log_k_parent_sink -6.252e-01 -8.908e-10 +## log_k_parent_m1 4.731e-01 1.652e-08 +## log_k_m1_sink 1.000e+00 -1.340e-10 +## sigma -1.340e-10 1.000e+00 +## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 99.600000 NA NA NA NA -## k_parent_sink 0.047920 NA NA NA NA -## k_parent_m1 0.050780 NA NA NA NA -## k_m1_sink 0.005261 NA NA NA NA -## sigma 3.126000 NA NA NA NA +## Estimate t value Pr(>t) Lower Upper +## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02 +## k_parent_sink 0.047920 13.110 6.126e-15 0.041030 5.596e-02 +## k_parent_m1 0.050780 24.800 3.269e-23 0.046780 5.512e-02 +## k_m1_sink 0.005261 7.510 6.165e-09 0.004012 6.898e-03 +## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df |