Updates in the FOCUS Z vignette

To make the text consistent with the results produced by the current mkin version. Static documentation articles rebuilt by pkgdown::build_articles()
author: Johannes Ranke <jranke@uni-bremen.de> 2018-01-16 06:11:54 +0100
committer: Johannes Ranke <jranke@uni-bremen.de> 2018-01-16 06:11:54 +0100
commit: f18213520f20aba947093e53113c44b689e8b98d (patch)
tree: 6b7d1e41c1c1f03f23dc52af5f1b03f0102b05e7
parent: 373d98038c514c5152478127a8a2b9b390ee1b58 (diff)
11 files changed, 122 insertions, 144 deletions
diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html
index e3f87eae..d9dd8ad5 100644
--- a/docs/articles/FOCUS_D.html
+++ b/docs/articles/FOCUS_D.html
@@ -77,7 +77,7 @@
       <h1>Example evaluation of FOCUS Example Dataset D</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-14</h4>
+            <h4 class="date">2018-01-16</h4>
           </div>
 
     
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index bc2b9947..42ec2df1 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -77,7 +77,7 @@
       <h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-14</h4>
+            <h4 class="date">2018-01-16</h4>
           </div>
 
     
@@ -100,15 +100,15 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <span class="kw">summary</span>(m.L1.SFO)</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:12 2018 
-## Date of summary: Sun Jan 14 18:36:12 2018 
+## Date of fit:     Tue Jan 16 06:11:06 2018 
+## Date of summary: Tue Jan 16 06:11:06 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.258 s
+## Fitted with method Port using 37 model solutions performed in 0.245 s
 ## 
 ## Weighting: none
 ## 
@@ -193,8 +193,8 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L1.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:13 2018 
-## Date of summary: Sun Jan 14 18:36:13 2018 
+## Date of fit:     Tue Jan 16 06:11:07 2018 
+## Date of summary: Tue Jan 16 06:11:07 2018 
 ## 
 ## 
 ## Warning: Optimisation by method Port did not converge.
@@ -206,7 +206,7 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 155 model solutions performed in 0.452 s
+## Fitted with method Port using 155 model solutions performed in 0.424 s
 ## 
 ## Weighting: none
 ## 
@@ -293,15 +293,15 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:14 2018 
-## Date of summary: Sun Jan 14 18:36:14 2018 
+## Date of fit:     Tue Jan 16 06:11:08 2018 
+## Date of summary: Tue Jan 16 06:11:08 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 81 model solutions performed in 0.177 s
+## Fitted with method Port using 81 model solutions performed in 0.168 s
 ## 
 ## Weighting: none
 ## 
@@ -364,8 +364,8 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.DFOP, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:15 2018 
-## Date of summary: Sun Jan 14 18:36:15 2018 
+## Date of fit:     Tue Jan 16 06:11:09 2018 
+## Date of summary: Tue Jan 16 06:11:09 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -374,7 +374,7 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 336 model solutions performed in 0.808 s
+## Fitted with method Port using 336 model solutions performed in 0.774 s
 ## 
 ## Weighting: none
 ## 
@@ -456,8 +456,8 @@ mm.L3 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:16 2018 
-## Date of summary: Sun Jan 14 18:36:16 2018 
+## Date of fit:     Tue Jan 16 06:11:10 2018 
+## Date of summary: Tue Jan 16 06:11:10 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -466,7 +466,7 @@ mm.L3 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 137 model solutions performed in 0.3 s
+## Fitted with method Port using 137 model solutions performed in 0.287 s
 ## 
 ## Weighting: none
 ## 
@@ -557,15 +557,15 @@ mm.L4 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:16 2018 
-## Date of summary: Sun Jan 14 18:36:17 2018 
+## Date of fit:     Tue Jan 16 06:11:10 2018 
+## Date of summary: Tue Jan 16 06:11:10 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.097 s
+## Fitted with method Port using 46 model solutions performed in 0.094 s
 ## 
 ## Weighting: none
 ## 
@@ -617,15 +617,15 @@ mm.L4 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
 <pre><code>## mkin version:    0.9.47.1 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 18:36:17 2018 
-## Date of summary: Sun Jan 14 18:36:17 2018 
+## Date of fit:     Tue Jan 16 06:11:10 2018 
+## Date of summary: Tue Jan 16 06:11:10 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 66 model solutions performed in 0.138 s
+## Fitted with method Port using 66 model solutions performed in 0.139 s
 ## 
 ## Weighting: none
 ## 
diff --git a/docs/articles/FOCUS_Z.R b/docs/articles/FOCUS_Z.R
index 65ddbeba..4d2dffca 100644
--- a/docs/articles/FOCUS_Z.R
+++ b/docs/articles/FOCUS_Z.R
@@ -1,5 +1,6 @@
 ## ---- include = FALSE----------------------------------------------------
 require(knitr)
+options(digits = 5)
 opts_chunk$set(engine='R', tidy = FALSE)
 
