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@@ -1,146 +1,146 @@
-\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{
-# 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"))
-
-# Original function used or 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$time == 0 & match(d_rep$name, 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}
+\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{

+# 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"))

+

+# Original function used or 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$time == 0 & match(d_rep$name, 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}