diff options
Diffstat (limited to 'man/synthetic_data_for_UBA.Rd')
| -rw-r--r-- | man/synthetic_data_for_UBA.Rd | 82 |
1 files changed, 79 insertions, 3 deletions
diff --git a/man/synthetic_data_for_UBA.Rd b/man/synthetic_data_for_UBA.Rd index a9739d55..3339798e 100644 --- a/man/synthetic_data_for_UBA.Rd +++ b/man/synthetic_data_for_UBA.Rd @@ -11,7 +11,7 @@ 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
+ 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.
@@ -36,12 +36,13 @@ \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")
@@ -61,7 +62,82 @@ m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2"), M1 = list(type = "SFO"),
M2 = list(type = "SFO"), use_of_ff = "max")
-fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data, quiet = TRUE)
+# The model predictions without intentional error were generated as follows
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+d_synth_SFO_lin <- mkinpredict(m_synth_SFO_lin,
+ c(k_parent = 0.7, f_parent_to_M1 = 0.8,
+ k_M1 = 0.3, f_M1_to_M2 = 0.7,
+ k_M2 = 0.02),
+ c(parent = 100, M1 = 0, M2 = 0),
+ sampling_times)
+
+d_synth_DFOP_lin <- mkinpredict(m_synth_DFOP_lin,
+ c(k1 = 0.2, k2 = 0.02, g = 0.5,
+ f_parent_to_M1 = 0.5, k_M1 = 0.3,
+ f_M1_to_M2 = 0.7, k_M2 = 0.02),
+ c(parent = 100, M1 = 0, M2 = 0),
+ sampling_times)
+
+d_synth_SFO_par <- mkinpredict(m_synth_SFO_par,
+ c(k_parent = 0.2,
+ f_parent_to_M1 = 0.8, k_M1 = 0.01,
+ f_parent_to_M2 = 0.2, k_M2 = 0.02),
+ c(parent = 100, M1 = 0, M2 = 0),
+ sampling_times)
+
+d_synth_DFOP_par <- mkinpredict(m_synth_DFOP_par,
+ c(k1 = 0.3, k2 = 0.02, g = 0.7,
+ f_parent_to_M1 = 0.6, k_M1 = 0.04,
+ f_parent_to_M2 = 0.4, k_M2 = 0.01),
+ c(parent = 100, M1 = 0, M2 = 0),
+ sampling_times)
+
+# Construct names for datasets with errors
+d_synth_names = paste0("d_synth_", c("SFO_lin", "SFO_par",
+ "DFOP_lin", "DFOP_par"))
+
+# Function for adding errors. The add_err function now published with this
+# package is a slightly generalised version where the names of secondary
+# compartments that should have an initial value of zero (M1 and M2 in this
+# case) are not hardcoded any more.
+add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)
+{
+ set.seed(seed)
+ d_long = mkin_wide_to_long(d, time = "time")
+ d_rep = data.frame(lapply(d_long, rep, each = 2))
+ d_rep$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$value, 1)
+ return(d_NA)
+}
+
+# The following is the two-component model of Rocke and Lorenzato (1995)
+sdfunc_twocomp = function(value, sd_low, rsd_high) {
+ sqrt(sd_low^2 + value^2 * rsd_high^2)
+}
+
+# Add the errors.
+for (d_synth_name in d_synth_names)
+{
+ d_synth = get(d_synth_name)
+ assign(paste0(d_synth_name, "_a"), add_err(d_synth, function(value) 3))
+ assign(paste0(d_synth_name, "_b"), add_err(d_synth, function(value) 7))
+ assign(paste0(d_synth_name, "_c"), add_err(d_synth,
+ function(value) sdfunc_twocomp(value, 0.5, 0.07)))
+
+}
+
+d_synth_err_names = c(
+ paste(rep(d_synth_names, each = 3), letters[1:3], sep = "_")
+)
+
+# This is just one example of an evaluation using the kinetic model used for
+# the generation of the data
+fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,
+ quiet = TRUE)
plot_sep(fit)
summary(fit)
}
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