 ## ---- echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7-------------
diff --git a/docs/articles/FOCUS_Z.html b/docs/articles/FOCUS_Z.html
index 7a37c66d..a5cfc616 100644
--- a/docs/articles/FOCUS_Z.html
+++ b/docs/articles/FOCUS_Z.html
@@ -77,7 +77,7 @@
       <h1>Example evaluation of FOCUS dataset Z</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-14</h4>
+            <h4 class="date">2018-01-16</h4>
           </div>
 
     
@@ -115,17 +115,12 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.2a)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="672"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.2a, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
-<pre><code>##               Estimate se_notrans      t value       Pr(&gt;t)      Lower
-## Z0_0      9.701488e+01 3.55313531 2.730402e+01 1.679194e-21 91.4013833
-## k_Z0_sink 6.213452e-10 0.22689429 2.738479e-09 5.000000e-01  0.0000000
-## k_Z0_Z1   2.236006e+00 0.16507349 1.354552e+01 7.393893e-14  1.8374087
-## k_Z1_sink 4.821248e-01 0.06585366 7.321154e+00 3.551981e-08  0.4005976
-##                 Upper
-## Z0_0      102.6283792
-## k_Z0_sink         Inf
-## k_Z0_Z1     2.7210739
-## k_Z1_sink   0.5802439</code></pre>
-<p>As obvious from the parameter summary (the  component of the summary), the kinetic rate constant from parent compound Z to sink is negligible. Accordingly, the exact magnitude of the fitted parameter  is ill-defined and the covariance matrix is not returned (not shown, would be visible in the complete summary). This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p>
+<pre><code>##             Estimate se_notrans    t value     Pr(&gt;t)   Lower     Upper
+## Z0_0      9.7015e+01   3.553135 2.7304e+01 1.6792e-21 91.4014 102.62838
+## k_Z0_sink 6.2135e-10   0.226894 2.7385e-09 5.0000e-01  0.0000       Inf
+## k_Z0_Z1   2.2360e+00   0.165073 1.3546e+01 7.3939e-14  1.8374   2.72107
+## k_Z1_sink 4.8212e-01   0.065854 7.3212e+00 3.5520e-08  0.4006   0.58024</code></pre>
+<p>As obvious from the parameter summary (the  component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p>
 <p>A similar result can be obtained when formation fractions are used in the model formulation:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.2a.ff &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>),
                    <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
@@ -135,13 +130,13 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.2a.ff)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="672"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.2a.ff, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
-<pre><code>##              Estimate se_notrans   t value       Pr(&gt;t) Lower Upper
-## Z0_0       97.0148813 3.55314638 27.303936 1.679331e-21    NA    NA
-## k_Z0        2.2360064 0.21684717 10.311439 3.661734e-11    NA    NA
-## k_Z1        0.4821248 0.06585372  7.321147 3.552046e-08    NA    NA
-## f_Z0_to_Z1  1.0000000 0.10147344  9.854795 9.707117e-11    NA    NA</code></pre>
-<p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Again, the covariance matrix is not returned as the model is overparameterised.</p>
-<p>The simplified model is obtained by setting the list component  to .</p>
+<pre><code>##            Estimate se_notrans t value     Pr(&gt;t) Lower Upper
+## Z0_0       97.01488   3.553146 27.3039 1.6793e-21    NA    NA
+## k_Z0        2.23601   0.216847 10.3114 3.6617e-11    NA    NA
+## k_Z1        0.48212   0.065854  7.3211 3.5520e-08    NA    NA
+## f_Z0_to_Z1  1.00000   0.101473  9.8548 9.7071e-11    NA    NA</code></pre>
+<p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p>
+<p>A simplified model is obtained by removing the pathway to the sink. </p>
 <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>),
                <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div>
@@ -150,10 +145,10 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.3</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="672"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.3</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
-<pre><code>##        Estimate se_notrans  t value       Pr(&gt;t)      Lower       Upper
-## Z0_0 97.0148816 2.68177104 36.17568 2.363590e-25 91.5215232 102.5082401
-## k_Z0  2.2360064 0.14686238 15.22518 2.247007e-15  1.9545318   2.5580166
-## k_Z1  0.4821248 0.04268711 11.29439 3.068559e-12  0.4021552   0.5779966</code></pre>
+<pre><code>##      Estimate se_notrans t value     Pr(&gt;t)    Lower   Upper
