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.

+ +

add_err(
+  prediction,
+  sdfunc,
+  secondary = c("M1", "M2"),
+  n = 1000,
+  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

+ +

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 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])
# }
+
+

+ +