tests/testthat/test-lincmt-solve.R
In nlmixrdevelopment/RxODE: Facilities for Simulating from ODE-Based Models
rxodeTest(
{
tol <- 5e-6 ## Current difference for all equations
type <- 2
types <- 1:4
for (type in types) {
.txt <- switch(type,
"linear",
"sensitivity",
"linear [no save]",
"advanSens"
)
sens <- switch(type,
"linCmtA",
"linCmtB",
"linCmtC",
"linCmtB"
)
sensType <- switch(type,
"autodiff",
"autodiff",
"autodiff",
"advan"
)
etSsB <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
etSsI <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
etSsR <- et(amt = 0, ss = 1, rate = 10000 / 8)
ode.1c <- RxODE(
{
C2 <- center / V
d / dt(center) <- -CL * C2
},
linCmtSens = sens
)
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
context(sprintf("Test steady state solutions 1 cmt (%s)", .txt))
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = etSsB)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = etSsB, sensType = sensType)
test_that("one compartment bolus steady state", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = etSsI)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = etSsI, sensType = sensType)
test_that("one compartment infusion tau steady state", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = etSsR)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = etSsR, sensType = sensType)
test_that("one compartment infusion steady state", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
context(sprintf("Test steady state solutions 2 cmt (%s)", .txt))
ode.2c <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
},
linCmtSens = sens
)
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q1)
},
linCmtSens = sens
)
o2 <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSsB)
s2 <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = etSsB, sensType = sensType)
test_that("two compartment bolus steady state", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSsI)
s2 <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = etSsI, sensType = sensType)
test_that("two compartment infusion steady state, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSsR)
s2 <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = etSsR, sensType = sensType)
test_that("two compartment infusion steady state", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
context(sprintf("Test steady state solutions 3 cmt (%s)", .txt))
ode.3c <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
},
linCmtSens = sens
)
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
o3 <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsB)
s3 <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsB, sensType = sensType)
test_that("three compartment bolus steady state", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsI)
s3 <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsI, sensType = sensType)
test_that("three compartment bolus steady state", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsR)
s3 <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsR, sensType = sensType)
test_that("three compartment bolus steady state", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
context(sprintf("Test steady state solutions 1 cmt ka (%s)", .txt))
ode.1c.ka <- RxODE(
{
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
},
linCmtSens = sens
)
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
},
linCmtSens = sens
)
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB, sensType = sensType)
test_that("one compartment bolus steady state to depot compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI, sensType = sensType)
test_that("one compartment infusion steady state to depot compartment, tau", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR, sensType = sensType)
test_that("one compartment infusion steady state to depot compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
etSsB2 <- et() %>%
et(amt = 3, cmt = 2) %>%
et(time = 4, amt = 3, ss = 1, ii = 24, cmt = 2) %>%
et(amt = 3, ss = 2, ii = 24, time = 8, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
etSsI2 <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
etSsR2 <- et(amt = 0, ss = 1, rate = 10000 / 8, cmt = 2)
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB2)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB2, sensType = sensType)
test_that("one compartment bolus steady state to central compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI2)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI2, sensType = sensType)
test_that("one compartment infusion steady state to central compartment, tau", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR2)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR2, sensType = sensType)
test_that("one compartment infusion steady state to central compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
context(sprintf("Test steady state solutions 2 cmt ka (%s)", .txt))
ode.2c.ka <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
},
linCmtSens = sens
)
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
},
linCmtSens = sens
)
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB, sensType = sensType)
test_that("two compartment bolus steady state to depot compartment", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI, sensType = sensType)
test_that("two compartment infusion steady state to depot compartment, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR, sensType = sensType)
test_that("two compartment infusion steady state to depot compartment, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB2)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB2, sensType = sensType)
test_that("two compartment bolus steady state to central compartment", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI2)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI2, sensType = sensType)
test_that("two compartment infusion steady state to central compartment, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR2)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR2, sensType = sensType)
test_that("two compartment infusion steady state to central compartment", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
context(sprintf("Test steady state solutions 3 cmt ka (%s)", .txt))
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
},
linCmtSens = sens
)
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB, sensType = sensType)
test_that("three compartment bolus steady state to depot compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI, sensType = sensType)
test_that("three compartment infusion steady state to depot compartment, tau", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR, sensType = sensType)
test_that("three compartment infusion steady state to depot compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
## B2
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB2)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB2, sensType = sensType)
test_that("three compartment bolus steady state to central compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI2)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI2, sensType = sensType)
test_that("three compartment infusion steady state to central compartment, tau", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR2)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR2, sensType = sensType)
test_that("three compartment infusion steady state to central compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
context(sprintf("Test the solved equations 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
test_that("ode model gives extraCmt=0", {
expect_equal(rxModelVars(ode.1c)$extraCmt, 0L)
})
goodP <- function(model, cmt = 1L, ka = 0L) {
test_that(sprintf("model '%s' parses to cmt=%d, ka=%d", as.character(substitute(model)), cmt, ka), {
.flags <- rxModelVars(model)$flags
expect_equal(setNames(.flags["ncmt"], NULL), cmt)
expect_equal(setNames(.flags["ka"], NULL), ka)
})
}
## Solved systems can check the variables in the RxODE statement
## to figure out what type of solved system is being requested
ode.1cs <- RxODE(
{
V <- theta[1]
CL <- theta[2]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.1cs)
context(sprintf("Test the solved equations 2 cmt (%s)", .txt))
ode.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
K <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cK)
ode.2cA1 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
alpha <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA1)
ode.2cA2 <- RxODE(
{
A <- 1 / theta[1]
CLx <- theta[2]
alpha <- CLx * A
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA2)
## Instead of specifying parameters in the solved system, you can
## specify them in the linCmt variable.
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
goodP(ode.1cs2)
test_that("linear compartment model gives extraCmt=1", {
expect_equal(rxModelVars(ode.1cs2)$extraCmt, 1L)
})
## The solved systems can be mixed with ODE solving routines (to
## speed them up a bit...?)
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.2c <- ode.1cs %>% solve(theta = c(20, 25), events = et, sensType = sensType)
s.2cK <- ode.2cK %>% solve(theta = c(20, 25), events = et, sensType = sensType)
s.2cA1 <- ode.2cA1 %>% solve(theta = c(20, 25), events = et, sensType = sensType)
s.2cA2 <- ode.2cA2 %>% solve(theta = c(20, 25), events = et, sensType = sensType)
test_that("Gives the correct parameters for THETAs", {
expect_equal(
s.2c$params,
structure(list("THETA[1]" = 20, "THETA[2]" = 25),
class = "data.frame",
row.names = c(NA, -1L)
)
)
})
test_that("1 compartment solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA2$C2, tolerance = tol)
})
## Test steady state doses.
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 1), events = etSs, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 1), events = etSs, sensType = sensType)
s.2c <- ode.1cs %>% solve(theta = c(20, 1), events = etSs, sensType = sensType)
test_that("1 compartment steady-state solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
ode.1c.ka <- RxODE({
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
})
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
},
linCmtSens = sens
)
goodP(sol.1c.ka, ka = 1L)
ode.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
Ka <- theta[3]
K <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cK, ka = 1L)
ode.2cA1 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
Ka <- theta[3]
alpha <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA1, ka = 1L)
ode.2cA2 <- RxODE(
{
A <- 1 / theta[1]
CLx <- theta[2]
Ka <- theta[3]
alpha <- CLx * A
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA2, ka = 1L)
test_that("linear oral model gives extraCmt=2", {
expect_equal(rxModelVars(sol.1c.ka)$extraCmt, 2L)
})
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
s.2cK <- ode.2cK %>% solve(theta = c(20, 25, KA = 2), events = et, sensType = sensType)
s.2cA1 <- ode.2cA1 %>% solve(theta = c(20, 25, KA = 2), events = et, sensType = sensType)
s.2cA2 <- ode.2cA2 %>% solve(theta = c(20, 25, KA = 2), events = et, sensType = sensType)
test_that("1 compartment oral solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA2$C2, tolerance = tol)
})
## Note the strange-looking dip at 4 hours. This is because ss=1 resets the system first.
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 2, KA = 2), events = etSs, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 2, KA = 2), events = etSs, sensType = sensType)
test_that("1 compartment oral solved models steady state ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q1)
},
linCmtSens = sens
)
goodP(sol.2c, cmt = 2L)
sol.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cK, cmt = 2L)
## A1 in terms of A, alpha, B, beta
sol.2cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
A <- (alpha - K21x) / (alpha - beta) / Vx
B <- (beta - K21x) / (beta - alpha) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA1, cmt = 2L)
## A2 V, alpha, beta, k21
sol.2cA2 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Kx <- CLx / V
K12x <- Qx / V
K21 <- Qx / V2x
beta <- 0.5 * (K12x + K21 + Kx -
sqrt((K12x +
K21 + Kx) * (K12x + K21 + Kx) - 4 * K21 *
Kx))
alpha <- K21 * Kx / beta
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA2, cmt = 2L)
## A3 alpha, beta, aob
sol.2cA3 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Kx <- CLx / V
K12x <- Qx / V
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
Ax <- (alpha - K21x) / (alpha - beta) / V
Bx <- (beta - K21x) / (beta - alpha) / V
aob <- Ax / Bx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA3, cmt = 2L)
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = et, sensType = sensType)
s.2cK <- sol.2cK %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2cA1 <- sol.2cA1 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2cA2 <- sol.2cA2 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2cA3 <- sol.2cA3 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same.", {
expect_equal(s.2cK$C2, s.2c$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA2$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA3$C2, tolerance = tol)
})
ode.2c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
},
linCmtSens = sens
)
goodP(sol.2c.ka, cmt = 2, ka = 1)
sol.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cK, cmt = 2, ka = 1)
## A1 in terms of A, alpha, B, beta
sol.2cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
A <- (alpha - K21x) / (alpha - beta) / Vx
B <- (beta - K21x) / (beta - alpha) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA1, cmt = 2, ka = 1)
## A2 V, alpha, beta, k21
sol.2cA2 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
Kx <- CLx / V
K12x <- Qx / V
K21 <- Qx / V2x
beta <- 0.5 * (K12x + K21 + Kx -
sqrt((K12x +
K21 + Kx) * (K12x + K21 + Kx) - 4 * K21 *
Kx))
alpha <- K21 * Kx / beta
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA2, cmt = 2, ka = 1)
## A3 alpha, beta, aob
sol.2cA3 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
Kx <- CLx / V
K12x <- Qx / V
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
Ax <- (alpha - K21x) / (alpha - beta) / V
Bx <- (beta - K21x) / (beta - alpha) / V
aob <- Ax / Bx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA3, cmt = 2, ka = 1)
sol.2cSS <- RxODE(
{
V <- theta[1]
CL <- theta[2]
V2x <- theta[3]
Q <- theta[4]
Ka <- theta[5]
Vss <- V + V2x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cSS, cmt = 2, ka = 1)
sol.2cT <- RxODE(
{
V <- theta[1]
CL <- theta[2]
VT <- theta[3]
Q <- theta[4]
Ka <- theta[5]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cT, cmt = 2, ka = 1)
o.2c <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
s.2cK <- sol.2cK %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cA1 <- sol.2cA1 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cA2 <- sol.2cA2 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cA3 <- sol.2cA3 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cSS <- sol.2cSS %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cT <- sol.2cT %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
test_that("2 compartment oral solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA2$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA3$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cSS$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cT$C2, tolerance = tol)
})
o.2c <- ode.2c.ka %>% solve(params = c(V = 40, CL = 1, V2 = 297, Q = 10, KA = 0.3), events = etSs, sensType = sensType)
s.2c <- sol.2c.ka %>% solve(params = c(V = 40, CL = 1, V2 = 297, Q = 10, KA = 0.3), events = etSs, sensType = sensType)
test_that("2 compartment oral steady-state solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Test the solved equations 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
goodP(sol.3c, 3)
sol.3cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
k13 <- Q2x / V
k31 <- Q2x / V3x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cK, 3)
sol.3cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
K13x <- Q2x / Vx
K31x <- Q2x / V3x
a0 <- Kx * K21x * K31x
a1 <- Kx * K31x + K21x * K31x + K21x * K13x +
Kx * K21x + K31x * K12x
a2 <- Kx + K12x + K13x + K21x + K31x
p <- a1 - a2 * a2 / 3
qq <- 2 * a2 * a2 * a2 / 27 - a1 * a2 / 3 + a0
r1 <- sqrt(-p * p * p / 27)
r2 <- 2 * r1^(1 / 3)
theta <- acos(-qq / (2 * r1)) / 3
alpha <- -(cos(theta) * r2 - a2 / 3)
beta <- -(cos(theta + 2 / 3 * pi) * r2 - a2 / 3)
gamma <- -(cos(theta + 4 / 3 * pi) * r2 - a2 / 3)
A <- (K21x - alpha) * (K31x - alpha) / (alpha - beta) / (alpha - gamma) / Vx
B <- (K21x - beta) * (K31x - beta) / (beta - alpha) / (beta - gamma) / Vx
C <- (K21x - gamma) * (K31x - gamma) / (gamma - alpha) / (gamma - beta) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cA1, 3)
sol.3cVp <- RxODE(
{
V <- theta[1]
CL <- theta[2]
Vp <- theta[3]
Q <- theta[4]
Q2 <- theta[5]
Vp2 <- theta[6]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cVp, 3)
sol.3cVt <- RxODE(
{
V <- theta[1]
CL <- theta[2]
Vt <- theta[3]
Q <- theta[4]
Q2 <- theta[5]
Vt2 <- theta[6]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cVt, 3)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cK <- sol.3cK %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cA1 <- sol.3cA1 %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cVp <- sol.3cVp %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cVt <- sol.3cVt %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cK$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cA1$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cVp$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cVt$C2, tolerance = tol)
})
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
test_that("3 compartment solved models and ODEs same with steady state.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
sol.3cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
ka <- theta[7]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
k13 <- Q2x / V
k31 <- Q2x / V3x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cK, 3, 1)
sol.3cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
ka <- theta[7]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
K13x <- Q2x / Vx
K31x <- Q2x / V3x
a0 <- Kx * K21x * K31x
a1 <- Kx * K31x + K21x * K31x + K21x * K13x +
Kx * K21x + K31x * K12x
a2 <- Kx + K12x + K13x + K21x + K31x
p <- a1 - a2 * a2 / 3
qq <- 2 * a2 * a2 * a2 / 27 - a1 * a2 / 3 + a0
r1 <- sqrt(-p * p * p / 27)
r2 <- 2 * r1^(1 / 3)
theta <- acos(-qq / (2 * r1)) / 3
alpha <- -(cos(theta) * r2 - a2 / 3)
beta <- -(cos(theta + 2 / 3 * pi) * r2 - a2 / 3)
gamma <- -(cos(theta + 4 / 3 * pi) * r2 - a2 / 3)
A <- (K21x - alpha) * (K31x - alpha) / (alpha - beta) / (alpha - gamma) / Vx
B <- (K21x - beta) * (K31x - beta) / (beta - alpha) / (beta - gamma) / Vx
C <- (K21x - gamma) * (K31x - gamma) / (gamma - alpha) / (gamma - beta) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cA1, 3, 1)
o.3c <- ode.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3cK <- sol.3cK %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3cA1 <- sol.3cA1 %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
test_that("3 compartment oral solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cK$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cA1$C2, tolerance = tol)
})
o.3c <- ode.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSs, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSs, sensType = sensType)
## Again the 4 hour strange discontinuity because ss=1
test_that("3 compartment oral solved models and ODEs same for steady state.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Infusion Models 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
etSs <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
## Solved systems can check the variables in the RxODE statement
## to figure out what type of solved system is being requested
ode.1cs <- RxODE(
{
V <- theta[1]
CL <- theta[2]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.1cs, 1)
## Instead of specifying parameters in the solved system, you can
## specify them in the linCmt variable.
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
goodP(ode.1cs2, 1)
## The solved systems can be mixed with ODE solving routines (to
## speed them up a bit...?)
o.1c <- ode.1c %>%
solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.1c <- ode.1cs2 %>%
solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.2c <- ode.1cs %>%
solve(theta = c(20, 25), events = et, sensType = sensType)
test_that("1 compartment solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
o.1c <- ode.1c %>%
solve(params = c(V = 20, CL = 10), events = etSs, sensType = sensType)
s.1c <- ode.1cs2 %>%
solve(params = c(V = 20, CL = 10), events = etSs, sensType = sensType)
s.2c <- ode.1cs %>%
solve(theta = c(20, 10), events = etSs, sensType = sensType)
test_that("1 compartment solved models and ODEs same; Steady State", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion Models 2 cmt (%s)", .txt))
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
},
linCmtSens = sens
)
goodP(sol.2c, 2)
o.2c <- ode.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2c <- sol.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
o.2c <- ode.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSs, sensType = sensType)
s.2c <- sol.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSs, sensType = sensType)
test_that("2 compartment steady state solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion Models 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
goodP(sol.3c, 3)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
test_that("3 compartment steady state solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Infusion + Bolus 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.dosing(dose = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
## Solved systems can check the variables in the RxODE statement
## to figure out what type of solved system is being requested
ode.1cs <- RxODE(
{
V <- theta[1]
CL <- theta[2]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.1cs, 1)
## Instead of specifying parameters in the solved system, you can
## specify them in the linCmt variable.
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
goodP(ode.1cs2, 1)