+## Z0_0 97.01488   2.681771  36.176 2.3636e-25 91.52152 102.508
+## k_Z0  2.23601   0.146862  15.225 2.2470e-15  1.95453   2.558
+## k_Z1  0.48212   0.042687  11.294 3.0686e-12  0.40216   0.578</code></pre>
 <p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p>
 </div>
 <div id="metabolites-z2-and-z3" class="section level1">
@@ -182,34 +177,27 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.FOCUS)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="672"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.FOCUS, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
-<pre><code>##               Estimate se_notrans   t value       Pr(&gt;t)       Lower
-## Z0_0       96.84024413 2.05881366 47.036916 5.572278e-44 92.70685169
-## k_Z0        2.21540096 0.11812759 18.754306 7.736886e-25  1.99050408
-## k_Z1        0.47835870 0.02929353 16.329843 3.344317e-22  0.42303496
-## k_Z2        0.45166296 0.04418624 10.221801 3.036447e-14  0.37106537
-## k_Z3        0.05868971 0.01428961  4.107158 7.256030e-05  0.03598292
-## f_Z2_to_Z3  0.47147387 0.05702672  8.267596 2.779011e-11  0.36029541
-##                   Upper
-## Z0_0       100.97363656
-## k_Z0         2.46570780
-## k_Z1         0.54091758
-## k_Z2         0.54976682
-## k_Z3         0.09572548
-## f_Z2_to_Z3   0.58555627</code></pre>
+<pre><code>##            Estimate se_notrans t value     Pr(&gt;t)     Lower      Upper
+## Z0_0       96.84024   2.058814 47.0369 5.5723e-44 92.706852 100.973637
+## k_Z0        2.21540   0.118128 18.7543 7.7369e-25  1.990504   2.465708
+## k_Z1        0.47836   0.029294 16.3298 3.3443e-22  0.423035   0.540918
+## k_Z2        0.45166   0.044186 10.2218 3.0364e-14  0.371065   0.549767
+## k_Z3        0.05869   0.014290  4.1072 7.2560e-05  0.035983   0.095725
+## f_Z2_to_Z3  0.47147   0.057027  8.2676 2.7790e-11  0.360295   0.585556</code></pre>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.FOCUS)</code></pre></div>
 <pre><code>## $ff
-##     Z2_Z3   Z2_sink 
-## 0.4714739 0.5285261 
+##   Z2_Z3 Z2_sink 
+## 0.47147 0.52853 
 ## 
 ## $SFORB
 ## logical(0)
 ## 
 ## $distimes
-##          DT50      DT90
-## Z0  0.3128766  1.039354
-## Z1  1.4490113  4.813512
-## Z2  1.5346558  5.098016
-## Z3 11.8103701 39.233200</code></pre>
+##        DT50    DT90
+## Z0  0.31288  1.0394
+## Z1  1.44901  4.8135
+## Z2  1.53466  5.0980
+## Z3 11.81037 39.2332</code></pre>
 <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p>
 </div>
 <div id="using-the-sforb-model" class="section level1">
@@ -274,18 +262,18 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.mkin.5a)</code></pre></div>
 <pre><code>## $ff
 ##   Z0_free_Z1        Z1_Z2      Z2_sink   Z2_Z3_free Z3_free_sink 
-##    1.0000000    1.0000000    0.4634423    0.5365577    1.0000000 
+##      1.00000      1.00000      0.46344      0.53656      1.00000 
 ## 
 ## $SFORB
-##       Z0_b1       Z0_b2       Z3_b1       Z3_b2 
-## 2.447137325 0.007512576 0.080007563 0.000000000 
+##     Z0_b1     Z0_b2     Z3_b1     Z3_b2 
+## 2.4471373 0.0075126 0.0800076 0.0000000 
 ## 
 ## $distimes
-##         DT50     DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2
-## Z0 0.3042974 1.184810  0.2832482   92.26492         NA         NA
-## Z1 1.5147780 5.031984         NA         NA         NA         NA
-## Z2 1.6413852 5.452564         NA         NA         NA         NA
-## Z3        NA       NA         NA         NA   8.663521        Inf</code></pre>
+##      DT50   DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2
+## Z0 0.3043 1.1848    0.28325     92.265         NA         NA
+## Z1 1.5148 5.0320         NA         NA         NA         NA
+## Z2 1.6414 5.4526         NA         NA         NA         NA
+## Z3     NA     NA         NA         NA     8.6635        Inf</code></pre>
 <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p>
 </div>
 <div id="references" class="section level1">
diff --git a/docs/articles/FOCUS_Z.pdf b/docs/articles/FOCUS_Z.pdf
index 8a87e874..975ad17f 100644
--- a/docs/articles/FOCUS_Z.pdf
+++ b/docs/articles/FOCUS_Z.pdf
diff --git a/docs/articles/compiled_models.html b/docs/articles/compiled_models.html
index 4c850d14..d5d29a1a 100644
--- a/docs/articles/compiled_models.html
+++ b/docs/articles/compiled_models.html
@@ -77,7 +77,7 @@
       <h1>Performance benefit by using compiled model definitions in mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-14</h4>
+            <h4 class="date">2018-01-16</h4>
           </div>
 
