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# 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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