tests/testthat/test-occ.R
In nlmixr2/rxode2: Facilities for Simulating from ODE-Based Models
rxTest({
test_that("occasions", {
# Nesting tests
mod <- rxode2({
eff(0) <- 1
C2 <- centr / V2 * (1 + prop.err)
C3 <- peri / V3
CL <- TCl * exp(eta.Cl + iov.Cl)
KA <- TKA * exp(eta.Ka + iov.Ka)
d/dt(depot) <- -KA * depot
d/dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d/dt(peri) <- Q * C2 - Q * C3
d/dt(eff) <- Kin - Kout * (1 - C2 / (EC50 + C2)) * eff
})
mod.eta <- rxode2({
eff(0) <- 1
C2 <- centr / V2 * (1 + prop.err)
C3 <- peri / V3
CL <- TCl * exp(ETA[1] + iov.Cl)
KA <- TKA * exp(ETA[2] + iov.Ka)
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
d / dt(eff) <- Kin - Kout * (1 - C2 / (EC50 + C2)) * eff
})
et(amountUnits = "mg", timeUnits = "hours") %>%
et(amt = 10000, addl = 9, ii = 12, cmt = "depot") %>%
et(time = 120, amt = 2000, addl = 4, ii = 14, cmt = "depot") %>%
et(seq(0, 240, by = 4)) %>%
# Assumes sampling when there is no dosing information
et(seq(0, 240, by = 4) + 0.1) %>%
## adds 0.1 for separate eye
et(id = 1:20) %>%
## Add an occasion per dose
dplyr::mutate(occ = cumsum(!is.na(amt))) %>%
dplyr::mutate(occ = ifelse(occ == 0, 1, occ)) %>%
dplyr::mutate(occ = 2 - occ %% 2) %>%
dplyr::mutate(eye = ifelse(round(time) == time, 1, 2)) %>%
dplyr::mutate(inv = ifelse(id < 10, 1, 2)) ->
ev
omega <- lotri(
lotri(
eta.Cl ~ 0.1,
eta.Ka ~ 0.1
) | id(nu = 100),
lotri(
eye.Cl ~ 0.05,
eye.Ka ~ 0.05
) | eye(nu = 50, same = 2),
lotri(
iov.Cl ~ 0.01,
iov.Ka ~ 0.01
) | occ(nu = 200, same = 2),
lotri(
inv.Cl ~ 0.02,
inv.Ka ~ 0.02
) | inv(nu = 10, same = 2)
)
attr(omega, "format") <- "THETA[%d]"
attr(omega, "start") <- 2L
## cvPost(nu=1000, omega, 2)
omega <- lotri(
lotri(
eta.Cl ~ 0.1,
eta.Ka ~ 0.1
) | id(nu = 100),
lotri(
eye.Cl ~ 0.05,
eye.Ka ~ 0.05
) | eye(nu = 50),
lotri(
iov.Cl ~ 0.01,
iov.Ka ~ 0.01
) | occ(nu = 200),
lotri(
inv.Cl ~ 0.02,
inv.Ka ~ 0.02
) | inv(nu = 10)
)
.ni <- nestingInfo_(omega, ev)
expect_equal(.ni$below, c(eye = 2L, occ = 2L))
expect_equal(.ni$above, c(inv = 2L))
expect_s3_class(.ni$data$eye, "factor")
expect_equal(attr(.ni$data$eye, "nu"), 40L)
expect_s3_class(.ni$data$inv, "factor")
expect_equal(attr(.ni$data$inv, "nu"), NULL)
expect_s3_class(.ni$data$occ, "factor")
expect_equal(attr(.ni$data$occ, "nu"), 40L)
expect_equal(.ni$extraTheta, 4)
expect_equal(.ni$extraEta, 8)
.en <- rxExpandNesting(mod, .ni, compile = TRUE)
.ett <- etTrans(.ni$data, .en$mod)
theta <- c(
KA = 2.94E-01, CL = 1.86E+01, V2 = 4.02E+01, # central
Q = 1.05E+01, V3 = 2.97E+02, # peripheral
Kin = 1, Kout = 1, EC50 = 200
) # effects
thetaMat <- lotri(
KA ~ 0.01,
CL ~ 0.01,
V2 ~ 0.01,
Q ~ 0.01,
V3 ~ 0.01,
Kin ~ 0.01,
Kout ~ 0.01,
EC50 ~ 0.01
)
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
nSub = 40, nStud = 3))
expect_equal(length(.ep$KA), 120L)
expect_equal(length(unique(.ep$KA)), 3L)
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
nStud = 3
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 60L)
