diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2015-04-16 09:53:58 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2015-04-16 09:53:58 +0200 |
| commit | d34e5c053794a08cc73c9042ccccfb334ae0f62d (patch) | |
| tree | ec515b04d0e2cf30104e972da80d5a2b43968991 /vignettes/FOCUS_L.html | |
| parent | 42739646dc36ff74d43b638fc2c4f5259496e2b9 (diff) | |
Pending commit of an updated vignette
Diffstat (limited to 'vignettes/FOCUS_L.html')
| -rw-r--r-- | vignettes/FOCUS_L.html | 153 |
1 files changed, 76 insertions, 77 deletions
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index b5228532..96ea70ce 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -214,7 +214,13 @@ hr { report, p. 284:</p> <pre><code class="r">library("mkin") -FOCUS_2006_L1 = data.frame( +</code></pre> + +<pre><code>## Loading required package: minpack.lm +## Loading required package: rootSolve +</code></pre> + +<pre><code class="r">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, @@ -236,17 +242,17 @@ given in the FOCUS report. </p> summary(m.L1.SFO) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:49 2014 -## Date of summary: Thu Dec 18 09:52:49 2014 +## Date of fit: Sat Feb 21 14:44:53 2015 +## Date of summary: Sat Feb 21 14:44:53 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.214 s +## Fitted with method Port using 37 model solutions performed in 0.098 s ## ## Weighting: none ## @@ -323,43 +329,32 @@ 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-4"/> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> 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-5"/> </p> +<p><img 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alt="plot of chunk unnamed-chunk-5"/> </p> <p>For comparison, the FOMC model is fitted as well, and the chi<sup>2</sup> error level is checked.</p> <pre><code class="r">m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE) +summary(m.L1.FOMC, data = FALSE) </code></pre> -<pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge. -## Convergence code is 1 -</code></pre> - -<pre><code class="r">summary(m.L1.FOMC, data = FALSE) -</code></pre> - -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:50 2014 -## Date of summary: Thu Dec 18 09:52:50 2014 -## -## -## Warning: Optimisation by method Port did not converge. -## Convergence code is 1 -## +## Date of fit: Sat Feb 21 14:44:55 2015 +## Date of summary: Sat Feb 21 14:44:55 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 188 model solutions performed in 1.085 s +## Fitted with method Port using 611 model solutions performed in 1.509 s ## ## Weighting: none ## @@ -379,24 +374,28 @@ is checked.</p> ## None ## ## Optimised, transformed parameters: -## Estimate Std. Error Lower Upper t value Pr(>|t|) Pr(>t) -## parent_0 92.47 1.422 89.44 95.50 65.030 8.317e-20 4.158e-20 -## log_alpha 15.43 15.080 -16.71 47.58 1.023 3.224e-01 1.612e-01 -## log_beta 17.78 15.090 -14.37 49.93 1.179 2.569e-01 1.284e-01 +## Estimate Std. Error Lower Upper t value Pr(>|t|) +## parent_0 92.47 1.482 89.31 95.63 62.39000 1.546e-19 +## log_alpha 11.25 598.200 -1264.00 1286.00 0.01880 9.852e-01 +## log_beta 13.60 598.200 -1261.00 1289.00 0.02273 9.822e-01 +## Pr(>t) +## parent_0 7.730e-20 +## log_alpha 4.926e-01 +## log_beta 4.911e-01 ## ## Parameter correlation: ## parent_0 log_alpha log_beta -## parent_0 1.0000 0.1129 0.1112 -## log_alpha 0.1129 1.0000 1.0000 -## log_beta 0.1112 1.0000 1.0000 +## parent_0 1.0000 -0.3016 -0.3016 +## log_alpha -0.3016 1.0000 1.0000 +## log_beta -0.3016 1.0000 1.0000 ## ## Residual standard error: 3.045 on 15 degrees of freedom ## ## Backtransformed parameters: -## Estimate Lower Upper -## parent_0 9.247e+01 8.944e+01 9.550e+01 -## alpha 5.044e+06 5.510e-08 4.618e+20 -## beta 5.276e+07 5.732e-07 4.857e+21 +## Estimate Lower Upper +## parent_0 92.47 89.31 95.63 +## alpha 76830.00 0.00 Inf +## beta 803500.00 0.00 Inf ## ## Chi2 error levels in percent: ## err.min n.optim df @@ -404,8 +403,8 @@ is checked.