## The solved systems can be mixed with ODE solving routines (to
## speed them up a bit...?)
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.2c <- ode.1cs %>% solve(theta = c(20, 25), events = et, sensType = sensType)
test_that("1 compartment solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion + Bolus 2 cmt (%s)", .txt))
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
},
linCmtSens = sens
)
goodP(sol.2c, 2)
o.2c <- ode.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2c <- sol.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion + Bolus 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
goodP(sol.3c, 3)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Oral + Infusion + Bolus Models 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c.ka <- RxODE({
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
})
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
},
linCmtSens = sens
)
goodP(sol.1c.ka, 1, 1)
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
test_that("1 compartment solved models and ODEs same for mixed oral, iv and infusion.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
context(sprintf("Oral + Infusion + Bolus Models 2 cmt (%s)", .txt))
ode.2c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
},
linCmtSens = sens
)
goodP(sol.2c.ka, 2, 1)
o.2c <- ode.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same for mixed oral, iv and infusion.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Oral + Infusion + Bolus Models 3 cmt (%s)", .txt))
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
o.3c <- ode.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same for mixed oral, iv and infusion.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Modeled bio-availability 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c.ka <- RxODE({
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
f(depot) <- fDepot
f(center) <- fCenter
})
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
f(depot) <- fDepot
f(central) <- fCenter
},
linCmtSens = sens
)
goodP(sol.1c.ka, 1, 1)
for (fd in c(0.5, 1, 2)) {
for (fc in c(0.5, 1, 2)) {
o.1c <- ode.1c.ka %>%
solve(params = c(V = 20, CL = 25, KA = 2, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>%
solve(params = c(V = 20, CL = 25, KA = 2, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled bio-availability 2 cmt (%s)", .txt))
ode.2c.ka <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
f(depot) <- fDepot
f(centr) <- fCenter
## FIXME:
## f(central) should throw an error
},
linCmtSens = sens
)
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
f(depot) <- fDepot
f(central) <- fCenter
},
linCmtSens = sens
)
goodP(sol.2c.ka, 2, 1)
for (fd in c(0.5, 1, 2)) {
for (fc in c(0.5, 1, 2)) {
o.2c <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled bio-availability 3 cmt (%s)", .txt))
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
f(depot) <- fDepot
f(centr) <- fCenter
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
f(depot) <- fDepot
f(central) <- fCenter
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
for (fd in c(0.5, 1, 2)) {
for (fc in c(0.5, 1, 2)) {
o.3c <- ode.3c.ka %>%
solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7,
V3 = 400, KA = 0.3, fDepot = fd, fCenter = fc
), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3,
fDepot = fd, fCenter = fc
), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled lag time 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c.ka <- RxODE(
{
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
alag(depot) <- lagDepot
alag(center) <- lagCenter
},
linCmtSens = sens
)
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
alag(depot) <- lagDepot
alag(central) <- lagCenter
},
linCmtSens = sens
)
goodP(sol.1c.ka, 1, 1)
for (fd in c(1, 2, 10)) {
for (fc in c(1, 2, 10)) {
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled lag time 2 cmt (%s)", .txt))
ode.2c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
alag(depot) <- lagDepot
alag(centr) <- lagCenter
})
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
alag(depot) <- lagDepot
alag(central) <- lagCenter
},
linCmtSens = sens
)
goodP(sol.2c.ka, 2, 1)
for (fd in c(1, 2, 10)) {
for (fc in c(1, 2, 10)) {
o.2c <- ode.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled lag time 3 cmt (%s)", .txt))
ode.3c.ka <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
alag(depot) <- lagDepot
alag(centr) <- lagCenter
},
linCmtSens = sens
)
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
alag(depot) <- lagDepot
alag(central) <- lagCenter
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
for (fd in c(1, 2, 10)) {
for (fc in c(1, 2, 10)) {
o.3c <- ode.3c.ka %>% solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7,
V3 = 400, KA = 0.3, lagDepot = fd, lagCenter = fc
), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>% solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3,
lagDepot = fd, lagCenter = fc
), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled rate 1 cmt (%s)", .txt))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
rate(center) <- rt
})
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
rate(central) <- rt
},
linCmtSens = sens
)
goodP(sol.1c, 1)
et <- eventTable() %>%
add.dosing(dose = 3, rate = -1, nbr.doses = 3, cmt = 1, dosing.interval = 12) %>%
add.sampling(seq(0, 36, length.out = 200))
for (rt in seq(0.5, 1, 1.5)) {
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25, rt = rt), events = et, sensType = sensType)
s.1c <- sol.1c %>% solve(params = c(V = 20, CL = 25, rt = rt), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for rate-modeled infusion: %s", rt), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
context(sprintf("Modeled rate 2 cmt (%s)", .txt))
ode.2c <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
rate(centr) <- rt
},
linCmtSens = sens
)
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
rate(central) <- rt
},
linCmtSens = sens
)
goodP(sol.2c, 2)
for (rt in seq(0.5, 1, 1.5)) {
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, rt = rt), events = et, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, rt = rt), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for rate-modeled infusion: %s", rt), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
context(sprintf("Modeled rate 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
rate(centr) <- rt
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
rate(central) <- rt
},
linCmtSens = sens
)
goodP(sol.3c, 3)
for (rt in seq(0.5, 1, 1.5)) {
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, rt = rt), events = et, sensType = sensType)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, rt = rt), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for rate-modeled infusion: %s", rt), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
context(sprintf("Modeled duration 1 cmt (%s)", .txt))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
dur(center) <- dr
})
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
dur(central) <- dr
},
linCmtSens = sens
)
goodP(sol.1c, 1)
et <- eventTable() %>%
add.dosing(dose = 3, rate = -2, nbr.doses = 3, cmt = 1, dosing.interval = 12) %>%
add.sampling(seq(0, 36, length.out = 200))
for (dur in seq(0.5, 1, 1.5)) {
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25, dr = dur), events = et, sensType = sensType)
s.1c <- sol.1c %>% solve(params = c(V = 20, CL = 25, dr = dur), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for dur-modeled infusion: %s", dur), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
context(sprintf("Modeled duration 2 cmt (%s)", .txt))
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
dur(centr) <- dr
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
dur(central) <- dr
},
linCmtSens = sens
)
goodP(sol.2c, 2)
for (dur in seq(0.5, 1, 1.5)) {
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, dr = dur), events = et, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, dr = dur), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for dur-modeled infusion: %s", dur), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
context(sprintf("Modeled duration 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
dur(centr) <- dr
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
dur(central) <- dr
},
linCmtSens = sens
)
goodP(sol.3c, 3)
for (dur in seq(0.5, 1, 1.5)) {
o.3c <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, dr = dur), events = et, sensType = sensType)
s.3c <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, dr = dur), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for dur-modeled infusion: %s", dur), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
test_that("central should throw error", {
expect_error(RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
alag(depot) <- lagDepot
alag(centr) <- lagCenter
alag(central) <- matt
}))
})
test_that("depot should throw error", {
expect_error(RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(dep) <- -KA * dep
d / dt(centr) <- KA * dep - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
alag(dep) <- lagDepot
alag(centr) <- lagCenter
alag(depot) <- matt
}))
})
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
mtime(t1) <- mt1
mtime(t2) <- mt2
},
linCmtSens = sens
)
goodP(ode.1cs2, 1)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(0:48)
s.1c <- ode.1cs2 %>% solve(
params = c(V = 20, CL = 25, mt1 = 0.5, mt2 = 1.75),
events = et, sensType = sensType
)
test_that("mtime with solved systems work", {
expect_equal(s.1c$time[1:4], c(0, 0.5, 1, 1.75))
})
context(sprintf("evid=3 (%s)", .txt))
test_that(sprintf("evid==3 (%s)", .txt), {
ode.1c <- RxODE(
{
C2 <- center / V
d / dt(center) <- -CL * C2
},
linCmtSens = sens
)
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
et(time = 25, evid = 3) %>%
add.sampling(seq(0, 48, length.out = 200))
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = et)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = et, sensType = sensType)
expect_equal(o1$C2, s1$C2, tol = tol)
})
}
if (length(types) > 1) {
test_that(
"double linCmt has error",
expect_error(RxODE({
C2 <- linCmt(CL, V)
C2 <- linCmt(CL, V)
}))
)
context(sprintf("Steady State Infusions (%s)", .txt))
test_that("Steady state IV infusion", {
ev <- et(amt = 0, ss = 1, rate = 10000 / 8)
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
ode.1cs2 <- RxODE({
C2 <- linCmt(CL, V)
})
goodP(ode.1cs2, 1)
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25), events = ev, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 25), events = ev, sensType = sensType)
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE({
C2 <- linCmt(V, CL, V2, Q1)
})
goodP(sol.2c, 2)
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = ev, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = ev, sensType = sensType)
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE({
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
})
goodP(sol.3c, 3)
o.3c <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = ev, sensType = sensType)
s.3c <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = ev, sensType = sensType)
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context("Issue #258")
test_that("Issue #258", {
m258 <- RxODE(
{
ka <- 1
cl <- 3.5
vc <- 40
Conc <- linCmt()
alag(depot) <- 1
},
linCmtSens = sens
)
m258o <- RxODE({
ka <- 1
cl <- 3.5
vc <- 40
d / dt(depot) <- -ka * depot
d / dt(central) <- ka * depot - cl / vc * central
Conc <- central / vc
alag(depot) <- 1
})
s1 <- m258 %>%
et(dose = 100, time = 0, addl = 6, ii = 24) %>%
et(0, 250, by = 0.1) %>%
rxSolve(sensType = sensType)
s2 <- m258o %>%
et(dose = 100, time = 0, addl = 6, ii = 24) %>%
et(0, 250, by = 0.1) %>%
rxSolve()
expect_equal(s1$Conc, s2$Conc, tol = tol)
})
tol <- 1e-5 ## Current difference for all equations
type <- 1
## forward/central differences don't work here...
for (type in c(1, 4)) {
sens <- switch(type,
"autodiff",
"forward",
"central",
"advan"
)
context(sprintf("1 cmt sensitivities (%s)", sens))
test_that("1 compartment sensitivities; IV bolus, Cl, V", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
prop.err <- THETA[3]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl)
})
pk1s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- center / Vc
d / dt(center) <- -Cl * Central
})
pk1o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
parms <- c(
"THETA[1]" = 20, "THETA[2]" = 25, "ETA[1]" = 1, "ETA[2]" = 1,
"THETA[3]" = 0.2
)
s1 <- rxSolve(pk1s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
## ETA1, ETA2 is zero for central
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
## ETA1, ETA2 is zero for central
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
})
## Now oral
test_that("1 compartment sensitivities; Oral Cl, V, Ka", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lKa <- THETA[3]
prop.err <- THETA[4]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Ka <- ETA[3]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Ka <- exp(lKa + eta.Ka)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Ka)
})
pk1s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- center / Vc
d / dt(depot) <- -Ka * depot
d / dt(center) <- Ka * depot - Cl * Central
})
pk1o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
parms <- c(
"THETA[1]" = log(20), "THETA[2]" = log(25), "THETA[3]" = log(2),
"ETA[1]" = 0, "ETA[2]" = 0, "ETA[3]" = 0,
"THETA[4]" = 0.2
)
s1 <- rxSolve(pk1s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etSs, sensType = sens)
## library(patchwork)
## plot(s1, rx_pred_) / plot(o1, rx_pred_)
## plot(s1, rx__sens_rx_pred__BY_ETA_1___) / plot(o1, rx__sens_rx_pred__BY_ETA_1___)
## plot(s1, rx__sens_rx_pred__BY_ETA_2___) / plot(o1, rx__sens_rx_pred__BY_ETA_2___)
## plot(s1, rx__sens_rx_pred__BY_ETA_3___) / plot(o1, rx__sens_rx_pred__BY_ETA_3___)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 2) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etMix <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etMix, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etMix, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
})
context(sprintf("2 cmt sensitivities (%s)", sens))
test_that("2 compartment sensitivities; IV bolus Cl, Vc, Q, Vp", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
prop.err <- THETA[5]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
}
pk2 <- function() {
lK10 <- THETA[1]
lVc <- THETA[2]
lK12 <- THETA[3]
lK21 <- THETA[4]
prop.err <- THETA[5]
eta.Vc <- ETA[1]
eta.K10 <- ETA[2]
eta.K12 <- ETA[3]
eta.K21 <- ETA[4]
Vc <- exp(lVc + eta.Vc)
k10 <- exp(lK10 + eta.K10)
k12 <- exp(lK12 + eta.K12)
k21 <- exp(lK21 + eta.K21)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q)
})
modK <- RxODE({
Central <- linCmt(Vc, k10, k12, k21)
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
pk2sK <- rxSymPySetupPred(modK, predfn = pred, pkpars = pk2, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
d / dt(centr) <- -Cl * Central - Q * Central + Q * C3
d / dt(peri) <- Q * Central - Q * C3
})
modK2 <- RxODE({
Central <- centr / Vc
d / dt(centr) <- -k10 * centr - k12 * centr + k21 * peri
d / dt(peri) <- k12 * centr - k21 * peri
})
pk2o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
pk2oK <- rxSymPySetupPred(modK2, predfn = pred, pkpars = pk2, err = err)
parms2 <- c(
"THETA[1]" = log(18 / 40), "THETA[2]" = log(40),
"THETA[3]" = log(10 / 40), "THETA[4]" = log(10 / 297),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"THETA[5]" = 0.2
)
parms <- c(
"THETA[1]" = log(18), "THETA[2]" = log(40),
"THETA[3]" = log(10), "THETA[4]" = log(297),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"THETA[5]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, et, sensType = sens)
s1 <- rxSolve(pk2sK$inner, parms2, et, sensType = sens)
o1 <- rxSolve(pk2oK$inner, parms2, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tol = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
})
test_that("2 compartment sensitivities; Oral Cl, Vc, Q, Vp, Ka", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lKa <- THETA[5]
prop.err <- THETA[6]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Ka <- ETA[5]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Ka <- exp(lKa + eta.Ka)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Ka)
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
d / dt(depot) <- -Ka * depot
d / dt(centr) <- Ka * depot - Cl * Central - Q * Central + Q * C3
d / dt(peri) <- Q * Central - Q * C3
})
pk2o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
parms <- c(
"THETA[1]" = log(18), ## Cl
"THETA[2]" = log(40), ## Vc
"THETA[3]" = log(10), ## Q
"THETA[4]" = log(297), ## Vp
"THETA[5]" = log(0.3), ## Ka
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0,
"THETA[6]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 2) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 1) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 1) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 1) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etMix <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etMix, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etMix, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
})
context(sprintf("3 cmt sensitivities (%s)", sens))
test_that("3 compartment sensitivities; IV bolus Cl, Vc, Q, Vp", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lQ2 <- THETA[5]
lVp2 <- THETA[6]
prop.err <- THETA[7]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Q2 <- ETA[5]
eta.Vp2 <- ETA[6]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Q2 <- exp(lQ2 + eta.Q2)
Vp2 <- exp(lVp2 + eta.Vp2)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Vp2, Q2)
})
pk3s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
C4 <- peri2 / Vp2
d / dt(centr) <- -Cl * Central - Q * Central + Q * C3 + Q2 * C4 - Q2 * Central
d / dt(peri) <- Q * Central - Q * C3
d / dt(peri2) <- Q2 * Central - Q2 * C4
})
pk3o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
parms <- c(
"THETA[1]" = log(18), "THETA[2]" = log(40),
"THETA[3]" = log(10), "THETA[4]" = log(297),
"THETA[5]" = log(7), "THETA[6]" = log(400),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0, "ETA[6]" = 0,
"THETA[7]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 1) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 1) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 1) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
})
test_that("3 compartment sensitivities; Oral Cl, Vc, Q, Vp, Ka", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lQ2 <- THETA[5]
lVp2 <- THETA[6]
lKa <- THETA[7]
prop.err <- THETA[8]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Q2 <- ETA[5]
eta.Vp2 <- ETA[6]
eta.Ka <- ETA[7]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Q2 <- exp(lQ2 + eta.Q2)
Vp2 <- exp(lVp2 + eta.Vp2)
Ka <- exp(lKa + eta.Ka)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Vp2, Q2, Ka)
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
C4 <- peri2 / Vp2
d / dt(depot) <- -Ka * depot
d / dt(centr) <- Ka * depot - Cl * Central - Q * Central + Q * C3 - Q2 * Central + Q2 * C4
d / dt(peri) <- Q * Central - Q * C3
d / dt(peri2) <- Q2 * Central - Q2 * C4
})
pk2o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
parms <- c(
"THETA[1]" = log(18), "THETA[2]" = log(40),
"THETA[3]" = log(10), "THETA[4]" = log(297),
"THETA[5]" = log(7), "THETA[6]" = log(400),
"THETA[7]" = log(0.3),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0, "ETA[6]" = 0, "ETA[7]" = 0,
"THETA[8]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 2) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 1) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 1) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 1) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etMix <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etMix, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etMix, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
})
}
context("Make sure central differences are working")
test_that("central differences", {
## One compartment model without oral absorption
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
prop.err <- THETA[3]
lF <- THETA[4]
lLag <- THETA[5]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.f <- ETA[3]
eta.lag <- ETA[4]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
fc <- exp(lF + eta.f)
lagc <- exp(lLag + eta.lag)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl)
f(central) <- fc
alag(central) <- lagc
})
parms <- c(
"THETA[1]" = 20, "THETA[2]" = 25, "ETA[1]" = 1, "ETA[2]" = 1,
"THETA[3]" = 0.2, "THETA[4]" = log(1), "THETA[5]" = log(0.5),
"ETA[3]" = 1, "ETA[4]" = 1
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
s1 <- rxSolve(pk2s$inner, parms, et, sensType = "advan")
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_3___)))
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_4___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_3___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_4___)))
## One compartment model with oral absorption
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lKa <- THETA[5]
prop.err <- THETA[6]
lF <- THETA[7]
lLag <- THETA[8]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Ka <- ETA[5]
eta.F <- ETA[6]
eta.lag <- ETA[7]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Ka <- exp(lKa + eta.Ka)
F <- exp(lF + eta.F)
lag <- exp(lLag + eta.lag)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Ka)
f(depot) <- F
alag(depot) <- lag
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
parms <- c(
"THETA[1]" = log(18), ## Cl
"THETA[2]" = log(40), ## Vc
"THETA[3]" = log(10), ## Q
"THETA[4]" = log(297), ## Vp
"THETA[5]" = log(0.3), ## Ka
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0,
"THETA[6]" = 0.2, "THETA[7]" = log(1.5), "THETA[8]" = log(0.5),
"ETA[6]" = 0, "ETA[7]" = 0
)
s1 <- rxSolve(pk2s$inner, parms, et, sensType = "advan")
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_6___)))
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_7___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_6___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_7___)))
})
}
},
silent = TRUE,
test = "lincmt"
)
nlmixrdevelopment/RxODE documentation built on April 10, 2022, 5:36 a.m.
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rxodeTest(
{
tol <- 5e-6 ## Current difference for all equations
type <- 2
types <- 1:4
for (type in types) {
.txt <- switch(type,
"linear",
"sensitivity",
"linear [no save]",
"advanSens"
)
sens <- switch(type,
"linCmtA",
"linCmtB",
"linCmtC",
"linCmtB"
)
sensType <- switch(type,
"autodiff",
"autodiff",
"autodiff",
"advan"
)
etSsB <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
etSsI <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
etSsR <- et(amt = 0, ss = 1, rate = 10000 / 8)
ode.1c <- RxODE(
{
C2 <- center / V
d / dt(center) <- -CL * C2
},
linCmtSens = sens
)
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
context(sprintf("Test steady state solutions 1 cmt (%s)", .txt))
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = etSsB)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = etSsB, sensType = sensType)
test_that("one compartment bolus steady state", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = etSsI)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = etSsI, sensType = sensType)
test_that("one compartment infusion tau steady state", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = etSsR)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = etSsR, sensType = sensType)
test_that("one compartment infusion steady state", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
context(sprintf("Test steady state solutions 2 cmt (%s)", .txt))
ode.2c <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
},
linCmtSens = sens
)
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q1)
},
linCmtSens = sens
)
o2 <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSsB)
s2 <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = etSsB, sensType = sensType)
test_that("two compartment bolus steady state", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSsI)
s2 <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = etSsI, sensType = sensType)
test_that("two compartment infusion steady state, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSsR)
s2 <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = etSsR, sensType = sensType)
test_that("two compartment infusion steady state", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
context(sprintf("Test steady state solutions 3 cmt (%s)", .txt))
ode.3c <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
},
linCmtSens = sens
)
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
o3 <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsB)
s3 <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsB, sensType = sensType)
test_that("three compartment bolus steady state", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsI)
s3 <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsI, sensType = sensType)
test_that("three compartment bolus steady state", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsR)
s3 <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSsR, sensType = sensType)
test_that("three compartment bolus steady state", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
context(sprintf("Test steady state solutions 1 cmt ka (%s)", .txt))
ode.1c.ka <- RxODE(
{
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
},
linCmtSens = sens
)
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
},
linCmtSens = sens
)
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB, sensType = sensType)
test_that("one compartment bolus steady state to depot compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI, sensType = sensType)
test_that("one compartment infusion steady state to depot compartment, tau", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR, sensType = sensType)
test_that("one compartment infusion steady state to depot compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
etSsB2 <- et() %>%
et(amt = 3, cmt = 2) %>%
et(time = 4, amt = 3, ss = 1, ii = 24, cmt = 2) %>%
et(amt = 3, ss = 2, ii = 24, time = 8, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
etSsI2 <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
etSsR2 <- et(amt = 0, ss = 1, rate = 10000 / 8, cmt = 2)
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB2)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsB2, sensType = sensType)
test_that("one compartment bolus steady state to central compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI2)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsI2, sensType = sensType)
test_that("one compartment infusion steady state to central compartment, tau", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
o1 <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR2)
s1 <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = etSsR2, sensType = sensType)
test_that("one compartment infusion steady state to central compartment", {
expect_equal(o1$C2, s1$C2, tol = tol)
})
context(sprintf("Test steady state solutions 2 cmt ka (%s)", .txt))
ode.2c.ka <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
},
linCmtSens = sens
)
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
},
linCmtSens = sens
)
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB, sensType = sensType)
test_that("two compartment bolus steady state to depot compartment", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI, sensType = sensType)
test_that("two compartment infusion steady state to depot compartment, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR, sensType = sensType)
test_that("two compartment infusion steady state to depot compartment, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB2)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsB2, sensType = sensType)
test_that("two compartment bolus steady state to central compartment", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI2)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsI2, sensType = sensType)
test_that("two compartment infusion steady state to central compartment, tau", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
o2 <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR2)
s2 <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = etSsR2, sensType = sensType)
test_that("two compartment infusion steady state to central compartment", {
expect_equal(o2$C2, s2$C2, tol = tol)
})
context(sprintf("Test steady state solutions 3 cmt ka (%s)", .txt))
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
},
linCmtSens = sens
)
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB, sensType = sensType)
test_that("three compartment bolus steady state to depot compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI, sensType = sensType)
test_that("three compartment infusion steady state to depot compartment, tau", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR, sensType = sensType)
test_that("three compartment infusion steady state to depot compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
## B2
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB2)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsB2, sensType = sensType)
test_that("three compartment bolus steady state to central compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI2)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsI2, sensType = sensType)
test_that("three compartment infusion steady state to central compartment, tau", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
o3 <- ode.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR2)
s3 <- sol.3c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSsR2, sensType = sensType)
test_that("three compartment infusion steady state to central compartment", {
expect_equal(o3$C2, s3$C2, tol = tol)
})
context(sprintf("Test the solved equations 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
test_that("ode model gives extraCmt=0", {
expect_equal(rxModelVars(ode.1c)$extraCmt, 0L)
})
goodP <- function(model, cmt = 1L, ka = 0L) {
test_that(sprintf("model '%s' parses to cmt=%d, ka=%d", as.character(substitute(model)), cmt, ka), {
.flags <- rxModelVars(model)$flags
expect_equal(setNames(.flags["ncmt"], NULL), cmt)
expect_equal(setNames(.flags["ka"], NULL), ka)
})
}
## Solved systems can check the variables in the RxODE statement
## to figure out what type of solved system is being requested
ode.1cs <- RxODE(
{
V <- theta[1]
CL <- theta[2]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.1cs)
context(sprintf("Test the solved equations 2 cmt (%s)", .txt))
ode.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
K <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cK)
ode.2cA1 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
alpha <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA1)
ode.2cA2 <- RxODE(
{
A <- 1 / theta[1]
CLx <- theta[2]
alpha <- CLx * A
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA2)
## Instead of specifying parameters in the solved system, you can
## specify them in the linCmt variable.
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
goodP(ode.1cs2)
test_that("linear compartment model gives extraCmt=1", {
expect_equal(rxModelVars(ode.1cs2)$extraCmt, 1L)
})
## The solved systems can be mixed with ODE solving routines (to
## speed them up a bit...?)
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.2c <- ode.1cs %>% solve(theta = c(20, 25), events = et, sensType = sensType)
s.2cK <- ode.2cK %>% solve(theta = c(20, 25), events = et, sensType = sensType)
s.2cA1 <- ode.2cA1 %>% solve(theta = c(20, 25), events = et, sensType = sensType)
s.2cA2 <- ode.2cA2 %>% solve(theta = c(20, 25), events = et, sensType = sensType)
test_that("Gives the correct parameters for THETAs", {
expect_equal(
s.2c$params,
structure(list("THETA[1]" = 20, "THETA[2]" = 25),
class = "data.frame",
row.names = c(NA, -1L)
)
)
})
test_that("1 compartment solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA2$C2, tolerance = tol)
})
## Test steady state doses.
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 1), events = etSs, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 1), events = etSs, sensType = sensType)
s.2c <- ode.1cs %>% solve(theta = c(20, 1), events = etSs, sensType = sensType)
test_that("1 compartment steady-state solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
ode.1c.ka <- RxODE({
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
})
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
},
linCmtSens = sens
)
goodP(sol.1c.ka, ka = 1L)
ode.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
Ka <- theta[3]
K <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cK, ka = 1L)
ode.2cA1 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
Ka <- theta[3]
alpha <- CLx / V
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA1, ka = 1L)
ode.2cA2 <- RxODE(
{
A <- 1 / theta[1]
CLx <- theta[2]
Ka <- theta[3]
alpha <- CLx * A
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.2cA2, ka = 1L)
test_that("linear oral model gives extraCmt=2", {
expect_equal(rxModelVars(sol.1c.ka)$extraCmt, 2L)
})
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
s.2cK <- ode.2cK %>% solve(theta = c(20, 25, KA = 2), events = et, sensType = sensType)
s.2cA1 <- ode.2cA1 %>% solve(theta = c(20, 25, KA = 2), events = et, sensType = sensType)
s.2cA2 <- ode.2cA2 %>% solve(theta = c(20, 25, KA = 2), events = et, sensType = sensType)
test_that("1 compartment oral solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2cA2$C2, tolerance = tol)
})
## Note the strange-looking dip at 4 hours. This is because ss=1 resets the system first.
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 2, KA = 2), events = etSs, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 2, KA = 2), events = etSs, sensType = sensType)
test_that("1 compartment oral solved models steady state ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q1)
},
linCmtSens = sens
)
goodP(sol.2c, cmt = 2L)
sol.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cK, cmt = 2L)
## A1 in terms of A, alpha, B, beta
sol.2cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
A <- (alpha - K21x) / (alpha - beta) / Vx
B <- (beta - K21x) / (beta - alpha) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA1, cmt = 2L)
## A2 V, alpha, beta, k21
sol.2cA2 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Kx <- CLx / V
K12x <- Qx / V
K21 <- Qx / V2x
beta <- 0.5 * (K12x + K21 + Kx -
sqrt((K12x +
K21 + Kx) * (K12x + K21 + Kx) - 4 * K21 *
Kx))
alpha <- K21 * Kx / beta
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA2, cmt = 2L)
## A3 alpha, beta, aob
sol.2cA3 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Kx <- CLx / V
K12x <- Qx / V
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
Ax <- (alpha - K21x) / (alpha - beta) / V
Bx <- (beta - K21x) / (beta - alpha) / V
aob <- Ax / Bx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA3, cmt = 2L)
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = et, sensType = sensType)
s.2cK <- sol.2cK %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2cA1 <- sol.2cA1 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2cA2 <- sol.2cA2 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2cA3 <- sol.2cA3 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same.", {
expect_equal(s.2cK$C2, s.2c$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA2$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA3$C2, tolerance = tol)
})
ode.2c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
},
linCmtSens = sens
)
goodP(sol.2c.ka, cmt = 2, ka = 1)
sol.2cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cK, cmt = 2, ka = 1)
## A1 in terms of A, alpha, B, beta
sol.2cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
A <- (alpha - K21x) / (alpha - beta) / Vx
B <- (beta - K21x) / (beta - alpha) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA1, cmt = 2, ka = 1)
## A2 V, alpha, beta, k21
sol.2cA2 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
Kx <- CLx / V
K12x <- Qx / V
K21 <- Qx / V2x
beta <- 0.5 * (K12x + K21 + Kx -
sqrt((K12x +
K21 + Kx) * (K12x + K21 + Kx) - 4 * K21 *
Kx))
alpha <- K21 * Kx / beta
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA2, cmt = 2, ka = 1)
## A3 alpha, beta, aob
sol.2cA3 <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
ka <- theta[5]
Kx <- CLx / V
K12x <- Qx / V
K21x <- Qx / V2x
beta <- 0.5 * (K12x + K21x + Kx -
sqrt((K12x +
K21x + Kx) * (K12x + K21x + Kx) - 4 * K21x *
Kx))
alpha <- K21x * Kx / beta
Ax <- (alpha - K21x) / (alpha - beta) / V
Bx <- (beta - K21x) / (beta - alpha) / V
aob <- Ax / Bx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cA3, cmt = 2, ka = 1)
sol.2cSS <- RxODE(
{
V <- theta[1]
CL <- theta[2]
V2x <- theta[3]
Q <- theta[4]
Ka <- theta[5]
Vss <- V + V2x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cSS, cmt = 2, ka = 1)
sol.2cT <- RxODE(
{
V <- theta[1]
CL <- theta[2]
VT <- theta[3]
Q <- theta[4]
Ka <- theta[5]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.2cT, cmt = 2, ka = 1)
o.2c <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
s.2cK <- sol.2cK %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cA1 <- sol.2cA1 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cA2 <- sol.2cA2 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cA3 <- sol.2cA3 %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cSS <- sol.2cSS %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
s.2cT <- sol.2cT %>% solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, ka = 0.3), events = et, sensType = sensType)
test_that("2 compartment oral solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cK$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA1$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA2$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cA3$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cSS$C2, tolerance = tol)
expect_equal(o.2c$C2, s.2cT$C2, tolerance = tol)
})
o.2c <- ode.2c.ka %>% solve(params = c(V = 40, CL = 1, V2 = 297, Q = 10, KA = 0.3), events = etSs, sensType = sensType)
s.2c <- sol.2c.ka %>% solve(params = c(V = 40, CL = 1, V2 = 297, Q = 10, KA = 0.3), events = etSs, sensType = sensType)
test_that("2 compartment oral steady-state solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Test the solved equations 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
goodP(sol.3c, 3)
sol.3cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
k13 <- Q2x / V
k31 <- Q2x / V3x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cK, 3)
sol.3cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
K13x <- Q2x / Vx
K31x <- Q2x / V3x
a0 <- Kx * K21x * K31x
a1 <- Kx * K31x + K21x * K31x + K21x * K13x +
Kx * K21x + K31x * K12x
a2 <- Kx + K12x + K13x + K21x + K31x
p <- a1 - a2 * a2 / 3
qq <- 2 * a2 * a2 * a2 / 27 - a1 * a2 / 3 + a0
r1 <- sqrt(-p * p * p / 27)
r2 <- 2 * r1^(1 / 3)
theta <- acos(-qq / (2 * r1)) / 3
alpha <- -(cos(theta) * r2 - a2 / 3)
beta <- -(cos(theta + 2 / 3 * pi) * r2 - a2 / 3)
gamma <- -(cos(theta + 4 / 3 * pi) * r2 - a2 / 3)
A <- (K21x - alpha) * (K31x - alpha) / (alpha - beta) / (alpha - gamma) / Vx
B <- (K21x - beta) * (K31x - beta) / (beta - alpha) / (beta - gamma) / Vx
C <- (K21x - gamma) * (K31x - gamma) / (gamma - alpha) / (gamma - beta) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cA1, 3)
sol.3cVp <- RxODE(
{
V <- theta[1]
CL <- theta[2]
Vp <- theta[3]
Q <- theta[4]
Q2 <- theta[5]
Vp2 <- theta[6]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cVp, 3)
sol.3cVt <- RxODE(
{
V <- theta[1]
CL <- theta[2]
Vt <- theta[3]
Q <- theta[4]
Q2 <- theta[5]
Vt2 <- theta[6]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cVt, 3)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cK <- sol.3cK %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cA1 <- sol.3cA1 %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cVp <- sol.3cVp %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3cVt <- sol.3cVt %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cK$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cA1$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cVp$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cVt$C2, tolerance = tol)
})
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
test_that("3 compartment solved models and ODEs same with steady state.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
sol.3cK <- RxODE(
{
V <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
ka <- theta[7]
K <- CLx / V
K12 <- Qx / V
K21 <- Qx / V2x
k13 <- Q2x / V
k31 <- Q2x / V3x
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cK, 3, 1)
sol.3cA1 <- RxODE(
{
Vx <- theta[1]
CLx <- theta[2]
V2x <- theta[3]
Qx <- theta[4]
Q2x <- theta[5]
V3x <- theta[6]
ka <- theta[7]
Kx <- CLx / Vx
K12x <- Qx / Vx
K21x <- Qx / V2x
K13x <- Q2x / Vx
K31x <- Q2x / V3x
a0 <- Kx * K21x * K31x
a1 <- Kx * K31x + K21x * K31x + K21x * K13x +
Kx * K21x + K31x * K12x
a2 <- Kx + K12x + K13x + K21x + K31x
p <- a1 - a2 * a2 / 3
qq <- 2 * a2 * a2 * a2 / 27 - a1 * a2 / 3 + a0
r1 <- sqrt(-p * p * p / 27)
r2 <- 2 * r1^(1 / 3)
theta <- acos(-qq / (2 * r1)) / 3
alpha <- -(cos(theta) * r2 - a2 / 3)
beta <- -(cos(theta + 2 / 3 * pi) * r2 - a2 / 3)
gamma <- -(cos(theta + 4 / 3 * pi) * r2 - a2 / 3)
A <- (K21x - alpha) * (K31x - alpha) / (alpha - beta) / (alpha - gamma) / Vx
B <- (K21x - beta) * (K31x - beta) / (beta - alpha) / (beta - gamma) / Vx
C <- (K21x - gamma) * (K31x - gamma) / (gamma - alpha) / (gamma - beta) / Vx
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(sol.3cA1, 3, 1)
o.3c <- ode.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3cK <- sol.3cK %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3cA1 <- sol.3cA1 %>%
solve(theta = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
test_that("3 compartment oral solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cK$C2, tolerance = tol)
expect_equal(o.3c$C2, s.3cA1$C2, tolerance = tol)
})
o.3c <- ode.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSs, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = etSs, sensType = sensType)
## Again the 4 hour strange discontinuity because ss=1
test_that("3 compartment oral solved models and ODEs same for steady state.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Infusion Models 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
etSs <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
## Solved systems can check the variables in the RxODE statement
## to figure out what type of solved system is being requested
ode.1cs <- RxODE(
{
V <- theta[1]
CL <- theta[2]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.1cs, 1)
