Synthetic datasets for one parent compound with two metabolites

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Description

The 12 datasets were generated using four different models and three different variance components. The four models are either the SFO or the DFOP model with either two sequential or two parallel metabolites.

Variance component 'a' is based on a normal distribution with standard deviation of 3, Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7. Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07 for the increase of the standard deviation with y.

Initial concentrations for metabolites and all values where adding the variance component resulted in a value below the assumed limit of detection of 0.1 were set to NA.

As an example, the first dataset has the title SFO_lin_a and is based on the SFO model with two sequential metabolites (linear pathway), with added variance component 'a'.

Compare also the code in the example section to see the degradation models.

Usage

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synthetic_data_for_UBA_2014

Format

A list containing datasets in the form internally used by the 'gmkin' package. The list has twelfe components. Each of the components is one dataset that has, among others, the following components

title

The name of the dataset, e.g. SFO_lin_a

data

A data frame with the data in the form expected by mkinfit

Source

Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452

Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for measurement error in analytical chemistry. Technometrics 37(2), 176-184.

Examples

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## Not run: 
m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"),
                           M1 = list(type = "SFO", to = "M2"),
                           M2 = list(type = "SFO"), use_of_ff = "max")


m_synth_SFO_par <- mkinmod(parent = list(type = "SFO", to = c("M1", "M2"),
                                         sink = FALSE),
                           M1 = list(type = "SFO"),
                           M2 = list(type = "SFO"), use_of_ff = "max")

m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"),
                            M1 = list(type = "SFO", to = "M2"),
                            M2 = list(type = "SFO"), use_of_ff = "max")

m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2"),
                                          sink = FALSE),
                            M1 = list(type = "SFO"),
                            M2 = list(type = "SFO"), use_of_ff = "max")

fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data, quiet = TRUE)
plot_sep(fit)
summary(fit)

## End(Not run)

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