poso_estim_map {posologyr} | R Documentation |
Estimates the Maximum A Posteriori (MAP) individual parameters, also known as Empirical Bayes Estimates (EBE).
poso_estim_map(
dat = NULL,
prior_model = NULL,
return_model = TRUE,
return_ofv = FALSE,
nocb = FALSE
)
dat |
Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records. |
prior_model |
A |
return_model |
A boolean. Returns a rxode2 model using the estimated
ETAs if set to |
return_ofv |
A boolean. Returns a the Objective Function Value (OFV)
if set to |
nocb |
A boolean. for time-varying covariates: the next observation
carried backward (nocb) interpolation style, similar to NONMEM. If
|
A named list consisting of one or more of the following elements
depending on the input parameters of the function: $eta
a named vector
of the MAP estimates of the individual values of ETA, $model
an rxode2
model using the estimated ETAs, $event
the data.table
used to solve the
returned rxode2 model.
rxode2::setRxThreads(1) # limit the number of threads
# model
mod_run001 <- function() {
ini({
THETA_Cl <- 4.0
THETA_Vc <- 70.0
THETA_Ka <- 1.0
ETA_Cl ~ 0.2
ETA_Vc ~ 0.2
ETA_Ka ~ 0.2
prop.sd <- sqrt(0.05)
})
model({
TVCl <- THETA_Cl
TVVc <- THETA_Vc
TVKa <- THETA_Ka
Cl <- TVCl*exp(ETA_Cl)
Vc <- TVVc*exp(ETA_Vc)
Ka <- TVKa*exp(ETA_Ka)
K20 <- Cl/Vc
Cc <- centr/Vc
d/dt(depot) = -Ka*depot
d/dt(centr) = Ka*depot - K20*centr
Cc ~ prop(prop.sd)
})
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
TIME=c(0.0,1.0,14.0),
DV=c(NA,25.0,5.5),
AMT=c(2000,0,0),
EVID=c(1,0,0),
DUR=c(0.5,NA,NA))
# estimate the Maximum A Posteriori individual parameters
poso_estim_map(dat=df_patient01,prior_model=mod_run001)