as.nlmixr2 | R Documentation |
Convert an object to a nlmixr2 fit object
as.nlmixr2(
x,
...,
table = nlmixr2est::tableControl(),
rxControl = rxode2::rxControl(),
ci = 0.95
)
as.nlmixr(
x,
...,
table = nlmixr2est::tableControl(),
rxControl = rxode2::rxControl(),
ci = 0.95
)
x |
Object to convert |
... |
Other arguments |
table |
is the |
rxControl |
is the |
ci |
is the confidence interval of the residual differences calculated (by default 0.95) |
nlmixr2 fit object
Matthew L. Fidler
# First read in the model (but without residuals)
mod <- nonmem2rx(system.file("mods/cpt/runODE032.ctl", package="nonmem2rx"),
determineError=FALSE, lst=".res", save=FALSE)
# define the model with residuals (and change the name of the
# parameters) In this step you need to be careful to not change the
# estimates and make sure the residual estimates are correct (could
# have to change var to sd).
mod2 <-function() {
ini({
lcl <- 1.37034036528946
lvc <- 4.19814911033061
lq <- 1.38003493562413
lvp <- 3.87657341967489
RSV <- c(0, 0.196446108190896, 1)
eta.cl ~ 0.101251418415006
eta.v ~ 0.0993872449483344
eta.q ~ 0.101302674763154
eta.v2 ~ 0.0730497519364148
})
model({
cmt(CENTRAL)
cmt(PERI)
cl <- exp(lcl + eta.cl)
v <- exp(lvc + eta.v)
q <- exp(lq + eta.q)
v2 <- exp(lvp + eta.v2)
v1 <- v
scale1 <- v
k21 <- q/v2
k12 <- q/v
d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1
d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL
f <- CENTRAL/scale1
f ~ prop(RSV)
})
}
# now we create another nonmem2rx object that validates the model above:
new <- as.nonmem2rx(mod2, mod)
# once that is done, you can translate to a full nlmixr2 fit (if you wish)
fit <- as.nlmixr2(new)
print(fit)
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