</p> ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.25 24.08 7.25 +## DT50 DT90 DT50back +## parent 7.249 24.08 7.249 </code></pre> <p>Due to the higher number of parameters, and the lower number of degrees of @@ -443,17 +442,17 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) summary(m.L2.SFO) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:51 2014 -## Date of summary: Thu Dec 18 09:52:51 2014 +## Date of fit: Sat Feb 21 14:44:55 2015 +## Date of summary: Sat Feb 21 14:44:55 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 41 model solutions performed in 0.238 s +## Fitted with method Port using 41 model solutions performed in 0.1 s ## ## Weighting: none ## @@ -527,7 +526,7 @@ plot(m.L2.SFO) mkinresplot(m.L2.SFO) </code></pre> -<p><img 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" alt="plot of chunk unnamed-chunk-9"/> </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 @@ -548,22 +547,22 @@ plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) </code></pre> -<p><img 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" alt="plot of chunk unnamed-chunk-10"/> </p> <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:51 2014 -## Date of summary: Thu Dec 18 09:52:51 2014 +## Date of fit: Sat Feb 21 14:44:55 2015 +## Date of summary: Sat Feb 21 14:44:55 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.464 s +## Fitted with method Port using 81 model solutions performed in 0.201 s ## ## Weighting: none ## @@ -622,7 +621,7 @@ 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-11"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </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 @@ -634,15 +633,15 @@ parameters.</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> <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:56 2014 -## Date of summary: Thu Dec 18 09:52:56 2014 +## Date of fit: Sat Feb 21 14:44:57 2015 +## Date of summary: Sat Feb 21 14:44:57 2015 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -651,7 +650,7 @@ plot(m.L2.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 1.986 s +## Fitted with method Port using 336 model solutions performed in 0.856 s ## ## Weighting: none ## @@ -723,22 +722,22 @@ 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-14"/> </p> <pre><code class="r">summary(m.L3.SFO) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:56 2014 -## Date of summary: Thu Dec 18 09:52:56 2014 +## Date of fit: Sat Feb 21 14:44:57 2015 +## Date of summary: Sat Feb 21 14:44:57 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 43 model solutions performed in 0.246 s +## Fitted with method Port using 43 model solutions performed in 0.109 s ## ## Weighting: none ## @@ -809,22 +808,22 @@ does not fit very well. </p> plot(m.L3.FOMC) </code></pre> -<p><img 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" alt="plot of chunk unnamed-chunk-15"/> </p> <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:57 2014 -## Date of summary: Thu Dec 18 09:52:57 2014 +## Date of fit: Sat Feb 21 14:44:58 2015 +## Date of summary: Sat Feb 21 14:44:58 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 83 model solutions performed in 0.475 s +## Fitted with method Port using 83 model solutions performed in 0.203 s ## ## Weighting: none ## @@ -882,15 +881,15 @@ considerably:</p> plot(m.L3.DFOP) </code></pre> -<p><img 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" alt="plot of chunk unnamed-chunk-16"/> </p> <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:58 2014 -## Date of summary: Thu Dec 18 09:52:58 2014 +## Date of fit: Sat Feb 21 14:44:58 2015 +## Date of summary: Sat Feb 21 14:44:58 2015 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -899,7 +898,7 @@ plot(m.L3.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.813 s +## Fitted with method Port using 137 model solutions performed in 0.346 s ## ## Weighting: none ## @@ -980,22 +979,22 @@ 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-18"/> </p> <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:58 2014 -## Date of summary: Thu Dec 18 09:52:58 2014 +## Date of fit: Sat Feb 21 14:44:58 2015 +## Date of summary: Sat Feb 21 14:44:58 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.264 s +## Fitted with method Port using 46 model solutions performed in 0.109 s ## ## Weighting: none ## @@ -1055,22 +1054,22 @@ fits very well. </p> plot(m.L4.FOMC) </code></pre> -<p><img 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alt="plot of chunk unnamed-chunk-19"/> </p> <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.34 +<pre><code>## mkin version: 0.9.35 ## R version: 3.1.2 -## Date of fit: Thu Dec 18 09:52:59 2014 -## Date of summary: Thu Dec 18 09:52:59 2014 +## Date of fit: Sat Feb 21 14:44:58 2015 +## Date of summary: Sat Feb 21 14:44:58 2015 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.375 s +## Fitted with method Port using 66 model solutions performed in 0.161 s ## ## Weighting: none ## |