aboutsummaryrefslogtreecommitdiff
path: root/man
diff options
context:
space:
mode:
Diffstat (limited to 'man')
-rw-r--r--man/add_err.Rd98
-rw-r--r--man/plot.mmkin.Rd5
2 files changed, 103 insertions, 0 deletions
diff --git a/man/add_err.Rd b/man/add_err.Rd
new file mode 100644
index 00000000..a0a5106f
--- /dev/null
+++ b/man/add_err.Rd
@@ -0,0 +1,98 @@
+\name{add_err}
+\alias{add_err}
+\title{
+ Add normally distributed errors to simulated kinetic degradation data
+}
+\description{
+ Normally distributed errors are added to data predicted for a specific
+ degradation model using \code{\link{mkinpredict}}. The variance of the error
+ may depend on the predicted value and is specified as a standard deviation.
+}
+\usage{
+ add_err(prediction, sdfunc,
+ n = 1000, LOD = 0.1, reps = 2,
+ digits = 1, seed = NA)
+}
+\arguments{
+ \item{prediction}{
+ A prediction from a kinetic model as produced by \code{\link{mkinpredict}}.
+ }
+ \item{sdfunc}{
+ A function taking the predicted value as its only argument and returning
+ a standard deviation that should be used for generating the random error
+ terms for this value.
+ }
+ \item{n}{
+ The number of datasets to be generated.
+ }
+ \item{LOD}{
+ The limit of detection (LOD). Values that are below the LOD after adding
+ the random error will be set to NA.
+ }
+ \item{reps}{
+ The number of replicates to be generated within the datasets.
+ }
+ \item{digits}{
+ The number of digits to which the values will be rounded.
+ }
+ \item{seed}{
+ The seed used for the generation of random numbers. If NA, the seed
+ is not set.
+ }
+}
+\value{
+ A list of datasets compatible with \code{\link{mmkin}}, i.e.
+ the components of the list are datasets compatible with
+ \code{\link{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
+ http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf
+}
+\author{
+ Johannes Ranke
+}
+\examples{
+# The kinetic model
+m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
+ M1 = mkinsub("SFO"), use_of_ff = "max")
+
+# 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,
+ c(k_parent = 0.1, f_parent_to_M1 = 0.5,
+ k_M1 = log(2)/1000),
+ c(parent = 100, M1 = 0),
+ sampling_times)
+
+# Add an error term with a constant (independent of the value) standard deviation
+# of 10, and generate three datasets
+d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )
+
+# Name the datasets for nicer plotting
+names(d_SFO_SFO_err) <- paste("Dataset", 1:3)
+
+# Name the model in the list of models (with only one member in this case)
+# for nicer plotting later on
+# Be quiet and use the faster Levenberg-Marquardt algorithm, as the datasets
+# are easy and examples are run often
+f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
+ d_SFO_SFO_err,
+ quiet = TRUE, method.modFit = "Marq")
+
+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)
+
+# If we use single brackets, we should give two indices (model and dataset),
+# and plot.mmkin is used
+plot(f_SFO_SFO[1, 3])
+
+}
+\keyword{ manip }
diff --git a/man/plot.mmkin.Rd b/man/plot.mmkin.Rd
index 761a4cd5..8362f16c 100644
--- a/man/plot.mmkin.Rd
+++ b/man/plot.mmkin.Rd
@@ -50,4 +50,9 @@
cores = 1, quiet = TRUE)
plot(fits[, "FOCUS C"])
plot(fits["FOMC", ])
+
+ # We can also plot a single fit, if we like the way mmkin works, but then the plot
+ # height should be smaller than the plot width (this is not possible for the html pages
+ # generated by staticdocs, as far as I know).
+ plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])
}

Contact - Imprint