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Diffstat (limited to 'vignettes/FOCUS_L.html')
| -rw-r--r-- | vignettes/FOCUS_L.html | 175 |
1 files changed, 84 insertions, 91 deletions
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+hljs.initHighlightingOnLoad(); +</script> + +<!-- MathJax scripts --> +<script type="text/javascript" src="https://c328740.ssl.cf1.rackcdn.com/mathjax/2.0-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> </head> @@ -193,13 +192,7 @@ hr { report, p. 284</p> <pre><code class="r">library("mkin") -</code></pre> - -<pre><code>## Loading required package: minpack.lm -## Loading required package: rootSolve -</code></pre> - -<pre><code class="r">FOCUS_2006_L1 = data.frame( +FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, @@ -229,10 +222,10 @@ FOCUS report.</p> summary(m.L1.SFO) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:12 2014 -## Date of summary: Mon Jul 14 12:36:12 2014 +## Date of fit: Mon Jul 14 19:59:26 2014 +## Date of summary: Mon Jul 14 19:59:26 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent @@ -315,14 +308,14 @@ summary(m.L1.SFO) <pre><code class="r">plot(m.L1.SFO) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-5"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> <p>The residual plot can be easily obtained by</p> <pre><code class="r">mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") </code></pre> -<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-6"/> </p> +<p><img 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alt="plot of chunk unnamed-chunk-6"/> </p> <p>For comparison, the FOMC model is fitted as well, and the chi<sup>2</sup> error level is checked.</p> @@ -331,10 +324,10 @@ is checked.</p> summary(m.L1.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:12 2014 -## Date of summary: Mon Jul 14 12:36:12 2014 +## Date of fit: Mon Jul 14 19:59:26 2014 +## Date of summary: Mon Jul 14 19:59:26 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent @@ -423,10 +416,10 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) summary(m.L2.SFO) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:12 2014 -## Date of summary: Mon Jul 14 12:36:13 2014 +## Date of fit: Mon Jul 14 19:59:27 2014 +## Date of summary: Mon Jul 14 19:59:27 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent @@ -506,7 +499,7 @@ plot(m.L2.SFO) mkinresplot(m.L2.SFO) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-10"/> </p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at @@ -527,15 +520,15 @@ plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-11"/> </p> <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:13 2014 -## Date of summary: Mon Jul 14 12:36:13 2014 +## Date of fit: Mon Jul 14 19:59:28 2014 +## Date of summary: Mon Jul 14 19:59:28 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent @@ -600,7 +593,7 @@ experimental error has to be assumed in order to explain the data.</p> plot(m.L2.DFOP) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-12"/> </p> +<p><img 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alt="plot of chunk unnamed-chunk-12"/> </p> <p>Here, the default starting parameters for the DFOP model obviously do not lead to a reasonable solution. Therefore the fit is repeated with different starting @@ -612,15 +605,15 @@ parameters.</p> plot(m.L2.DFOP) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-13"/> </p> +<p><img 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alt="plot of chunk unnamed-chunk-13"/> </p> <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:13 2014 -## Date of summary: Mon Jul 14 12:36:13 2014 +## Date of fit: Mon Jul 14 19:59:28 2014 +## Date of summary: Mon Jul 14 19:59:28 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent @@ -698,15 +691,15 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) plot(m.L3.SFO) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-15"/> </p> <pre><code class="r">summary(m.L3.SFO) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:14 2014 -## Date of summary: Mon Jul 14 12:36:14 2014 +## Date of fit: Mon Jul 14 19:59:29 2014 +## Date of summary: Mon Jul 14 19:59:29 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent @@ -783,15 +776,15 @@ does not fit very well. </p> plot(m.L3.FOMC) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-16"/> </p> <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:14 2014 -## Date of summary: Mon Jul 14 12:36:14 2014 +## Date of fit: Mon Jul 14 19:59:29 2014 +## Date of summary: Mon Jul 14 19:59:29 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent @@ -855,15 +848,15 @@ considerably:</p> plot(m.L3.DFOP) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-17"/> </p> <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:14 2014 -## Date of summary: Mon Jul 14 12:36:14 2014 +## Date of fit: Mon Jul 14 19:59:30 2014 +## Date of summary: Mon Jul 14 19:59:30 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent @@ -945,15 +938,15 @@ FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) plot(m.L4.SFO) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-19"/> </p> <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:15 2014 -## Date of summary: Mon Jul 14 12:36:15 2014 +## Date of fit: Mon Jul 14 19:59:30 2014 +## Date of summary: Mon Jul 14 19:59:30 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent @@ -1019,15 +1012,15 @@ fits very well. </p> plot(m.L4.FOMC) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-20"/> </p> <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 12:36:15 2014 -## Date of summary: Mon Jul 14 12:36:15 2014 +## Date of fit: Mon Jul 14 19:59:31 2014 +## Date of summary: Mon Jul 14 19:59:31 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent |