Synthetic data for hierarchical kinetic degradation models

‘R/saem.R’: ‘logLik’, ‘update’ and ‘anova’ methods for ‘saem.mmkin’ objects.

Synthetic data for hierarchical kinetic degradation models — ds_mixed • mkin

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The R code used to create this data object is installed with this package in +the 'dataset_generation' directory.

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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)
+

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FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar

mkin 1.2.1 (unreleased)

mkin 1.2.0 (2022-11-17)

Synthetic data for hierarchical kinetic degradation models

Examples