From 95178837d3f91e84837628446b5fd468179af2b9 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Tue, 4 Jun 2019 15:09:28 +0200
Subject: Additional algorithm "d_c", more tests, docs

The new algorithm tries direct optimization of the likelihood, as well
as a three step procedure. In this way, we consistently get the
model with the highest likelihood for SFO, DFOP and HS for all 12
new test datasets.
---
 docs/reference/synthetic_data_for_UBA.html | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

(limited to 'docs/reference/synthetic_data_for_UBA.html')

diff --git a/docs/reference/synthetic_data_for_UBA.html b/docs/reference/synthetic_data_for_UBA.html
index 2c2623e4..4a7ca728 100644
--- a/docs/reference/synthetic_data_for_UBA.html
+++ b/docs/reference/synthetic_data_for_UBA.html
@@ -40,7 +40,7 @@ Variance component 'a' is based on a normal distribution with standard deviation
  Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
  minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
  for the increase of the standard deviation with y. Note that this is a simplified version
- of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the 
+ of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
  measured values approximates lognormal distribution for high values, whereas we are using
  normally distributed error components all along.
 Initial concentrations for metabolites and all values where adding the variance component resulted
@@ -78,7 +78,7 @@ Compare also the code in the example section to see the degradation models." />
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.4</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.5</span>
       </span>
     </div>
 
@@ -151,7 +151,7 @@ Compare also the code in the example section to see the degradation models." />
  Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
  minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
  for the increase of the standard deviation with y. Note that this is a simplified version
- of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the 
+ of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
  measured values approximates lognormal distribution for high values, whereas we are using
  normally distributed error components all along.</p>
 <p>Initial concentrations for metabolites and all values where adding the variance component resulted
@@ -253,7 +253,8 @@ add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)
   return(d_NA)
 }
 
-# The following is the two-component model of Rocke and Lorenzato (1995)
+# The following is the simplified version of the two-component model of Rocke
+# and Lorenzato (1995)
 sdfunc_twocomp = function(value, sd_low, rsd_high) {
   sqrt(sd_low^2 + value^2 * rsd_high^2)
 }
@@ -304,7 +305,7 @@ summary(fit)
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
 </div>
       </footer>
    </div>
-- 
cgit v1.2.1