## Instead of specifying parameters in the solved system, you can
## specify them in the linCmt variable.
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
goodP(ode.1cs2, 1)
## The solved systems can be mixed with ODE solving routines (to
## speed them up a bit...?)
o.1c <- ode.1c %>%
solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.1c <- ode.1cs2 %>%
solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.2c <- ode.1cs %>%
solve(theta = c(20, 25), events = et, sensType = sensType)
test_that("1 compartment solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
o.1c <- ode.1c %>%
solve(params = c(V = 20, CL = 10), events = etSs, sensType = sensType)
s.1c <- ode.1cs2 %>%
solve(params = c(V = 20, CL = 10), events = etSs, sensType = sensType)
s.2c <- ode.1cs %>%
solve(theta = c(20, 10), events = etSs, sensType = sensType)
test_that("1 compartment solved models and ODEs same; Steady State", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion Models 2 cmt (%s)", .txt))
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
},
linCmtSens = sens
)
goodP(sol.2c, 2)
o.2c <- ode.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2c <- sol.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
o.2c <- ode.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSs, sensType = sensType)
s.2c <- sol.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = etSs, sensType = sensType)
test_that("2 compartment steady state solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion Models 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
goodP(sol.3c, 3)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = etSs, sensType = sensType)
test_that("3 compartment steady state solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Infusion + Bolus 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.dosing(dose = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
## Solved systems can check the variables in the RxODE statement
## to figure out what type of solved system is being requested
ode.1cs <- RxODE(
{
V <- theta[1]
CL <- theta[2]
C2 <- linCmt()
},
linCmtSens = sens
)
goodP(ode.1cs, 1)
## Instead of specifying parameters in the solved system, you can
## specify them in the linCmt variable.
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
goodP(ode.1cs2, 1)
## The solved systems can be mixed with ODE solving routines (to
## speed them up a bit...?)
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 25), events = et, sensType = sensType)
s.2c <- ode.1cs %>% solve(theta = c(20, 25), events = et, sensType = sensType)
test_that("1 compartment solved models and ODEs same.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
expect_equal(o.1c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion + Bolus 2 cmt (%s)", .txt))
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
},
linCmtSens = sens
)
goodP(sol.2c, 2)
o.2c <- ode.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
s.2c <- sol.2c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Infusion + Bolus 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
},
linCmtSens = sens
)
goodP(sol.3c, 3)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Oral + Infusion + Bolus Models 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c.ka <- RxODE({
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
})
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
},
linCmtSens = sens
)
goodP(sol.1c.ka, 1, 1)
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2), events = et, sensType = sensType)
test_that("1 compartment solved models and ODEs same for mixed oral, iv and infusion.", {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
context(sprintf("Oral + Infusion + Bolus Models 2 cmt (%s)", .txt))
ode.2c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
},
linCmtSens = sens
)
goodP(sol.2c.ka, 2, 1)
o.2c <- ode.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3), events = et, sensType = sensType)
test_that("2 compartment solved models and ODEs same for mixed oral, iv and infusion.", {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
context(sprintf("Oral + Infusion + Bolus Models 3 cmt (%s)", .txt))
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
o.3c <- ode.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3), events = et, sensType = sensType)
test_that("3 compartment solved models and ODEs same for mixed oral, iv and infusion.", {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context(sprintf("Modeled bio-availability 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c.ka <- RxODE({
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
f(depot) <- fDepot
f(center) <- fCenter
})
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
f(depot) <- fDepot
f(central) <- fCenter
},
linCmtSens = sens
)
goodP(sol.1c.ka, 1, 1)
for (fd in c(0.5, 1, 2)) {
for (fc in c(0.5, 1, 2)) {
o.1c <- ode.1c.ka %>%
solve(params = c(V = 20, CL = 25, KA = 2, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>%
solve(params = c(V = 20, CL = 25, KA = 2, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled bio-availability 2 cmt (%s)", .txt))
ode.2c.ka <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
f(depot) <- fDepot
f(centr) <- fCenter
## FIXME:
## f(central) should throw an error
},
linCmtSens = sens
)
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
f(depot) <- fDepot
f(central) <- fCenter
},
linCmtSens = sens
)
goodP(sol.2c.ka, 2, 1)
for (fd in c(0.5, 1, 2)) {
for (fc in c(0.5, 1, 2)) {
o.2c <- ode.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, fDepot = fd, fCenter = fc), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled bio-availability 3 cmt (%s)", .txt))
ode.3c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
f(depot) <- fDepot
f(centr) <- fCenter
})
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
f(depot) <- fDepot
f(central) <- fCenter
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
for (fd in c(0.5, 1, 2)) {
for (fc in c(0.5, 1, 2)) {
o.3c <- ode.3c.ka %>%
solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7,
V3 = 400, KA = 0.3, fDepot = fd, fCenter = fc
), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>%
solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3,
fDepot = fd, fCenter = fc
), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled lag time 1 cmt (%s)", .txt))
et <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
ode.1c.ka <- RxODE(
{
C2 <- center / V
d / dt(depot) <- -KA * depot
d / dt(center) <- KA * depot - CL * C2
alag(depot) <- lagDepot
alag(center) <- lagCenter
},
linCmtSens = sens
)
sol.1c.ka <- RxODE(
{
C2 <- linCmt(V, CL, KA)
alag(depot) <- lagDepot
alag(central) <- lagCenter
},
linCmtSens = sens
)
goodP(sol.1c.ka, 1, 1)
for (fd in c(1, 2, 10)) {
for (fc in c(1, 2, 10)) {
o.1c <- ode.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
s.1c <- sol.1c.ka %>% solve(params = c(V = 20, CL = 25, KA = 2, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled lag time 2 cmt (%s)", .txt))
ode.2c.ka <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
alag(depot) <- lagDepot
alag(centr) <- lagCenter
})
sol.2c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, KA)
alag(depot) <- lagDepot
alag(central) <- lagCenter
},
linCmtSens = sens
)
goodP(sol.2c.ka, 2, 1)
for (fd in c(1, 2, 10)) {
for (fc in c(1, 2, 10)) {
o.2c <- ode.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
s.2c <- sol.2c.ka %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, KA = 0.3, lagDepot = fd, lagCenter = fc), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled lag time 3 cmt (%s)", .txt))
ode.3c.ka <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
alag(depot) <- lagDepot
alag(centr) <- lagCenter
},
linCmtSens = sens
)
sol.3c.ka <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3, KA)
alag(depot) <- lagDepot
alag(central) <- lagCenter
},
linCmtSens = sens
)
goodP(sol.3c.ka, 3, 1)
for (fd in c(1, 2, 10)) {
for (fc in c(1, 2, 10)) {
