Two component error model

y	The magnitude of the observed value
sigma_low	The asymptotic minimum of the standard deviation for low observed values
rsd_high	The coefficient describing the increase of the standard deviation with + the magnitude of the observed value

#> mkin version used for fitting: 0.9.47.1 -#> R version used for fitting: 3.5.0 -#> Date of fit: Wed Jun 6 01:21:46 2018 -#> Date of summary: Wed Jun 6 01:21:46 2018 +

  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.47.2 
+#> R version used for fitting:       3.5.1 
+#> Date of fit:     Wed Jul 18 15:16:13 2018 
+#> Date of summary: Wed Jul 18 15:16:13 2018 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent_sink * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted with method Port using 35 model solutions performed in 0.075 s
+#> Fitted with method Port using 35 model solutions performed in 0.073 s
 #> 
 #> Weighting: none
 #> 
diff --git a/docs/reference/synthetic_data_for_UBA.html b/docs/reference/synthetic_data_for_UBA.html
index 6e0ac227..9d9404e5 100644
--- a/docs/reference/synthetic_data_for_UBA.html
+++ b/docs/reference/synthetic_data_for_UBA.html
@@ -39,7 +39,10 @@ Variance component 'a' is based on a normal distribution with standard deviation
  Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
  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.
+ 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 
+ 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
  in a value below the assumed limit of detection of 0.1 were set to NA.
 As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
@@ -73,7 +76,7 @@ Compare also the code in the example section to see the degradation models." />
       
       
         mkin
-        0.9.47.1
+        0.9.47.2
       
     
 
@@ -142,7 +145,10 @@ Compare also the code in the example section to see the degradation models." />
  Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
  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.
+ 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 
+ 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
  in a value below the assumed limit of detection of 0.1 were set to NA.
 As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html
index 090b0c22..5b683283 100644
--- a/docs/reference/test_data_from_UBA_2014.html
+++ b/docs/reference/test_data_from_UBA_2014.html
@@ -62,7 +62,7 @@
       
       
         mkin
-        0.9.47.1
+        0.9.47.2

Example evaluation of FOCUS Example Dataset D

Johannes Ranke

2018-06-06

2018-07-18

Example evaluation of FOCUS Laboratory Data L1 to L3

Johannes Ranke

2018-06-06

2018-07-18

Example evaluation of FOCUS dataset Z

Johannes Ranke

2018-06-06

2018-07-18

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

2018-06-06

2018-07-18

@@ -156,14 +156,14 @@ SFO_SFO <- mkin - 0.9.47.1 + 0.9.47.2 diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index 28186062..14a2f9b9 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -29,7 +29,7 @@ mkin - 0.9.47.1 + 0.9.47.2 @@ -84,7 +84,7 @@

Introduction to mkin

Johannes Ranke

2018-06-06

2018-07-18

Calculation of time weighted average concentrations with mkin

Johannes Ranke

2018-06-06

2018-07-18

+mkin 0.9.47.2 Unreleased +

Contents

Examples

Arguments

Value

References

Contents

Examples