expect_equal(length(unique(.ep$KA)), 3L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
nStud = 3, nSub = 20
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat,
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_false(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_false(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_false(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
omega = lotri(eta.Cl ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(dfObs = 10, nStud = 3, nSub = 4)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 12L)
expect_false(any(names(.ep) == "eta.Cl"))
expect_error(.expandPars(mod, NULL, ev,
control = rxControl(thetaMat = thetaMat, omega = omega, nStud = 3)
))
.ep <- .expandPars(mod, NULL, ev,
control = rxControl(omega = omega, nStud = 3)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$eta.Ka), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, NULL, ev,
control = rxControl(
omega = omega,
nStud = 3, dfObs = 100, nSub = 20, dfSub = 10
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$eta.Ka), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = lotri(KA ~ 1, CL ~ 1),
omega = omega,
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
## Test edge case -- no between or above occasion variability
.ni <- nestingInfo_(
lotri(lotri(eta.Cl ~ 0.1, eta.Ka ~ 0.1) | id(nu = 100)),
ev
)
expect_equal(.ni$above, structure(integer(0), .Names = character(0)))
expect_equal(.ni$below, structure(integer(0), .Names = character(0)))
expect_equal(.ni$idName, "id")
expect_s3_class(.ni$omega, "lotri")
expect_equal(names(.ni$omega), "id")
.en <- rxExpandNesting(mod, .ni)
})
test_that("nesting test from https://github.com/nlmixr2/rxode2random/issues/25", {
mod <- rxode2({
TABS = TV_TABS * exp(eta.TABS + iov.TABS)
TR_Fbio = TV_TR_Fbio + eta.TR_Fbio + iov.TR_Fbio
CL = TV_CL * exp(eta.CL)
V1 = TV_V1 * exp(eta.V1)
V2 = TV_V2 * exp(eta.V2)
CLD = TV_CLD * exp(eta.CLD)
KA = log(2) / (TABS/60)
FBIO = 1 / (exp(-TR_Fbio) + 1)
DC1 = AMT1/V1
DC2 = AMT2/V2
d/dt(AMTa) = -KA * AMTa
d/dt(AMT1) = FBIO * KA * AMTa - CLD * DC1 + CLD * DC2 - CL * DC1
d/dt(AMT2) = + CLD * DC1 - CLD * DC2
d/dt(AUC) = DC1
})
n <- 10
theta <- c("TV_TABS" = 45,
"TV_TR_Fbio" = logit(x = 0.85),
"TV_CL" = 10,
"TV_V1" = 10,
"TV_V2" = 65,
"TV_CLD" = 25)
omega <- lotri::lotri(
lotri::lotri(eta.TABS~0.25,
eta.TR_Fbio~0.20,
eta.CL~0.30,
eta.V1~0.30,
eta.V2~0.45,
eta.CLD~0.15) | id(nu=n),
lotri::lotri(iov.TABS~0.15,
iov.TR_Fbio~0.15) | occ(nu=n*2))
dosing <- et(amt=1000,
addl=6,
ii=24,
evid=1,
cmt="AMTa",
time=0) %>%
et(amt=1000,
addl=6,
ii=24,
evid=4,
cmt="AMTa",
time = 336) %>%
et(seq(0,168,0.5)) %>%
et(seq(336,672,0.5)) %>%
et(id=seq(1,n))
dosing <- dplyr::mutate(dosing, occ = 1) %>%
dplyr::mutate(occ = ifelse(time>=336,2,occ))
expect_error(rxSolve(object = mod,
theta,
omega=omega,
ev=dosing,
nDisplayProgress=100L), NA)
})
})
nlmixr2/rxode2 documentation built on Jan. 11, 2025, 8:48 a.m.
