diff options
Diffstat (limited to 'vignettes/FOCUS_D.html')
| -rw-r--r-- | vignettes/FOCUS_D.html | 50 |
1 files changed, 21 insertions, 29 deletions
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 6573cc7a..b1ea64ea 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -215,13 +215,7 @@ library we look a the data. We have observed concentrations in the column named named <code>parent</code> and <code>m1</code>.</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">print(FOCUS_2006_D) +print(FOCUS_2006_D) </code></pre> <pre><code>## name time value @@ -276,7 +270,7 @@ kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If -the <code>ccSolve</code> package is installed and functional, the differential equation model +the gcc compiler is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <pre><code class="r">SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")) @@ -312,7 +306,7 @@ using the <code>plot</code> method for <code>mkinfit</code> objects.</p> <pre><code class="r">mkinparplot(fit) </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>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> @@ -321,9 +315,9 @@ objects.</p> </code></pre> <pre><code>## mkin version: 0.9.36 -## R version: 3.2.0 -## Date of fit: Fri Jun 5 14:20:31 2015 -## Date of summary: Fri Jun 5 14:20:31 2015 +## R version: 3.2.1 +## Date of fit: Sun Jun 21 01:47:59 2015 +## Date of summary: Sun Jun 21 01:47:59 2015 ## ## Equations: ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent @@ -331,7 +325,7 @@ objects.</p> ## ## Model predictions using solution type deSolve ## -## Fitted with method Port using 153 model solutions performed in 0.621 s +## Fitted with method Port using 153 model solutions performed in 0.698 s ## ## Weighting: none ## @@ -353,17 +347,12 @@ objects.</p> ## value type ## m1_0 0 state ## -## Optimised, transformed parameters: -## Estimate Std. Error Lower Upper t value Pr(>|t|) -## parent_0 99.600 1.61400 96.330 102.900 61.72 4.048e-38 -## log_k_parent_sink -3.038 0.07826 -3.197 -2.879 -38.82 5.601e-31 -## log_k_parent_m1 -2.980 0.04124 -3.064 -2.897 -72.27 1.446e-40 -## log_k_m1_sink -5.248 0.13610 -5.523 -4.972 -38.56 7.087e-31 -## Pr(>t) -## parent_0 2.024e-38 -## log_k_parent_sink 2.800e-31 -## log_k_parent_m1 7.228e-41 -## log_k_m1_sink 3.543e-31 +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 99.600 1.61400 96.330 102.900 +## log_k_parent_sink -3.038 0.07826 -3.197 -2.879 +## log_k_parent_m1 -2.980 0.04124 -3.064 -2.897 +## log_k_m1_sink -5.248 0.13610 -5.523 -4.972 ## ## Parameter correlation: ## parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink @@ -375,11 +364,14 @@ objects.</p> ## Residual standard error: 3.211 on 36 degrees of freedom ## ## Backtransformed parameters: -## Estimate Lower Upper -## parent_0 99.600000 96.330000 1.029e+02 -## k_parent_sink 0.047920 0.040890 5.616e-02 -## k_parent_m1 0.050780 0.046700 5.521e-02 -## k_m1_sink 0.005261 0.003992 6.933e-03 +## 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 61.720 2.024e-38 96.330000 1.029e+02 +## k_parent_sink 0.047920 12.780 3.050e-15 0.040890 5.616e-02 +## k_parent_m1 0.050780 24.250 3.407e-24 0.046700 5.521e-02 +## k_m1_sink 0.005261 7.349 5.758e-09 0.003992 6.933e-03 ## ## Chi2 error levels in percent: ## err.min n.optim df |