     
@@ -115,9 +115,9 @@ SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mki
 }</code></pre></div>
 <pre><code>## Lade nötiges Paket: rbenchmark</code></pre>
 <pre><code>##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   2.083    1.000     2.084    0.000
-## 1 deSolve, not compiled            3  14.501    6.962    14.472    0.016
-## 2      Eigenvalue based            3   2.566    1.232     2.564    0.000
+## 3     deSolve, compiled            3   1.940    1.000     1.940        0
+## 1 deSolve, not compiled            3  13.865    7.147    13.864        0
+## 2      Eigenvalue based            3   2.427    1.251     2.428        0
 ##   user.child sys.child
 ## 3          0         0
 ## 1          0         0
@@ -146,8 +146,8 @@ SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mki
 }</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
 <pre><code>##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   3.534    1.000     3.532    0.000
-## 1 deSolve, not compiled            3  29.973    8.481    29.960    0.004
+## 2     deSolve, compiled            3   3.432    1.000     3.428        0
+## 1 deSolve, not compiled            3  28.844    8.404    28.840        0
 ##   user.child sys.child
 ## 2          0         0
 ## 1          0         0</code></pre>
diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 13df8a5d..b70918ab 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -77,7 +77,7 @@
       <h1>Introduction to mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-14</h4>
+            <h4 class="date">2018-01-16</h4>
           </div>
 
     
diff --git a/docs/articles/twa.html b/docs/articles/twa.html
index f141ce63..086c8593 100644
--- a/docs/articles/twa.html
+++ b/docs/articles/twa.html
@@ -77,7 +77,7 @@
       <h1>Calculation of time weighted average concentrations with mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-14</h4>
+            <h4 class="date">2018-01-16</h4>
           </div>
 
     
diff --git a/vignettes/FOCUS_Z.Rmd b/vignettes/FOCUS_Z.Rmd
index b1ac7d42..951d5eee 100644
--- a/vignettes/FOCUS_Z.Rmd
+++ b/vignettes/FOCUS_Z.Rmd
@@ -20,6 +20,7 @@ vignette: >
 
 ```{r, include = FALSE}
 require(knitr)
+options(digits = 5)
 opts_chunk$set(engine='R', tidy = FALSE)
 ```
 
