Comment out the code of the add_err() function

as it leads to problems with current pkgdown versions r-lib/pkgdown#1149

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-\name{synthetic_data_for_UBA_2014}

-\alias{synthetic_data_for_UBA_2014}

-\docType{data}

-\title{

-  Synthetic datasets for one parent compound with two metabolites

-}

-\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. Note that this is a simplified version

- of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the

- measured values approximates lognormal distribution for high values, whereas we are using

- normally distributed error components all along.

-

- 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 \code{NA}.

-

- As an example, the first dataset has the title \code{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{synthetic_data_for_UBA_2014}

-\format{

-  A list containing twelve datasets as an R6 class defined by \code{\link{mkinds}},

-  each containing, among others, the following components

-  \describe{

-    \item{\code{title}}{The name of the dataset, e.g. \code{SFO_lin_a}}

-    \item{\code{data}}{A data frame with the data in the form expected by \code{\link{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{

-\dontrun{

-# The data have been generated using the following kinetic models

-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")

-

-# The model predictions without intentional error were generated as follows

-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)

-

-d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,

-                               c(k_parent = 0.7, f_parent_to_M1 = 0.8,

-                                 k_M1 = 0.3, f_M1_to_M2 = 0.7,

-                                 k_M2 = 0.02),

-                               c(parent = 100, M1 = 0, M2 = 0),

-                               sampling_times)

-

-d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,

-                                c(k1 = 0.2, k2 = 0.02, g = 0.5,

-                                  f_parent_to_M1 = 0.5, k_M1 = 0.3,

-                                  f_M1_to_M2 = 0.7, k_M2 = 0.02),

-                                 c(parent = 100, M1 = 0, M2 = 0),

-                                 sampling_times)

-

-d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,

-                               c(k_parent = 0.2,

-                                 f_parent_to_M1 = 0.8, k_M1 = 0.01,

-                                 f_parent_to_M2 = 0.2, k_M2 = 0.02),

-                                 c(parent = 100, M1 = 0, M2 = 0),

-                                 sampling_times)

-

-d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,

-                               c(k1 = 0.3, k2 = 0.02, g = 0.7,

-                                 f_parent_to_M1 = 0.6, k_M1 = 0.04,

-                                 f_parent_to_M2 = 0.4, k_M2 = 0.01),

-                                 c(parent = 100, M1 = 0, M2 = 0),

-                                 sampling_times)

-

-# Construct names for datasets with errors

-d_synth_names = paste0("d_synth_", c("SFO_lin", "SFO_par",

-                                     "DFOP_lin", "DFOP_par"))

-

-# Function for adding errors. The add_err function now published with this

-# package is a slightly generalised version where the names of secondary

-# compartments that should have an initial value of zero (M1 and M2 in this

-# case) are not hardcoded any more.

-add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)

-{

-  set.seed(seed)

-  d_long = mkin_wide_to_long(d, time = "time")

-  d_rep = data.frame(lapply(d_long, rep, each = 2))

-  d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))

-

-  d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0

-  d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))

-  d_NA$value <- round(d_NA$value, 1)

-  return(d_NA)

-}

-

-# The following is the simplified version of the two-component model of Rocke

-# and Lorenzato (1995)

-sdfunc_twocomp = function(value, sd_low, rsd_high) {

-  sqrt(sd_low^2 + value^2 * rsd_high^2)

-}

-

-# Add the errors.

-for (d_synth_name in d_synth_names)

-{

-  d_synth = get(d_synth_name)

-  assign(paste0(d_synth_name, "_a"), add_err(d_synth, function(value) 3))

-  assign(paste0(d_synth_name, "_b"), add_err(d_synth, function(value) 7))

-  assign(paste0(d_synth_name, "_c"), add_err(d_synth,

-                           function(value) sdfunc_twocomp(value, 0.5, 0.07)))

-

-}

-

-d_synth_err_names = c(

-  paste(rep(d_synth_names, each = 3), letters[1:3], sep = "_")

-)

-

-# This is just one example of an evaluation using the kinetic model used for

-# the generation of the data

-fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,

-               quiet = TRUE)

-plot_sep(fit)

-summary(fit)

-}

-}

-\keyword{datasets}