Nothing
nmTest({
test_that("focei complex event info", {
pheno <- function() {
ini({
tcl <- log(0.008) # typical value of clearance
tv <- log(0.6) # typical value of volume
max_dose <- 5
## var(eta.cl)
eta.cl + eta.v ~ c(1,
0.01, 1) ## cov(eta.cl, eta.v), var(eta.v)
# interindividual variability on clearance and volume
add.err <- 0.1 # residual variability
})
model({
cl <- exp(tcl + eta.cl)
v <- exp(tv + eta.v)
fest <- max_dose/DOSE
if (fest > 1) fest <- 1 # error is here
d/dt(A1) = - ke * A1
f(A1) <- fest
cp = A1 / v
cp ~ add(add.err)
})
}
f <- pheno()
expect_error(f$foceiModel, NA)
})
test_that("Inner test", {
ev <- eventTable() %>%
add.sampling(c(
95.99, 119.99, 143.99, 144.25, 144.5, 144.75,
145, 145.5, 146, 146.5, 147, 148, 150, 152, 156, 160, 164, 167.99,
191.99, 215.99, 216.25, 216.5, 216.75, 217, 217.5, 218, 218.5, 219,
220, 222, 224, 228, 232, 236, 240, 252, 264, 276, 288
)) %>%
add.dosing(dose = 60000, start.time = 72, nbr.doses = 7, dosing.interval = 24)
dv <- c(
263.6, 164.7, 287.3, 1248.7, 1211.5, 1017.7, 1690.1, 1029.8,
890.7, 598.4, 1009.3, 1159.8, 742.2, 724.6, 728.2, 509.7, 243.1,
259.9, 242.2, 281.4, 1500.1, 1281.4, 1200.2, 1378.8, 1373.2,
582.9, 960.2, 720.3, 852.6, 950.3, 654.7, 402.5, 456, 346.5,
268.2, 134.2, 42.6, 25.9, 14.6
)
m1 <- function() {
ini({
tcl <- 1.6
tv <- 4.5
eta.cl ~ 0.1
eta.v ~ 0.1
prop.sd <- sqrt(0.1)
})
model({
CL <- exp(tcl + eta.cl)
V <- exp(tv + eta.v)
C2 <- centr / V
d/dt(centr) <- -CL * C2
C2 ~ prop(prop.sd)
})
}
w7 <- data.frame(ev$get.EventTable())
w7$DV <- NA
w7$DV[which(is.na(w7$amt))] <- dv
w7$ID <- 1
ETA <- matrix(c(-0.147736086922763, -0.294637022436797), ncol = 2)
fitPi <- suppressMessages(nlmixr(
m1, w7,
est="focei",
foceiControl(
etaMat = ETA,
maxOuterIterations = 0, maxInnerIterations = 0,
covMethod = ""
)
))
expect_equal(418.935, round(fitPi$objective, 3))
})
test_that("boundary value is not triggered by bounds on both sides of zero (#318)", {
one.compartment <- function() {
ini({
tka <- c(-6, -4, 2)
tcl <- 1
tv <- 3.45
eta.ka ~ 0.6
eta.cl ~ 0.3
eta.v ~ 0.1
add.sd <- 0.7
})
model({
ka <- exp(tka + eta.ka)*100
cl <- exp(tcl + eta.cl)
v <- exp(tv + eta.v)
d/dt(depot) = -ka * depot
d/dt(center) = ka * depot - cl / v * center
cp = center / v
cp ~ add(add.sd)
})
}
fit <- nlmixr2(one.compartment, theo_sd, est="focei", control = list(print=0))
# SE being present indicates that the covariance matrix was estimated
expect_true("SE" %in% names(fit$parFixedDf))
})
})
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