From aed80b602afbe8c22ba601bf236dda22bc39187c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 21 Oct 2019 22:46:33 +0200 Subject: Static documentation rebuilt by pkgdown --- docs/reference/index.html | 5 ++++- docs/reference/mkinerrplot.html | 17 ++++++--------- docs/reference/synthetic_data_for_UBA_2014.html | 28 +++++++++++-------------- 3 files changed, 22 insertions(+), 28 deletions(-) (limited to 'docs/reference') diff --git a/docs/reference/index.html b/docs/reference/index.html index e74a8ad5..17a879a9 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -8,11 +8,13 @@ Function reference • mkin + + @@ -32,10 +34,12 @@ + + @@ -106,7 +110,6 @@ News - diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html index be2f7614..4386673c 100644 --- a/docs/reference/mkinerrplot.html +++ b/docs/reference/mkinerrplot.html @@ -8,11 +8,13 @@ Function to plot squared residuals and the error model for an mkin object — mkinerrplot • mkin + + @@ -32,8 +34,8 @@ - + @@ -114,7 +117,6 @@ News - @@ -136,14 +138,12 @@
-

This function plots the squared residuals for the specified subset of the observed variables from an mkinfit object. In addition, one or more dashed line(s) show the fitted error model. A combined plot of the fitted model and this error model plot can be obtained with plot.mkinfit using the argument show_errplot = TRUE.

- 
mkinerrplot(object,
@@ -154,7 +154,7 @@
     col_obs = "auto", pch_obs = "auto",
     frame = TRUE,
     ...)
- +

Arguments

@@ -210,16 +210,14 @@

further arguments passed to plot.
- +

Value

Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.

-

See also

mkinplot, for a way to plot the data and the fitted lines of the mkinfit object.

-

Examples

# \dontrun{
@@ -230,11 +228,8 @@
     
Contents

     
 
diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html
index ddc88c21..f23b77b0 100644
--- a/docs/reference/synthetic_data_for_UBA_2014.html
+++ b/docs/reference/synthetic_data_for_UBA_2014.html
@@ -8,11 +8,13 @@
 
 Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin
 
+
 
 
 
 
 
+
 
 
 
@@ -32,8 +34,8 @@
 
 
 
-
 
+
 
 
 
 
+
 
 
 
@@ -124,7 +127,6 @@ Compare also the code in the example section to see the degradation models." />
   News
 
       
-      
       
    
         
       

@@ -146,7 +148,6 @@ Compare also the code in the example section to see the degradation models." />
     

 
     

-    
     
The 12 datasets were generated using four different models and three different
  variance components. The four models are either the SFO or the DFOP model with either
  two sequential or two parallel metabolites.

@@ -163,11 +164,11 @@ Compare also the code in the example section to see the degradation models." />
 
As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
  with two sequential metabolites (linear pathway), with added variance component 'a'.

 
Compare also the code in the example section to see the degradation models.

-    
     

 
     
synthetic_data_for_UBA_2014

-        
+
+
     
Format

 
     
A list containing twelve datasets as an R6 class defined by mkinds,
@@ -177,17 +178,16 @@ Compare also the code in the example section to see the degradation models." />
   
 
 
-    
     
Source

 
     
Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
   zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452

 
Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
   measurement error in analytical chemistry. Technometrics 37(2), 176-184.

-    
 
     
Examples

-    
# The data have been generated using the following kinetic models
+    
# \dontrun{
+# The data have been generated using the following kinetic models
 m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"),
                            M1 = list(type = "SFO", to = "M2"),
                            M2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

@@ -249,7 +249,7 @@ Compare also the code in the example section to see the degradation models." />
 #   d_rep = data.frame(lapply(d_long, rep, each = 2))
 #   d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))
 #
-#   d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0
+#   d_rep[d_rep$time == 0 & d_rep$name 
 #   d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))
 #   d_NA$value <- round(d_NA$value, 1)
 #   return(d_NA)
@@ -278,13 +278,12 @@ Compare also the code in the example section to see the degradation models." />
 
 # This is just one example of an evaluation using the kinetic model used for
 # the generation of the data
-# \dontrun{
   fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,
                  quiet = TRUE)
   plot_sep(fit)
  summary(fit)
#> mkin version used for fitting:    0.9.49.6 
 #> R version used for fitting:       3.6.1 
-#> Date of fit:     Mon Oct 21 18:52:20 2019 
-#> Date of summary: Mon Oct 21 18:52:20 2019 
+#> Date of fit:     Mon Oct 21 22:46:31 2019 
+#> Date of summary: Mon Oct 21 22:46:31 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -293,7 +292,7 @@ Compare also the code in the example section to see the degradation models." />
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 847 model solutions performed in 2.42 s
+#> Fitted using 847 model solutions performed in 2.45 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -429,11 +428,8 @@ Compare also the code in the example section to see the degradation models." />