From 8f38147e145c66ebd27f5d9fcc6f00172c25872f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 26 Oct 2016 23:18:20 +0200 Subject: Static documentation rebuilt by pkgdown::build_site() --- docs/reference/add_err.html | 88 +++++++++++++++++++++++++++------------------ 1 file changed, 54 insertions(+), 34 deletions(-) (limited to 'docs/reference/add_err.html') diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index cbc925ca..9020a486 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -6,14 +6,14 @@ -add_err. mkin + + — add_err • mkin - - + @@ -22,6 +22,7 @@ + @@ -34,10 +35,9 @@ -
+
- -
-
-

+
+
+
+

-
+
-
-

Normally distributed errors are added to data predicted for a specific - degradation model using mkinpredict. The variance of the error + degradation model using mkinpredict. The variance of the error may depend on the predicted value and is specified as a standard deviation.

@@ -87,11 +89,11 @@ n = 1000, LOD = 0.1, reps = 2, digits = 1, seed = NA) -

Arguments

+

Arguments

prediction
- A prediction from a kinetic model as produced by mkinpredict. + A prediction from a kinetic model as produced by mkinpredict.
sdfunc
@@ -123,31 +125,31 @@
-
-

Value

+

Value

-

A list of datasets compatible with mmkin, i.e. +

A list of datasets compatible with mmkin, i.e. the components of the list are datasets compatible with - mkinfit.

-
- -
-

References

+ mkinfit.

+ +

References

-

Ranke J and Lehmann R (2015) To t-test or not to t-test, that is the question. XV Symposium on Pesticide Chemistry 2-4 September 2015, Piacenza, Italy +

Ranke J and Lehmann R (2015) To t-test or not to t-test, that is the question. XV Symposium on Pesticide Chemistry 2-4 September 2015, Piacenza, Italy http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf

-
-

Examples

+ +

+ + Examples + 

# The kinetic model
-m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
-                     M1 = mkinsub("SFO"), use_of_ff = "max")
Successfully compiled differential equation model from auto-generated C code.

+m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
+                     M1 = mkinsub("SFO"), use_of_ff = "max")
Successfully compiled differential equation model from auto-generated C code.

 # Generate a prediction for a specific set of parameters
 sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
 
 # This is the prediction used for the "Type 2 datasets" on the Piacenza poster
 # from 2015
-d_SFO_SFO <- mkinpredict(m_SFO_SFO,
+d_SFO_SFO <- mkinpredict(m_SFO_SFO,
                          c(k_parent = 0.1, f_parent_to_M1 = 0.5,
                            k_M1 = log(2)/1000),
                          c(parent = 100, M1 = 0),
@@ -165,21 +167,32 @@
 # Be quiet and use the faster Levenberg-Marquardt algorithm, as the datasets
 # are easy and examples are run often. Use only one core not to offend CRAN
 # checks
-f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
+f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
                    d_SFO_SFO_err, cores = 1,
                    quiet = TRUE, method.modFit = "Marq")
 
-plot(f_SFO_SFO)

+plot(f_SFO_SFO)

 # We would like to inspect the fit for dataset 3 more closely
 # Using double brackets makes the returned object an mkinfit object
 # instead of a list of mkinfit objects, so plot.mkinfit is used
-plot(f_SFO_SFO[[3]], show_residuals = TRUE)

+plot(f_SFO_SFO[[3]], show_residuals = TRUE)

 # If we use single brackets, we should give two indices (model and dataset),
 # and plot.mmkin is used
-plot(f_SFO_SFO[1, 3])

+plot(f_SFO_SFO[1, 3])
-
+
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