o.3c <- ode.3c.ka %>% solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7,
V3 = 400, KA = 0.3, lagDepot = fd, lagCenter = fc
), events = et, sensType = sensType)
s.3c <- sol.3c.ka %>% solve(params = c(
V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, KA = 0.3,
lagDepot = fd, lagCenter = fc
), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for mixed oral, iv and infusion + Fd=%f,Fc=%f", fd, fc), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
}
context(sprintf("Modeled rate 1 cmt (%s)", .txt))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
rate(center) <- rt
})
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
rate(central) <- rt
},
linCmtSens = sens
)
goodP(sol.1c, 1)
et <- eventTable() %>%
add.dosing(dose = 3, rate = -1, nbr.doses = 3, cmt = 1, dosing.interval = 12) %>%
add.sampling(seq(0, 36, length.out = 200))
for (rt in seq(0.5, 1, 1.5)) {
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25, rt = rt), events = et, sensType = sensType)
s.1c <- sol.1c %>% solve(params = c(V = 20, CL = 25, rt = rt), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for rate-modeled infusion: %s", rt), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
context(sprintf("Modeled rate 2 cmt (%s)", .txt))
ode.2c <- RxODE(
{
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
rate(centr) <- rt
},
linCmtSens = sens
)
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
rate(central) <- rt
},
linCmtSens = sens
)
goodP(sol.2c, 2)
for (rt in seq(0.5, 1, 1.5)) {
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, rt = rt), events = et, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, rt = rt), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for rate-modeled infusion: %s", rt), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
context(sprintf("Modeled rate 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
rate(centr) <- rt
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
rate(central) <- rt
},
linCmtSens = sens
)
goodP(sol.3c, 3)
for (rt in seq(0.5, 1, 1.5)) {
s.3c <- sol.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, rt = rt), events = et, sensType = sensType)
o.3c <- ode.3c %>%
solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, rt = rt), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for rate-modeled infusion: %s", rt), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
context(sprintf("Modeled duration 1 cmt (%s)", .txt))
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
dur(center) <- dr
})
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
dur(central) <- dr
},
linCmtSens = sens
)
goodP(sol.1c, 1)
et <- eventTable() %>%
add.dosing(dose = 3, rate = -2, nbr.doses = 3, cmt = 1, dosing.interval = 12) %>%
add.sampling(seq(0, 36, length.out = 200))
for (dur in seq(0.5, 1, 1.5)) {
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25, dr = dur), events = et, sensType = sensType)
s.1c <- sol.1c %>% solve(params = c(V = 20, CL = 25, dr = dur), events = et, sensType = sensType)
test_that(sprintf("1 compartment solved models and ODEs same for dur-modeled infusion: %s", dur), {
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
})
}
context(sprintf("Modeled duration 2 cmt (%s)", .txt))
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
dur(centr) <- dr
})
sol.2c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q)
dur(central) <- dr
},
linCmtSens = sens
)
goodP(sol.2c, 2)
for (dur in seq(0.5, 1, 1.5)) {
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, dr = dur), events = et, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, dr = dur), events = et, sensType = sensType)
test_that(sprintf("2 compartment solved models and ODEs same for dur-modeled infusion: %s", dur), {
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
})
}
context(sprintf("Modeled duration 3 cmt (%s)", .txt))
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
dur(centr) <- dr
})
sol.3c <- RxODE(
{
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
dur(central) <- dr
},
linCmtSens = sens
)
goodP(sol.3c, 3)
for (dur in seq(0.5, 1, 1.5)) {
o.3c <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, dr = dur), events = et, sensType = sensType)
s.3c <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400, dr = dur), events = et, sensType = sensType)
test_that(sprintf("3 compartment solved models and ODEs same for dur-modeled infusion: %s", dur), {
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
}
test_that("central should throw error", {
expect_error(RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
alag(depot) <- lagDepot
alag(centr) <- lagCenter
alag(central) <- matt
}))
})
test_that("depot should throw error", {
expect_error(RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(dep) <- -KA * dep
d / dt(centr) <- KA * dep - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
alag(dep) <- lagDepot
alag(centr) <- lagCenter
alag(depot) <- matt
}))
})
ode.1cs2 <- RxODE(
{
C2 <- linCmt(CL, V)
mtime(t1) <- mt1
mtime(t2) <- mt2
},
linCmtSens = sens
)
goodP(ode.1cs2, 1)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(0:48)
s.1c <- ode.1cs2 %>% solve(
params = c(V = 20, CL = 25, mt1 = 0.5, mt2 = 1.75),
events = et, sensType = sensType
)
test_that("mtime with solved systems work", {
expect_equal(s.1c$time[1:4], c(0, 0.5, 1, 1.75))
})
context(sprintf("evid=3 (%s)", .txt))
test_that(sprintf("evid==3 (%s)", .txt), {
ode.1c <- RxODE(
{
C2 <- center / V
d / dt(center) <- -CL * C2
},
linCmtSens = sens
)
sol.1c <- RxODE(
{
C2 <- linCmt(CL, V)
},
linCmtSens = sens
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
et(time = 25, evid = 3) %>%
add.sampling(seq(0, 48, length.out = 200))
o1 <- rxSolve(ode.1c, params = c(V = 20, CL = 25), events = et)
s1 <- rxSolve(sol.1c, params = c(V = 20, CL = 25), events = et, sensType = sensType)
expect_equal(o1$C2, s1$C2, tol = tol)
})
}
if (length(types) > 1) {
test_that(
"double linCmt has error",
expect_error(RxODE({
C2 <- linCmt(CL, V)
C2 <- linCmt(CL, V)
}))
)
context(sprintf("Steady State Infusions (%s)", .txt))
test_that("Steady state IV infusion", {
ev <- et(amt = 0, ss = 1, rate = 10000 / 8)
ode.1c <- RxODE({
C2 <- center / V
d / dt(center) <- -CL * C2
})
ode.1cs2 <- RxODE({
C2 <- linCmt(CL, V)
})
goodP(ode.1cs2, 1)
o.1c <- ode.1c %>% solve(params = c(V = 20, CL = 25), events = ev, sensType = sensType)
s.1c <- ode.1cs2 %>% solve(params = c(V = 20, CL = 25), events = ev, sensType = sensType)
expect_equal(o.1c$C2, s.1c$C2, tolerance = tol)
ode.2c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
})
sol.2c <- RxODE({
C2 <- linCmt(V, CL, V2, Q1)
})
goodP(sol.2c, 2)
o.2c <- ode.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10), events = ev, sensType = sensType)
s.2c <- sol.2c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q1 = 10), events = ev, sensType = sensType)
expect_equal(o.2c$C2, s.2c$C2, tolerance = tol)
ode.3c <- RxODE({
C2 <- centr / V
C3 <- peri / V2
C4 <- peri2 / V3
d / dt(centr) <- -CL * C2 - Q * C2 + Q * C3 - Q2 * C2 + Q2 * C4
d / dt(peri) <- Q * C2 - Q * C3
d / dt(peri2) <- Q2 * C2 - Q2 * C4
})
sol.3c <- RxODE({
C2 <- linCmt(V, CL, V2, Q, Q2, V3)
})
goodP(sol.3c, 3)
o.3c <- ode.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = ev, sensType = sensType)
s.3c <- sol.3c %>% solve(params = c(V = 40, CL = 18, V2 = 297, Q = 10, Q2 = 7, V3 = 400), events = ev, sensType = sensType)
expect_equal(o.3c$C2, s.3c$C2, tolerance = tol)
})
context("Issue #258")
test_that("Issue #258", {
m258 <- RxODE(
{
ka <- 1
cl <- 3.5
vc <- 40
Conc <- linCmt()
alag(depot) <- 1
},
linCmtSens = sens
)
m258o <- RxODE({
ka <- 1
cl <- 3.5
vc <- 40
d / dt(depot) <- -ka * depot
d / dt(central) <- ka * depot - cl / vc * central
Conc <- central / vc
alag(depot) <- 1
})
s1 <- m258 %>%
et(dose = 100, time = 0, addl = 6, ii = 24) %>%
et(0, 250, by = 0.1) %>%
rxSolve(sensType = sensType)
s2 <- m258o %>%
et(dose = 100, time = 0, addl = 6, ii = 24) %>%
et(0, 250, by = 0.1) %>%
rxSolve()
expect_equal(s1$Conc, s2$Conc, tol = tol)
})
