rxTest({
test_that("Test ui solve", {
# Step 1 - Create a model specification
f <- function() {
ini({
KA <- .291
CL <- 18.6
V2 <- 40.2
Q <- 10.5
V3 <- 297.0
Kin <- 1.0
Kout <- 1.0
EC50 <- 200.0
})
model({
# A 4-compartment model, 3 PK and a PD (effect) compartment
# (notice state variable names 'depot', 'centr', 'peri', 'eff')
C2 <- centr/V2
C3 <- peri/V3
d/dt(depot) <- -KA*depot
d/dt(centr) <- KA*depot - CL*C2 - Q*C2 + Q*C3
d/dt(peri) <- Q*C2 - Q*C3
d/dt(eff) <- Kin - Kout*(1-C2/(EC50+C2))*eff
eff(0) <- 1
})
}
u <- f()
# this pre-compiles and displays the simulation model
u$simulationModel
# Step 2 - Create the model input as an EventTable,
# including dosing and observation (sampling) events
# QD (once daily) dosing for 5 days.
qd <- eventTable(amount.units = "ug", time.units = "hours")
qd$add.dosing(dose = 10000, nbr.doses = 5, dosing.interval = 24)
# Sample the system hourly during the first day, every 8 hours
# then after
qd$add.sampling(0:24)
qd$add.sampling(seq(from = 24 + 8, to = 5 * 24, by = 8))
# Step 3 - set starting parameter estimates and initial
# values of the state
# Step 4 - Fit the model to the data
expect_error(solve(u, qd), NA)
u1 <- u$simulationModel
expect_error(solve(u1, qd), NA)
u2 <- u$simulationIniModel
expect_error(solve(u2, qd), NA)
})
})
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