Convert main vignette to Rmd/html, add_err(), fixes

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+\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 }