nlmixr2extra (version 5.0.0)

profile.nlmixr2FitCore: Perform likelihood profiling on nlmixr2 focei fits

Description

Perform likelihood profiling on nlmixr2 focei fits

Usage

# S3 method for nlmixr2FitCore
profile(
  fitted,
  ...,
  which = NULL,
  method = c("llp", "fixed"),
  control = list()
)

Value

A data.frame with one column named Parameter indicating the parameter being fixed on that row, one column for the OFV indicating the OFV estimated for the model at that step, one column named profileBound

indicating the estimated value for the profile likelihood and its step above the minimum profile likelihood value, and columns for each parameter estimate (or fixed) in the model.

Arguments

fitted

The fit model

...

ignored

which

The parameter names to perform likelihood profiling on (NULL indicates all parameters)

method

Method to use for profiling (see the details)

control

Control arguments for the method

Log-likelihood profiling

method = "llp"

The search will stop when either the OFV is within ofvtol of the desired OFV change or when the parameter is interpolating to more significant digits than specified in paramDigits. The "llp" method uses the profileLlp() function. See its help for more details.

Fixed points

method = "fixed"

Estimate the OFV for specific fixed values. The "fixed" method uses the profileFixed() function. See its help for more details.

See Also

Other Profiling: fixedControl(), llpControl(), profileFixed(), profileLlp(), profileNlmixr2FitCoreRet()

Examples

if (FALSE) {
# Likelihood profiling takes a long time to run each model multiple times, so
# be aware that running this example may take a few minutes.
oneCmt <- function() {
  ini({
    tka <- log(1.57)
    tcl <- log(2.72)
    tv <- fixed(log(31.5))
    eta.ka ~ 0.6
    add.sd <- 0.7
  })
  model({
    ka <- exp(tka + eta.ka)
    cl <- exp(tcl)
    v <- exp(tv)
    cp <- linCmt()
    cp ~ add(add.sd)
  })
}

fit <-
  nlmixr2(
    oneCmt, data = nlmixr2data::theo_sd, est="focei", control = list(print=0)
  )
# profile all parameters
profall <- profile(fit)

# profile a single parameter
proftka <- profile(fit, which = "tka")
}