diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2022-05-18 21:26:17 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2022-05-18 21:26:17 +0200 |
| commit | 91c5db736a4d3f2290a0cc5698fb4e35ae7bda59 (patch) | |
| tree | 8e9e34d18f055936172329c1248e6e2ff136bff9 /vignettes/FOCUS_L.html | |
| parent | ea57db98cef3fb882f7bd9656b44f270e7bded38 (diff) | |
Remove outdated comment in FOCUS L vignette, update docs
This also adds the first benchmark results obtained on my laptop system
Diffstat (limited to 'vignettes/FOCUS_L.html')
| -rw-r--r-- | vignettes/FOCUS_L.html | 155 |
1 files changed, 76 insertions, 79 deletions
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 2934bc99..45d264b4 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -1329,6 +1329,7 @@ if (window.hljs) { + <style type="text/css"> .main-container { max-width: 940px; @@ -1350,6 +1351,9 @@ button.code-folding-btn:focus { summary { display: list-item; } +details > summary > p:only-child { + display: inline; +} pre code { padding: 0; } @@ -1513,7 +1517,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 17 November 2016 (rebuilt 2022-03-02)</h4> +<h4 class="date">Last change 17 November 2016 (rebuilt 2022-05-18)</h4> </div> @@ -1533,9 +1537,9 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:09 2022 -## Date of summary: Wed Mar 2 16:28:09 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:22 2022 +## Date of summary: Wed May 18 20:03:22 2022 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -1574,9 +1578,9 @@ summary(m.L1.SFO)</code></pre> ## ## Parameter correlation: ## parent_0 log_k_parent sigma -## parent_0 1.000e+00 6.186e-01 -1.516e-09 -## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 -## sigma -1.516e-09 -3.124e-09 1.000e+00 +## parent_0 1.000e+00 6.186e-01 -1.712e-09 +## log_k_parent 6.186e-01 1.000e+00 -3.237e-09 +## sigma -1.712e-09 -3.237e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1623,27 +1627,25 @@ summary(m.L1.SFO)</code></pre> <pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre> -<pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: -## false convergence (8)</code></pre> -<pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> +<pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE) +plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:09 2022 -## Date of summary: Wed Mar 2 16:28:09 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:22 2022 +## Date of summary: Wed May 18 20:03:22 2022 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 369 model solutions performed in 0.082 s +## Fitted using 357 model solutions performed in 0.07 s ## ## Error model: Constant variance ## @@ -1664,39 +1666,34 @@ summary(m.L1.SFO)</code></pre> ## Fixed parameter values: ## None ## -## -## Warning(s): -## Optimisation did not converge: -## false convergence (8) -## ## Results: ## -## AIC BIC logLik -## 95.88781 99.44929 -43.9439 +## AIC BIC logLik +## 95.88804 99.44953 -43.94402 ## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 92.47 1.2820 89.720 95.220 -## log_alpha 13.78 NaN NaN NaN -## log_beta 16.13 NaN NaN NaN -## sigma 2.78 0.4598 1.794 3.766 +## log_alpha 11.37 NaN NaN NaN +## log_beta 13.72 NaN NaN NaN +## sigma 2.78 0.4621 1.789 3.771 ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.0000000 NaN NaN 0.0001671 +## parent_0 1.0000000 NaN NaN 0.0005548 ## log_alpha NaN 1 NaN NaN ## log_beta NaN NaN 1 NaN -## sigma 0.0001671 NaN NaN 1.0000000 +## sigma 0.0005548 NaN NaN 1.0000000 ## ## 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 9.247e+01 NA NA 89.720 95.220 -## alpha 9.658e+05 NA NA NA NA -## beta 1.010e+07 NA NA NA NA -## sigma 2.780e+00 NA NA 1.794 3.766 +## parent_0 92.47 NA NA 89.720 95.220 +## alpha 87110.00 NA NA NA NA +## beta 911100.00 NA NA NA NA +## sigma 2.78 NA NA 1.789 3.771 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -1704,8 +1701,8 @@ summary(m.L1.SFO)</code></pre> ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.25 24.08 7.25</code></pre> +## DT50 DT90 DT50back +## parent 7.249 24.08 7.249</code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> <p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline"><em>χ</em><sup>2</sup></span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline"><em>χ</em><sup>2</sup></span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> @@ -1739,16 +1736,16 @@ plot(m.L2.FOMC, show_residuals = TRUE, <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:10 2022 -## Date of summary: Wed Mar 2 16:28:10 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:22 2022 +## Date of summary: Wed May 18 20:03:22 2022 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.049 s +## Fitted using 239 model solutions performed in 0.043 s ## ## Error model: Constant variance ## @@ -1783,10 +1780,10 @@ plot(m.L2.FOMC, show_residuals = TRUE, ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09 -## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07 -## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00 +## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.637e-09 +## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07 +## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.387e-07 +## sigma -7.637e-09 -1.617e-07 -1.387e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1817,9 +1814,9 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, <p><img 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:10 2022 -## Date of summary: Wed Mar 2 16:28:10 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:23 2022 +## Date of summary: Wed May 18 20:03:23 2022 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1828,7 +1825,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.137 s +## Fitted using 581 model solutions performed in 0.118 s ## ## Error model: Constant variance ## @@ -1859,18 +1856,18 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.950 9.998e-01 91.5900 96.3100 -## log_k1 3.112 1.842e+03 -4353.0000 4359.0000 +## log_k1 3.113 1.845e+03 -4360.0000 4367.0000 ## log_k2 -1.088 6.285e-02 -1.2370 -0.9394 ## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638 ## sigma 1.414 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10 -## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05 -## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09 -## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09 -## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 1.000e+00 +## parent_0 1.000e+00 6.784e-07 -5.188e-10 2.665e-01 -5.800e-10 +## log_k1 6.784e-07 1.000e+00 1.114e-04 -2.191e-04 -1.029e-05 +## log_k2 -5.188e-10 1.114e-04 1.000e+00 -7.903e-01 5.080e-09 +## g_qlogis 2.665e-01 -2.191e-04 -7.903e-01 1.000e+00 -7.991e-09 +## sigma -5.800e-10 -1.029e-05 5.080e-09 -7.991e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1878,7 +1875,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100 -## k1 22.4800 5.553e-04 4.998e-01 0.0000 Inf +## k1 22.4800 5.544e-04 4.998e-01 0.0000 Inf ## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909 ## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591 ## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960 @@ -1890,8 +1887,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Estimated disappearance times: ## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 0.5335 5.311 1.599 0.03084 2.058</code></pre> -<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> +## parent 0.5335 5.311 1.599 0.03083 2.058</code></pre> +<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p> </div> </div> <div id="laboratory-data-l3" class="section level1"> @@ -1917,9 +1914,9 @@ plot(mm.L3)</code></pre> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:10 2022 -## Date of summary: Wed Mar 2 16:28:10 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:23 2022 +## Date of summary: Wed May 18 20:03:23 2022 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1928,7 +1925,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.08 s +## Fitted using 376 model solutions performed in 0.073 s ## ## Error model: Constant variance ## @@ -1966,11 +1963,11 @@ plot(mm.L3)</code></pre> ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.664e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-07 -## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06 -## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.926e-07 -## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-07 1.000e+00 +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.632e-08 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.145e-07 +## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.021e-06 +## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.925e-07 +## sigma -9.632e-08 7.145e-07 1.021e-06 -7.925e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2025,16 +2022,16 @@ plot(mm.L4)</code></pre> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:11 2022 -## Date of summary: Wed Mar 2 16:28:11 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:23 2022 +## Date of summary: Wed May 18 20:03:23 2022 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.03 s +## Fitted using 142 model solutions performed in 0.027 s ## ## Error model: Constant variance ## @@ -2066,9 +2063,9 @@ plot(mm.L4)</code></pre> ## ## Parameter correlation: ## parent_0 log_k_parent sigma -## parent_0 1.000e+00 5.938e-01 3.387e-07 -## log_k_parent 5.938e-01 1.000e+00 5.830e-07 -## sigma 3.387e-07 5.830e-07 1.000e+00 +## parent_0 1.000e+00 5.938e-01 3.440e-07 +## log_k_parent 5.938e-01 1.000e+00 5.885e-07 +## sigma 3.440e-07 5.885e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2089,16 +2086,16 @@ plot(mm.L4)</code></pre> ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.1.2 -## Date of fit: Wed Mar 2 16:28:11 2022 -## Date of summary: Wed Mar 2 16:28:11 2022 +## R version used for fitting: 4.2.0 +## Date of fit: Wed May 18 20:03:23 2022 +## Date of summary: Wed May 18 20:03:23 2022 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.045 s +## Fitted using 224 model solutions performed in 0.04 s ## ## Error model: Constant variance ## @@ -2133,10 +2130,10 @@ plot(mm.L4)</code></pre> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08 -## sigma -2.468e-07 2.478e-08 5.211e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.563e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 4.066e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 6.818e-08 +## sigma -2.563e-07 4.066e-08 6.818e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. |