Add normally distributed errors to simulated kinetic degradation data

+ +

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

+ +

Usage

add_err(
+  prediction,
+  sdfunc,
+  secondary = c("M1", "M2"),
+  n = 10,
+  LOD = 0.1,
+  reps = 2,
+  digits = 1,
+  seed = NA
+)

+ +

Arguments

+ + +

prediction: A prediction from a kinetic model as produced by +mkinpredict.
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.
secondary: The names of state variables that should have an initial +value of zero
n: The number of datasets to be generated.
LOD: The limit of detection (LOD). Values that are below the LOD after +adding the random error will be set to NA.
reps: The number of replicates to be generated within the datasets.
digits: The number of digits to which the values will be rounded.
seed: The seed used for the generation of random numbers. If NA, the +seed is not set.

Value

A list of datasets compatible with mmkin, i.e. the +components of the list are datasets compatible with 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 +https://jrwb.de/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")
+#> Temporary DLL for differentials generated and loaded
+
+# 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 only one core not to offend CRAN
+# checks
+# \dontrun{
+f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
+                   d_SFO_SFO_err, cores = 1,
+                   quiet = TRUE)
+
+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])
+
+# }
+
+