@@ -64,11 +65,10 @@ summary(m.Z.2a, data = FALSE)$bpar
 
 As obvious from the parameter summary (the \texttt{bpar} component of the
 summary), the kinetic rate constant from parent compound Z to sink
-is negligible. Accordingly, the exact magnitude of the fitted parameter
-\texttt{log k\_Z0\_sink} is ill-defined and the covariance matrix is not
-returned (not shown, would be visible in the complete summary).
-This suggests, in agreement with the analysis in the FOCUS kinetics report, to
-simplify the model by removing the pathway to sink.
+is very small and the t-test for this parameter suggests that it is
+not significantly different from zero. This suggests, in agreement with the
+analysis in the FOCUS kinetics report, to simplify the model by removing the
+pathway to sink.
 
 A similar result can be obtained when formation fractions are used in the model
 formulation:
@@ -85,11 +85,12 @@ summary(m.Z.2a.ff, data = FALSE)$bpar
 
 Here, the ilr transformed formation fraction fitted in the model takes a very
 large value, and the backtransformed formation fraction from parent Z to Z1 is
-practically unity. Again, the covariance matrix is not returned as the model is
+practically unity. Here, the covariance matrix used for the calculation
+of confidence intervals is not returned as the model is
 overparameterised.
 
-The simplified model is obtained by setting the list component \texttt{sink} to
-\texttt{FALSE}.\footnote{If the model formulation without formation fractions
+A simplified model is obtained by removing the pathway to the sink.
+\footnote{If the model formulation without formation fractions
 is used, the same effect can be obtained by fixing the parameter \texttt{k\_Z\_sink}
 to a value of zero.}
 
diff --git a/vignettes/FOCUS_Z.html b/vignettes/FOCUS_Z.html
index 1428ea85..95a67f94 100644
--- a/vignettes/FOCUS_Z.html
+++ b/vignettes/FOCUS_Z.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2018-01-14" />
+<meta name="date" content="2018-01-16" />
 
 <title>Example evaluation of FOCUS dataset Z</title>
 
@@ -234,7 +234,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS dataset Z</h1>
 <h4 class="author"><em>Johannes Ranke</em></h4>
-<h4 class="date"><em>2018-01-14</em></h4>
+<h4 class="date"><em>2018-01-16</em></h4>
 
