Add normally distributed errors to simulated kinetic degradation data

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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.

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

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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

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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

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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])
-
-# }
-
-

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