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Diffstat (limited to 'man/synthetic_data_for_UBA_2014.Rd')
| -rw-r--r-- | man/synthetic_data_for_UBA_2014.Rd | 292 |
1 files changed, 146 insertions, 146 deletions
diff --git a/man/synthetic_data_for_UBA_2014.Rd b/man/synthetic_data_for_UBA_2014.Rd index 4e10d209..2d726d5a 100644 --- a/man/synthetic_data_for_UBA_2014.Rd +++ b/man/synthetic_data_for_UBA_2014.Rd @@ -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}
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