 </div>
 
@@ -269,17 +269,12 @@ FOCUS_2006_Z_mkin &lt;- mkin_wide_to_long(FOCUS_2006_Z)</code></pre>
 plot_sep(m.Z.2a)</code></pre>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 <pre class="r"><code>summary(m.Z.2a, data = FALSE)$bpar</code></pre>
-<pre><code>##               Estimate se_notrans      t value       Pr(&gt;t)      Lower
-## Z0_0      9.701488e+01 3.55313531 2.730402e+01 1.679194e-21 91.4013833
-## k_Z0_sink 6.213452e-10 0.22689429 2.738479e-09 5.000000e-01  0.0000000
-## k_Z0_Z1   2.236006e+00 0.16507349 1.354552e+01 7.393893e-14  1.8374087
-## k_Z1_sink 4.821248e-01 0.06585366 7.321154e+00 3.551981e-08  0.4005976
-##                 Upper
-## Z0_0      102.6283792
-## k_Z0_sink         Inf
-## k_Z0_Z1     2.7210739
-## k_Z1_sink   0.5802439</code></pre>
-<p>As obvious from the parameter summary (the  component of the summary), the kinetic rate constant from parent compound Z to sink is negligible. Accordingly, the exact magnitude of the fitted parameter  is ill-defined and the covariance matrix is not returned (not shown, would be visible in the complete summary). This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p>
+<pre><code>##             Estimate se_notrans    t value     Pr(&gt;t)   Lower     Upper
+## Z0_0      9.7015e+01   3.553135 2.7304e+01 1.6792e-21 91.4014 102.62838
+## k_Z0_sink 6.2135e-10   0.226894 2.7385e-09 5.0000e-01  0.0000       Inf
+## k_Z0_Z1   2.2360e+00   0.165073 1.3546e+01 7.3939e-14  1.8374   2.72107
+## k_Z1_sink 4.8212e-01   0.065854 7.3212e+00 3.5520e-08  0.4006   0.58024</code></pre>
+<p>As obvious from the parameter summary (the  component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p>
 <p>A similar result can be obtained when formation fractions are used in the model formulation:</p>
 <pre class="r"><code>Z.2a.ff &lt;- mkinmod(Z0 = mkinsub(&quot;SFO&quot;, &quot;Z1&quot;),
                    Z1 = mkinsub(&quot;SFO&quot;),
@@ -289,13 +284,13 @@ plot_sep(m.Z.2a)</code></pre>
 plot_sep(m.Z.2a.ff)</code></pre>
 <p><img 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" /><!-- --></p>
 <pre class="r"><code>summary(m.Z.2a.ff, data = FALSE)$bpar</code></pre>
-<pre><code>##              Estimate se_notrans   t value       Pr(&gt;t) Lower Upper
-## Z0_0       97.0148813 3.55314638 27.303936 1.679331e-21    NA    NA
-## k_Z0        2.2360064 0.21684717 10.311439 3.661734e-11    NA    NA
-## k_Z1        0.4821248 0.06585372  7.321147 3.552046e-08    NA    NA
-## f_Z0_to_Z1  1.0000000 0.10147344  9.854795 9.707117e-11    NA    NA</code></pre>
-<p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Again, the covariance matrix is not returned as the model is overparameterised.</p>
-<p>The simplified model is obtained by setting the list component  to .</p>
+<pre><code>##            Estimate se_notrans t value     Pr(&gt;t) Lower Upper
+## Z0_0       97.01488   3.553146 27.3039 1.6793e-21    NA    NA
+## k_Z0        2.23601   0.216847 10.3114 3.6617e-11    NA    NA
+## k_Z1        0.48212   0.065854  7.3211 3.5520e-08    NA    NA
+## f_Z0_to_Z1  1.00000   0.101473  9.8548 9.7071e-11    NA    NA</code></pre>
+<p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p>
+<p>A simplified model is obtained by removing the pathway to the sink. </p>
 <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p>
 <pre class="r"><code>Z.3 &lt;- mkinmod(Z0 = mkinsub(&quot;SFO&quot;, &quot;Z1&quot;, sink = FALSE),
                Z1 = mkinsub(&quot;SFO&quot;), use_of_ff = &quot;max&quot;)</code></pre>
@@ -304,10 +299,10 @@ plot_sep(m.Z.2a.ff)</code></pre>
 plot_sep(m.Z.3)</code></pre>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 <pre class="r"><code>summary(m.Z.3, data = FALSE)$bpar</code></pre>
-<pre><code>##        Estimate se_notrans  t value       Pr(&gt;t)      Lower       Upper
-## Z0_0 97.0148816 2.68177104 36.17568 2.363590e-25 91.5215232 102.5082401
-## k_Z0  2.2360064 0.14686238 15.22518 2.247007e-15  1.9545318   2.5580166
-## k_Z1  0.4821248 0.04268711 11.29439 3.068559e-12  0.4021552   0.5779966</code></pre>
+<pre><code>##      Estimate se_notrans t value     Pr(&gt;t)    Lower   Upper
+## Z0_0 97.01488   2.681771  36.176 2.3636e-25 91.52152 102.508