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rxTest({
test_that("occasions", {
# Nesting tests
mod <- rxode2({
eff(0) <- 1
C2 <- centr / V2 * (1 + prop.err)
C3 <- peri / V3
CL <- TCl * exp(eta.Cl + iov.Cl)
KA <- TKA * exp(eta.Ka + iov.Ka)
d/dt(depot) <- -KA * depot
d/dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d/dt(peri) <- Q * C2 - Q * C3
d/dt(eff) <- Kin - Kout * (1 - C2 / (EC50 + C2)) * eff
})
mod.eta <- rxode2({
eff(0) <- 1
C2 <- centr / V2 * (1 + prop.err)
C3 <- peri / V3
CL <- TCl * exp(ETA[1] + iov.Cl)
KA <- TKA * exp(ETA[2] + iov.Ka)
d / dt(depot) <- -KA * depot
d / dt(centr) <- KA * depot - CL * C2 - Q * C2 + Q * C3
d / dt(peri) <- Q * C2 - Q * C3
d / dt(eff) <- Kin - Kout * (1 - C2 / (EC50 + C2)) * eff
})
et(amountUnits = "mg", timeUnits = "hours") %>%
et(amt = 10000, addl = 9, ii = 12, cmt = "depot") %>%
et(time = 120, amt = 2000, addl = 4, ii = 14, cmt = "depot") %>%
et(seq(0, 240, by = 4)) %>%
# Assumes sampling when there is no dosing information
et(seq(0, 240, by = 4) + 0.1) %>%
## adds 0.1 for separate eye
et(id = 1:20) %>%
## Add an occasion per dose
dplyr::mutate(occ = cumsum(!is.na(amt))) %>%
dplyr::mutate(occ = ifelse(occ == 0, 1, occ)) %>%
dplyr::mutate(occ = 2 - occ %% 2) %>%
dplyr::mutate(eye = ifelse(round(time) == time, 1, 2)) %>%
dplyr::mutate(inv = ifelse(id < 10, 1, 2)) ->
ev
omega <- lotri(
lotri(
eta.Cl ~ 0.1,
eta.Ka ~ 0.1
) | id(nu = 100),
lotri(
eye.Cl ~ 0.05,
eye.Ka ~ 0.05
) | eye(nu = 50, same = 2),
lotri(
iov.Cl ~ 0.01,
iov.Ka ~ 0.01
) | occ(nu = 200, same = 2),
lotri(
inv.Cl ~ 0.02,
inv.Ka ~ 0.02
) | inv(nu = 10, same = 2)
)
attr(omega, "format") <- "THETA[%d]"
attr(omega, "start") <- 2L
## cvPost(nu=1000, omega, 2)
omega <- lotri(
lotri(
eta.Cl ~ 0.1,
eta.Ka ~ 0.1
) | id(nu = 100),
lotri(
eye.Cl ~ 0.05,
eye.Ka ~ 0.05
) | eye(nu = 50),
lotri(
iov.Cl ~ 0.01,
iov.Ka ~ 0.01
) | occ(nu = 200),
lotri(
inv.Cl ~ 0.02,
inv.Ka ~ 0.02
) | inv(nu = 10)
)
.ni <- nestingInfo_(omega, ev)
expect_equal(.ni$below, c(eye = 2L, occ = 2L))
expect_equal(.ni$above, c(inv = 2L))
expect_s3_class(.ni$data$eye, "factor")
expect_equal(attr(.ni$data$eye, "nu"), 40L)
expect_s3_class(.ni$data$inv, "factor")
expect_equal(attr(.ni$data$inv, "nu"), NULL)
expect_s3_class(.ni$data$occ, "factor")
expect_equal(attr(.ni$data$occ, "nu"), 40L)
expect_equal(.ni$extraTheta, 4)
expect_equal(.ni$extraEta, 8)
.en <- rxExpandNesting(mod, .ni, compile = TRUE)
.ett <- etTrans(.ni$data, .en$mod)
theta <- c(
KA = 2.94E-01, CL = 1.86E+01, V2 = 4.02E+01, # central
Q = 1.05E+01, V3 = 2.97E+02, # peripheral
Kin = 1, Kout = 1, EC50 = 200
) # effects
thetaMat <- lotri(
KA ~ 0.01,
CL ~ 0.01,
V2 ~ 0.01,
Q ~ 0.01,
V3 ~ 0.01,
Kin ~ 0.01,
Kout ~ 0.01,
EC50 ~ 0.01
)
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
nSub = 40, nStud = 3))
expect_equal(length(.ep$KA), 120L)
expect_equal(length(unique(.ep$KA)), 3L)
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
nStud = 3
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 60L)
expect_equal(length(unique(.ep$KA)), 3L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
nStud = 3, nSub = 20
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat, omega = omega,
