add_err. mkin 0.9.44.9000

Usage

add_err(prediction, sdfunc,
-          n = 1000, 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. -
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. -

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Description

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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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Value

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

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References

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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

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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.
-
-# 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. Use only one core not to offend CRAN
-# checks
-f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
-                   d_SFO_SFO_err, cores = 1,
-                   quiet = TRUE, method.modFit = "Marq")
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-plot(f_SFO_SFO)
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-
-# 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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