+## k_Z0  2.23601   0.146862  15.225 2.2470e-15  1.95453   2.558
+## k_Z1  0.48212   0.042687  11.294 3.0686e-12  0.40216   0.578</code></pre>
 <p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p>
 </div>
 <div id="metabolites-z2-and-z3" class="section level1">
@@ -335,34 +330,27 @@ plot_sep(m.Z.5)</code></pre>
 <pre class="r"><code>plot_sep(m.Z.FOCUS)</code></pre>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 <pre class="r"><code>summary(m.Z.FOCUS, data = FALSE)$bpar</code></pre>
-<pre><code>##               Estimate se_notrans   t value       Pr(&gt;t)       Lower
-## Z0_0       96.84024413 2.05881366 47.036916 5.572278e-44 92.70685169
-## k_Z0        2.21540096 0.11812759 18.754306 7.736886e-25  1.99050408
-## k_Z1        0.47835870 0.02929353 16.329843 3.344317e-22  0.42303496
-## k_Z2        0.45166296 0.04418624 10.221801 3.036447e-14  0.37106537
-## k_Z3        0.05868971 0.01428961  4.107158 7.256030e-05  0.03598292
-## f_Z2_to_Z3  0.47147387 0.05702672  8.267596 2.779011e-11  0.36029541
-##                   Upper
-## Z0_0       100.97363656
-## k_Z0         2.46570780
-## k_Z1         0.54091758
-## k_Z2         0.54976682
-## k_Z3         0.09572548
-## f_Z2_to_Z3   0.58555627</code></pre>
+<pre><code>##            Estimate se_notrans t value     Pr(&gt;t)     Lower      Upper
+## Z0_0       96.84024   2.058814 47.0369 5.5723e-44 92.706852 100.973637
+## k_Z0        2.21540   0.118128 18.7543 7.7369e-25  1.990504   2.465708
+## k_Z1        0.47836   0.029294 16.3298 3.3443e-22  0.423035   0.540918
+## k_Z2        0.45166   0.044186 10.2218 3.0364e-14  0.371065   0.549767
+## k_Z3        0.05869   0.014290  4.1072 7.2560e-05  0.035983   0.095725
+## f_Z2_to_Z3  0.47147   0.057027  8.2676 2.7790e-11  0.360295   0.585556</code></pre>
 <pre class="r"><code>endpoints(m.Z.FOCUS)</code></pre>
 <pre><code>## $ff
-##     Z2_Z3   Z2_sink 
-## 0.4714739 0.5285261 
+##   Z2_Z3 Z2_sink 
+## 0.47147 0.52853 
 ## 
 ## $SFORB
 ## logical(0)
 ## 
 ## $distimes
-##          DT50      DT90
-## Z0  0.3128766  1.039354
-## Z1  1.4490113  4.813512
-## Z2  1.5346558  5.098016
-## Z3 11.8103701 39.233200</code></pre>
+##        DT50    DT90
+## Z0  0.31288  1.0394
+## Z1  1.44901  4.8135
+## Z2  1.53466  5.0980
+## Z3 11.81037 39.2332</code></pre>
 <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p>
 </div>
 <div id="using-the-sforb-model" class="section level1">
@@ -426,18 +414,18 @@ plot_sep(m.Z.mkin.5a)</code></pre>
 <pre class="r"><code>endpoints(m.Z.mkin.5a)</code></pre>
 <pre><code>## $ff
 ##   Z0_free_Z1        Z1_Z2      Z2_sink   Z2_Z3_free Z3_free_sink 
-##    1.0000000    1.0000000    0.4634423    0.5365577    1.0000000 
+##      1.00000      1.00000      0.46344      0.53656      1.00000 
 ## 
 ## $SFORB
-##       Z0_b1       Z0_b2       Z3_b1       Z3_b2 
-## 2.447137325 0.007512576 0.080007563 0.000000000 
+##     Z0_b1     Z0_b2     Z3_b1     Z3_b2 
+## 2.4471373 0.0075126 0.0800076 0.0000000 
 ## 
 ## $distimes
-##         DT50     DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2
-## Z0 0.3042974 1.184810  0.2832482   92.26492         NA         NA
-## Z1 1.5147780 5.031984         NA         NA         NA         NA
-## Z2 1.6413852 5.452564         NA         NA         NA         NA
-## Z3        NA       NA         NA         NA   8.663521        Inf</code></pre>
+##      DT50   DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2
+## Z0 0.3043 1.1848    0.28325     92.265         NA         NA
+## Z1 1.5148 5.0320         NA         NA         NA         NA
+## Z2 1.6414 5.4526         NA         NA         NA         NA
+## Z3     NA     NA         NA         NA     8.6635        Inf</code></pre>
 <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p>
 </div>
 <div id="references" class="section level1">
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
new file mode 100644
index 00000000..975ad17f
--- /dev/null
+++ b/vignettes/FOCUS_Z.pdf
author	Johannes Ranke <jranke@uni-bremen.de>	2018-01-16 06:11:54 +0100
committer	Johannes Ranke <jranke@uni-bremen.de>	2018-01-16 06:11:54 +0100
commit	f18213520f20aba947093e53113c44b689e8b98d (patch)
tree	6b7d1e41c1c1f03f23dc52af5f1b03f0102b05e7
parent	373d98038c514c5152478127a8a2b9b390ee1b58 (diff)