diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2016-06-28 10:32:31 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2016-06-28 10:32:31 +0200 |
| commit | a7600ca6d4e5dfa62a16102f5a965f5e9891cf28 (patch) | |
| tree | e68a26806a807ab9c1ee69a6b0a646ae7033ddcb /vignettes/FOCUS_L.html | |
| parent | 7faf98ac5475bb2041d7e434478c58c2f2cec0fd (diff) | |
Bump version for new release, rebuild static docs
The test test_FOMC_ill-defined leads to errors on several
architectures/distributions, as apparent from CRAN checks,
so we need a new release.
Static documentation rebuilt by staticdocs::build_site()
Diffstat (limited to 'vignettes/FOCUS_L.html')
| -rw-r--r-- | vignettes/FOCUS_L.html | 56 |
1 files changed, 28 insertions, 28 deletions
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 05b9bdbd..4c509eb2 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -233,17 +233,17 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <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: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:32 2016 -## Date of summary: Tue Jun 28 08:19:32 2016 +## Date of fit: Tue Jun 28 10:30:10 2016 +## Date of summary: Tue Jun 28 10:30:10 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.245 s +## Fitted with method Port using 37 model solutions performed in 0.249 s ## ## Weighting: none ## @@ -326,10 +326,10 @@ summary(m.L1.SFO)</code></pre> <pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> <p><img 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" alt /><!-- --></p> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:34 2016 -## Date of summary: Tue Jun 28 08:19:34 2016 +## Date of fit: Tue Jun 28 10:30:12 2016 +## Date of summary: Tue Jun 28 10:30:12 2016 ## ## ## Warning: Optimisation by method Port did not converge. @@ -341,7 +341,7 @@ summary(m.L1.SFO)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 188 model solutions performed in 1.216 s +## Fitted with method Port using 188 model solutions performed in 1.227 s ## ## Weighting: none ## @@ -423,17 +423,17 @@ plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> <p><img src="data:image/png;base64,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" alt /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:36 2016 -## Date of summary: Tue Jun 28 08:19:36 2016 +## Date of fit: Tue Jun 28 10:30:14 2016 +## Date of summary: Tue Jun 28 10:30:14 2016 ## ## Equations: ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.537 s +## Fitted with method Port using 81 model solutions performed in 0.543 s ## ## Weighting: none ## @@ -493,10 +493,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> <p><img src="data:image/png;base64,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" alt /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:39 2016 -## Date of summary: Tue Jun 28 08:19:39 2016 +## Date of fit: Tue Jun 28 10:30:17 2016 +## Date of summary: Tue Jun 28 10:30:17 2016 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -505,7 +505,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 2.267 s +## Fitted with method Port using 336 model solutions performed in 2.274 s ## ## Weighting: none ## @@ -582,10 +582,10 @@ plot(mm.L3)</code></pre> <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <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: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:41 2016 -## Date of summary: Tue Jun 28 08:19:42 2016 +## Date of fit: Tue Jun 28 10:30:19 2016 +## Date of summary: Tue Jun 28 10:30:20 2016 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -594,7 +594,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.924 s +## Fitted with method Port using 137 model solutions performed in 0.898 s ## ## Weighting: none ## @@ -682,17 +682,17 @@ plot(mm.L4)</code></pre> <p><img 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" alt /><!-- --></p> <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: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:42 2016 -## Date of summary: Tue Jun 28 08:19:43 2016 +## Date of fit: Tue Jun 28 10:30:20 2016 +## Date of summary: Tue Jun 28 10:30:21 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.307 s +## Fitted with method Port using 46 model solutions performed in 0.302 s ## ## Weighting: none ## @@ -742,17 +742,17 @@ plot(mm.L4)</code></pre> ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.43.9000 +<pre><code>## mkin version: 0.9.44 ## R version: 3.3.1 -## Date of fit: Tue Jun 28 08:19:43 2016 -## Date of summary: Tue Jun 28 08:19:43 2016 +## Date of fit: Tue Jun 28 10:30:21 2016 +## Date of summary: Tue Jun 28 10:30:21 2016 ## ## Equations: ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.414 s +## Fitted with method Port using 66 model solutions performed in 0.425 s ## ## Weighting: none ## |