Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, eval=FALSE--------------------------------------------------------
# library(tktdjl2r)
## ----setup_tktdjl2r, eval=FALSE-----------------------------------------------
# # Julia can be quite long to install the first time.
# tktdjl2r::tktdjl2r_setup()
## ----runTK, eval=FALSE--------------------------------------------------------
# single_runTK = runTK(c(0,1,2,3), c(0,1,2,2), 0.5)
## ----runTK_MCMC, eval=FALSE---------------------------------------------------
# kdVector = rbeta(4,10,10)
#
# mcmc_runTK = runTK_MCMC(c(0,1,2,3), c(0,1,2,2), kdVector)
## ----runSD, eval=FALSE--------------------------------------------------------
# single_runSD = runSD(c(0,1,2,3), c(0,1,2,2), 0.5, 0.2, 1, 0.4)
## ----runSD_MCMC, eval=FALSE---------------------------------------------------
# paramDF = data.frame(
# kd = rbeta(4,10,10),
# hb = rbeta(4,10,10),
# z = rbeta(4,10,10),
# kk = rbeta(4,10,10))
# mcmc_runSD = runSD_MCMC(c(0,1,2,3), c(0,1,2,2), paramDF$kd, paramDF$hb, paramDF$z, paramDF$kk)
## ----runIT, eval=FALSE--------------------------------------------------------
# single_runIT = runIT(c(0,1,2,3), c(0,1,2,2),0.5, 0.2, 1, 0.4)
## ----runIT_MCMC, eval=FALSE---------------------------------------------------
# paramDF = data.frame(
# kd = rbeta(4,10,10),
# hb = rbeta(4,10,10),
# alpha = rbeta(4,10,10),
# beta = rbeta(4,10,10))
# mcmc_runIT = runIT_MCMC(c(0,1,2,3), c(0,1,2,2), paramDF$kd, paramDF$hb, paramDF$alpha, paramDF$beta)
## ----deSolveTK, eval=FALSE----------------------------------------------------
# library(deSolve)
#
# model_TK <- function(t, State, parms, input) {
# with(as.list(c(parms, State)), {
# list(kd*(input(t) - State)) # internal damage
# })
# }
#
# deSolve_TK <- function(time, conc, listParameters){
# signal <- data.frame(times = time,
# import = conc)
#
# sigimp <- stats::approxfun(signal$times,
# signal$import,
# method = "linear",
# rule = 2)
#
# ## values for steady state
# xstart <- c(D = 0)
# ## model
# out <- ode(y = xstart,
# times = time,
# func = model_TK,
# parms = listParameters,
# input = sigimp)
#
# data.frame(
# time = time,
# exposure = conc,
# TK = out[,2]
# )
#
# }
## ----plotTK, eval=FALSE-------------------------------------------------------
# listParameters = list(kd = 0.5)
# testR_runTK = deSolve_TK(c(0,1,2,3), c(0,1,2,2), list(kd=0.5))
# plot(c(0,1,2,3), single_runTK$TK, type = "l", lwd = 3)
# lines(c(0,1,2,3), testR_runTK$TK, col = "red", lwd = 2)
## ----speedTK, eval=FALSE------------------------------------------------------
# library(microbenchmark)
#
# microbenchmark::microbenchmark(
# testJulia_runTK = runTK(c(0,1,2,3), c(0,1,2,2), 0.5),
# testR_runTK = deSolve_TK(c(0,1,2,3), c(0,1,2,2), list(kd=0.5)),
# times = 10
# )
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