From 317fd7514e638780c09ed6349a165a854ba2deea Mon Sep 17 00:00:00 2001
From: Johannes Ranke The R code used to create this data object is installed with this package in
+the 'dataset_generation' directory.Examples
+ # \dontrun{
+ sfo_mmkin <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = 15)
+ sfo_saem <- saem(sfo_mmkin, no_random_effect = "parent_0")
+ plot(sfo_saem)
+
+# }
+
+# This is the code used to generate the datasets
+cat(readLines(system.file("dataset_generation/ds_mixed.R", package = "mkin")), sep = "\n")
+#> # Synthetic data for hierarchical kinetic models
+#> # Refactored version of the code previously in tests/testthat/setup_script.R
+#> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it
+#> # is always 15 for consistency
+#>
+#> library(mkin) # We use mkinmod and mkinpredict
+#> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+#> n <- 15
+#> log_sd <- 0.3
+#> err_1 = list(const = 1, prop = 0.05)
+#> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)
+#> const <- function(value) 2
+#>
+#> set.seed(123456)
+#> SFO <- mkinmod(parent = mkinsub("SFO"))
+#> sfo_pop <- list(parent_0 = 100, k_parent = 0.03)
+#> sfo_parms <- as.matrix(data.frame(
+#> k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))
+#> set.seed(123456)
+#> ds_sfo <- lapply(1:n, function(i) {
+#> ds_mean <- mkinpredict(SFO, sfo_parms[i, ],
+#> c(parent = sfo_pop$parent_0), sampling_times)
+#> add_err(ds_mean, tc, n = 1)[[1]]
+#> })
+#> attr(ds_sfo, "pop") <- sfo_pop
+#> attr(ds_sfo, "parms") <- sfo_parms
+#>
+#> set.seed(123456)
+#> FOMC <- mkinmod(parent = mkinsub("FOMC"))
+#> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)
+#> fomc_parms <- as.matrix(data.frame(
+#> alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),
+#> beta = rlnorm(n, log(fomc_pop$beta), 0.2)))
+#> set.seed(123456)
+#> ds_fomc <- lapply(1:n, function(i) {
+#> ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],
+#> c(parent = fomc_pop$parent_0), sampling_times)
+#> add_err(ds_mean, tc, n = 1)[[1]]
+#> })
+#> attr(ds_fomc, "pop") <- fomc_pop
+#> attr(ds_fomc, "parms") <- fomc_parms
+#>
+#> set.seed(123456)
+#> DFOP <- mkinmod(parent = mkinsub("DFOP"))
+#> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)
+#> dfop_parms <- as.matrix(data.frame(
+#> k1 = rlnorm(n, log(dfop_pop$k1), log_sd),
+#> k2 = rlnorm(n, log(dfop_pop$k2), log_sd),
+#> g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))
+#> set.seed(123456)
+#> ds_dfop <- lapply(1:n, function(i) {
+#> ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],
+#> c(parent = dfop_pop$parent_0), sampling_times)
+#> add_err(ds_mean, tc, n = 1)[[1]]
+#> })
+#> attr(ds_dfop, "pop") <- dfop_pop
+#> attr(ds_dfop, "parms") <- dfop_parms
+#>
+#> set.seed(123456)
+#> HS <- mkinmod(parent = mkinsub("HS"))
+#> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)
+#> hs_parms <- as.matrix(data.frame(
+#> k1 = rlnorm(n, log(hs_pop$k1), log_sd),
+#> k2 = rlnorm(n, log(hs_pop$k2), log_sd),
+#> tb = rlnorm(n, log(hs_pop$tb), 0.1)))
+#> set.seed(123456)
+#> ds_hs <- lapply(1:n, function(i) {
+#> ds_mean <- mkinpredict(HS, hs_parms[i, ],
+#> c(parent = hs_pop$parent_0), sampling_times)
+#> add_err(ds_mean, const, n = 1)[[1]]
+#> })
+#> attr(ds_hs, "pop") <- hs_pop
+#> attr(ds_hs, "parms") <- hs_parms
+#>
+#> set.seed(123456)
+#> DFOP_SFO <- mkinmod(
+#> parent = mkinsub("DFOP", "m1"),
+#> m1 = mkinsub("SFO"),
+#> quiet = TRUE)
+#> dfop_sfo_pop <- list(parent_0 = 100,
+#> k_m1 = 0.007, f_parent_to_m1 = 0.5,
+#> k1 = 0.1, k2 = 0.02, g = 0.5)
+#> dfop_sfo_parms <- as.matrix(data.frame(
+#> k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),
+#> k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),
+#> g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),
+#> f_parent_to_m1 = plogis(rnorm(n,
+#> qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),
+#> k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))
+#> ds_dfop_sfo_mean <- lapply(1:n,
+#> function(i) {
+#> mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],
+#> c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)
+#> }
+#> )
+#> set.seed(123456)
+#> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) {
+#> add_err(ds,
+#> sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),
+#> n = 1, secondary = "m1")[[1]]
+#> })
+#> attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop
+#> attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms
+#>
+#> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)
+
FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar
Synthetic data for hierarchical kinetic degradation models
Three experimental datasets from two water sediment systems and one soil
FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar