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| -rw-r--r-- | man/synthetic_data_for_UBA_2014.Rd (renamed from man/synthetic_data_for_UBA.Rd) | 294 |
1 files changed, 146 insertions, 148 deletions
diff --git a/man/synthetic_data_for_UBA.Rd b/man/synthetic_data_for_UBA_2014.Rd index e910297d..4e10d209 100644 --- a/man/synthetic_data_for_UBA.Rd +++ b/man/synthetic_data_for_UBA_2014.Rd @@ -1,148 +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{
-\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}
+\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} |