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = thetaMat,
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_false(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_false(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(length(.rxModels[[".sigmaL"]]), 3L)
expect_equal(length(.ep$KA), 60L)
expect_false(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
omega = lotri(eta.Cl ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(dfObs = 10, nStud = 3, nSub = 4)
)
expect_equal(.rxModels[[".thetaL"]], NULL)
expect_equal(.rxModels[[".omegaL"]], NULL)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$KA), 12L)
expect_false(any(names(.ep) == "eta.Cl"))
expect_error(.expandPars(mod, NULL, ev,
control = rxControl(thetaMat = thetaMat, omega = omega, nStud = 3)
))
.ep <- .expandPars(mod, NULL, ev,
control = rxControl(omega = omega, nStud = 3)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$eta.Ka), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, NULL, ev,
control = rxControl(
omega = omega,
nStud = 3, dfObs = 100, nSub = 20, dfSub = 10
)
)
expect_equal(length(.rxModels[[".thetaL"]]), 3L)
expect_equal(length(.rxModels[[".omegaL"]]), 3L)
expect_equal(.rxModels[[".sigmaL"]], NULL)
expect_equal(length(.ep$eta.Ka), 60L)
expect_true(any(names(.ep) == "eta.Cl"))
.ep <- .expandPars(mod, theta, ev,
control = rxControl(
thetaMat = lotri(KA ~ 1, CL ~ 1),
omega = omega,
sigma = lotri(prop.err ~ 0.1), dfObs = 10,
nStud = 3, nSub = 20
)
)
## Test edge case -- no between or above occasion variability
.ni <- nestingInfo_(
lotri(lotri(eta.Cl ~ 0.1, eta.Ka ~ 0.1) | id(nu = 100)),
ev
)
expect_equal(.ni$above, structure(integer(0), .Names = character(0)))
expect_equal(.ni$below, structure(integer(0), .Names = character(0)))
expect_equal(.ni$idName, "id")
expect_s3_class(.ni$omega, "lotri")
expect_equal(names(.ni$omega), "id")
.en <- rxExpandNesting(mod, .ni)
})
test_that("nesting test from https://github.com/nlmixr2/rxode2random/issues/25", {
mod <- rxode2({
TABS = TV_TABS * exp(eta.TABS + iov.TABS)
TR_Fbio = TV_TR_Fbio + eta.TR_Fbio + iov.TR_Fbio
CL = TV_CL * exp(eta.CL)
V1 = TV_V1 * exp(eta.V1)
V2 = TV_V2 * exp(eta.V2)
CLD = TV_CLD * exp(eta.CLD)
KA = log(2) / (TABS/60)
FBIO = 1 / (exp(-TR_Fbio) + 1)
DC1 = AMT1/V1
DC2 = AMT2/V2
d/dt(AMTa) = -KA * AMTa
d/dt(AMT1) = FBIO * KA * AMTa - CLD * DC1 + CLD * DC2 - CL * DC1
d/dt(AMT2) = + CLD * DC1 - CLD * DC2
d/dt(AUC) = DC1
})
n <- 10
theta <- c("TV_TABS" = 45,
"TV_TR_Fbio" = logit(x = 0.85),
"TV_CL" = 10,
"TV_V1" = 10,
"TV_V2" = 65,
"TV_CLD" = 25)
omega <- lotri::lotri(
lotri::lotri(eta.TABS~0.25,
eta.TR_Fbio~0.20,
eta.CL~0.30,
eta.V1~0.30,
eta.V2~0.45,
eta.CLD~0.15) | id(nu=n),
lotri::lotri(iov.TABS~0.15,
iov.TR_Fbio~0.15) | occ(nu=n*2))
dosing <- et(amt=1000,
addl=6,
ii=24,
evid=1,
cmt="AMTa",
time=0) %>%
et(amt=1000,
addl=6,
ii=24,
evid=4,
cmt="AMTa",
time = 336) %>%
et(seq(0,168,0.5)) %>%
et(seq(336,672,0.5)) %>%
et(id=seq(1,n))
dosing <- dplyr::mutate(dosing, occ = 1) %>%
dplyr::mutate(occ = ifelse(time>=336,2,occ))
expect_error(rxSolve(object = mod,
theta,
omega=omega,
ev=dosing,
nDisplayProgress=100L), NA)
})
})
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