tol <- 1e-5 ## Current difference for all equations
type <- 1
## forward/central differences don't work here...
for (type in c(1, 4)) {
sens <- switch(type,
"autodiff",
"forward",
"central",
"advan"
)
context(sprintf("1 cmt sensitivities (%s)", sens))
test_that("1 compartment sensitivities; IV bolus, Cl, V", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
prop.err <- THETA[3]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl)
})
pk1s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- center / Vc
d / dt(center) <- -Cl * Central
})
pk1o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
parms <- c(
"THETA[1]" = 20, "THETA[2]" = 25, "ETA[1]" = 1, "ETA[2]" = 1,
"THETA[3]" = 0.2
)
s1 <- rxSolve(pk1s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
## ETA1, ETA2 is zero for central
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
## ETA1, ETA2 is zero for central
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___)
expect_equal(s1$rx_r_, o1$rx_r_)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___)
})
## Now oral
test_that("1 compartment sensitivities; Oral Cl, V, Ka", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lKa <- THETA[3]
prop.err <- THETA[4]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Ka <- ETA[3]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Ka <- exp(lKa + eta.Ka)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Ka)
})
pk1s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- center / Vc
d / dt(depot) <- -Ka * depot
d / dt(center) <- Ka * depot - Cl * Central
})
pk1o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
parms <- c(
"THETA[1]" = log(20), "THETA[2]" = log(25), "THETA[3]" = log(2),
"ETA[1]" = 0, "ETA[2]" = 0, "ETA[3]" = 0,
"THETA[4]" = 0.2
)
s1 <- rxSolve(pk1s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etSs, sensType = sens)
## library(patchwork)
## plot(s1, rx_pred_) / plot(o1, rx_pred_)
## plot(s1, rx__sens_rx_pred__BY_ETA_1___) / plot(o1, rx__sens_rx_pred__BY_ETA_1___)
## plot(s1, rx__sens_rx_pred__BY_ETA_2___) / plot(o1, rx__sens_rx_pred__BY_ETA_2___)
## plot(s1, rx__sens_rx_pred__BY_ETA_3___) / plot(o1, rx__sens_rx_pred__BY_ETA_3___)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 2) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
etMix <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk1s$inner, parms, etMix, sensType = sens)
o1 <- rxSolve(pk1o$inner, parms, etMix, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
})
context(sprintf("2 cmt sensitivities (%s)", sens))
test_that("2 compartment sensitivities; IV bolus Cl, Vc, Q, Vp", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
prop.err <- THETA[5]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
}
pk2 <- function() {
lK10 <- THETA[1]
lVc <- THETA[2]
lK12 <- THETA[3]
lK21 <- THETA[4]
prop.err <- THETA[5]
eta.Vc <- ETA[1]
eta.K10 <- ETA[2]
eta.K12 <- ETA[3]
eta.K21 <- ETA[4]
Vc <- exp(lVc + eta.Vc)
k10 <- exp(lK10 + eta.K10)
k12 <- exp(lK12 + eta.K12)
k21 <- exp(lK21 + eta.K21)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q)
})
modK <- RxODE({
Central <- linCmt(Vc, k10, k12, k21)
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
pk2sK <- rxSymPySetupPred(modK, predfn = pred, pkpars = pk2, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
d / dt(centr) <- -Cl * Central - Q * Central + Q * C3
d / dt(peri) <- Q * Central - Q * C3
})
modK2 <- RxODE({
Central <- centr / Vc
d / dt(centr) <- -k10 * centr - k12 * centr + k21 * peri
d / dt(peri) <- k12 * centr - k21 * peri
})
pk2o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
pk2oK <- rxSymPySetupPred(modK2, predfn = pred, pkpars = pk2, err = err)
parms2 <- c(
"THETA[1]" = log(18 / 40), "THETA[2]" = log(40),
"THETA[3]" = log(10 / 40), "THETA[4]" = log(10 / 297),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"THETA[5]" = 0.2
)
parms <- c(
"THETA[1]" = log(18), "THETA[2]" = log(40),
"THETA[3]" = log(10), "THETA[4]" = log(297),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"THETA[5]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, et, sensType = sens)
s1 <- rxSolve(pk2sK$inner, parms2, et, sensType = sens)
o1 <- rxSolve(pk2oK$inner, parms2, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tol = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tol = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
})
test_that("2 compartment sensitivities; Oral Cl, Vc, Q, Vp, Ka", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lKa <- THETA[5]
prop.err <- THETA[6]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Ka <- ETA[5]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Ka <- exp(lKa + eta.Ka)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Ka)
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
d / dt(depot) <- -Ka * depot
d / dt(centr) <- Ka * depot - Cl * Central - Q * Central + Q * C3
d / dt(peri) <- Q * Central - Q * C3
})
pk2o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
parms <- c(
"THETA[1]" = log(18), ## Cl
"THETA[2]" = log(40), ## Vc
"THETA[3]" = log(10), ## Q
"THETA[4]" = log(297), ## Vp
"THETA[5]" = log(0.3), ## Ka
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0,
"THETA[6]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 2) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 1) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 1) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 1) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
etMix <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etMix, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etMix, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
})
context(sprintf("3 cmt sensitivities (%s)", sens))
test_that("3 compartment sensitivities; IV bolus Cl, Vc, Q, Vp", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lQ2 <- THETA[5]
lVp2 <- THETA[6]
prop.err <- THETA[7]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Q2 <- ETA[5]
eta.Vp2 <- ETA[6]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Q2 <- exp(lQ2 + eta.Q2)
Vp2 <- exp(lVp2 + eta.Vp2)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Vp2, Q2)
})
pk3s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
C4 <- peri2 / Vp2
d / dt(centr) <- -Cl * Central - Q * Central + Q * C3 + Q2 * C4 - Q2 * Central
d / dt(peri) <- Q * Central - Q * C3
d / dt(peri2) <- Q2 * Central - Q2 * C4
})
pk3o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
parms <- c(
"THETA[1]" = log(18), "THETA[2]" = log(40),
"THETA[3]" = log(10), "THETA[4]" = log(297),
"THETA[5]" = log(7), "THETA[6]" = log(400),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0, "ETA[6]" = 0,
"THETA[7]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 1) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 1) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 1) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk3s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk3o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
})
test_that("3 compartment sensitivities; Oral Cl, Vc, Q, Vp, Ka", {
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lQ2 <- THETA[5]
lVp2 <- THETA[6]
lKa <- THETA[7]
prop.err <- THETA[8]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Q2 <- ETA[5]
eta.Vp2 <- ETA[6]
eta.Ka <- ETA[7]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Q2 <- exp(lQ2 + eta.Q2)
Vp2 <- exp(lVp2 + eta.Vp2)
Ka <- exp(lKa + eta.Ka)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Vp2, Q2, Ka)
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
mod2 <- RxODE({
Central <- centr / Vc
C3 <- peri / Vp
C4 <- peri2 / Vp2
d / dt(depot) <- -Ka * depot
d / dt(centr) <- Ka * depot - Cl * Central - Q * Central + Q * C3 - Q2 * Central + Q2 * C4
d / dt(peri) <- Q * Central - Q * C3
d / dt(peri2) <- Q2 * Central - Q2 * C4
})
pk2o <- rxSymPySetupPred(mod2, predfn = pred, pkpars = pk, err = err)
parms <- c(
"THETA[1]" = log(18), "THETA[2]" = log(40),
"THETA[3]" = log(10), "THETA[4]" = log(297),
"THETA[5]" = log(7), "THETA[6]" = log(400),
"THETA[7]" = log(0.3),
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0, "ETA[6]" = 0, "ETA[7]" = 0,
"THETA[8]" = 0.2
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, et, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, et, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etSs <- et() %>%
et(amt = 3) %>%
et(time = 4, amt = 3, ss = 1, ii = 24) %>%
et(amt = 3, ss = 2, ii = 24, time = 8) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 2) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInf <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 6, dosing.interval = 8, cmt = 1) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInf, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInf, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 2) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 2) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 2) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etInfSs <- et() %>%
et(amt = 3, rate = 1.5, cmt = 1) %>%
et(time = 4, amt = 3, rate = 1.5, ss = 1, ii = 24, cmt = 1) %>%
et(time = 8, amt = 3, rate = 1.5, ss = 2, ii = 24, cmt = 1) %>%
et(seq(0, 24, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etInfSs, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etInfSs, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
etMix <- eventTable() %>%
add.dosing(dose = 3, rate = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 2) %>%
add.dosing(dose = 1.5, nbr.doses = 3, dosing.interval = 16, cmt = 1, start.time = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
s1 <- rxSolve(pk2s$inner, parms, etMix, sensType = sens)
o1 <- rxSolve(pk2o$inner, parms, etMix, sensType = sens)
expect_equal(s1$rx_pred_, o1$rx_pred_, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_1___, o1$rx__sens_rx_pred__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_2___, o1$rx__sens_rx_pred__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_3___, o1$rx__sens_rx_pred__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_4___, o1$rx__sens_rx_pred__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_5___, o1$rx__sens_rx_pred__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_6___, o1$rx__sens_rx_pred__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_pred__BY_ETA_7___, o1$rx__sens_rx_pred__BY_ETA_7___, tolerance = tol)
expect_equal(s1$rx_r_, o1$rx_r_, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_1___, o1$rx__sens_rx_r__BY_ETA_1___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_2___, o1$rx__sens_rx_r__BY_ETA_2___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_3___, o1$rx__sens_rx_r__BY_ETA_3___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_4___, o1$rx__sens_rx_r__BY_ETA_4___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_5___, o1$rx__sens_rx_r__BY_ETA_5___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_6___, o1$rx__sens_rx_r__BY_ETA_6___, tolerance = tol)
expect_equal(s1$rx__sens_rx_r__BY_ETA_7___, o1$rx__sens_rx_r__BY_ETA_7___, tolerance = tol)
})
}
context("Make sure central differences are working")
test_that("central differences", {
## One compartment model without oral absorption
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
prop.err <- THETA[3]
lF <- THETA[4]
lLag <- THETA[5]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.f <- ETA[3]
eta.lag <- ETA[4]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
fc <- exp(lF + eta.f)
lagc <- exp(lLag + eta.lag)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl)
f(central) <- fc
alag(central) <- lagc
})
parms <- c(
"THETA[1]" = 20, "THETA[2]" = 25, "ETA[1]" = 1, "ETA[2]" = 1,
"THETA[3]" = 0.2, "THETA[4]" = log(1), "THETA[5]" = log(0.5),
"ETA[3]" = 1, "ETA[4]" = 1
)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
s1 <- rxSolve(pk2s$inner, parms, et, sensType = "advan")
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_3___)))
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_4___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_3___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_4___)))
## One compartment model with oral absorption
pred <- function() {
return(Central)
}
pk <- function() {
lCl <- THETA[1]
lVc <- THETA[2]
lQ <- THETA[3]
lVp <- THETA[4]
lKa <- THETA[5]
prop.err <- THETA[6]
lF <- THETA[7]
lLag <- THETA[8]
eta.Vc <- ETA[1]
eta.Cl <- ETA[2]
eta.Vp <- ETA[3]
eta.Q <- ETA[4]
eta.Ka <- ETA[5]
eta.F <- ETA[6]
eta.lag <- ETA[7]
Vc <- exp(lVc + eta.Vc)
Cl <- exp(lCl + eta.Cl)
Vp <- exp(lVp + eta.Vp)
Q <- exp(lQ + eta.Q)
Ka <- exp(lKa + eta.Ka)
F <- exp(lF + eta.F)
lag <- exp(lLag + eta.lag)
}
err <- function() {
return(prop(prop.err))
}
mod <- RxODE({
Central <- linCmt(Vc, Cl, Vp, Q, Ka)
f(depot) <- F
alag(depot) <- lag
})
pk2s <- rxSymPySetupPred(mod, predfn = pred, pkpars = pk, err = err)
et <- eventTable() %>%
add.dosing(dose = 3, nbr.doses = 6, dosing.interval = 8) %>%
add.sampling(seq(0, 48, length.out = 200))
parms <- c(
"THETA[1]" = log(18), ## Cl
"THETA[2]" = log(40), ## Vc
"THETA[3]" = log(10), ## Q
"THETA[4]" = log(297), ## Vp
"THETA[5]" = log(0.3), ## Ka
"ETA[1]" = 0, "ETA[2]" = 0,
"ETA[3]" = 0, "ETA[4]" = 0,
"ETA[5]" = 0,
"THETA[6]" = 0.2, "THETA[7]" = log(1.5), "THETA[8]" = log(0.5),
"ETA[6]" = 0, "ETA[7]" = 0
)
s1 <- rxSolve(pk2s$inner, parms, et, sensType = "advan")
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_6___)))
expect_false(any(is.na(s1$rx__sens_rx_pred__BY_ETA_7___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_6___)))
expect_false(any(is.na(s1$rx__sens_rx_r__BY_ETA_7___)))
})
}
},
silent = TRUE,
test = "lincmt"
)
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