Augmented Prediction for nlmixr2 fit

Augmented Prediction for nlmixr2 fit

nlmixr2AugPredSolve(
  fit,
  covsInterpolation = c("locf", "nocb", "linear", "midpoint"),
  minimum = NULL,
  maximum = NULL,
  length.out = 51L,
  ...
)

# S3 method for class 'nlmixr2FitData'
augPred(
  object,
  primary = NULL,
  minimum = NULL,
  maximum = NULL,
  length.out = 51,
  ...
)

Arguments

fit

Nlmixr2 fit object

covsInterpolation

specifies the interpolation method for time-varying covariates. When solving ODEs it often samples times outside the sampling time specified in events. When this happens, the time varying covariates are interpolated. Currently this can be:

• "linear" interpolation, which interpolates the covariate by solving the line between the observed covariates and extrapolating the new covariate value.

• "locf" – Last observation carried forward (the default).

• "nocb" – Next Observation Carried Backward. This is the same method that NONMEM uses.

• "midpoint" Last observation carried forward to midpoint; Next observation carried backward to midpoint.

  For time-varying covariates where a missing value is present, the interpolation method will use either "locf" or "nocb" throughout if they are the type of covariate interpolation that is selected.

  When using the linear or midpoint interpolation, the lower point in the interpolation will use locf to interpolate missing covariates and the upper point will use the nocb to interpolate missing covariates.

minimum

an optional lower limit for the primary covariate. Defaults to min(primary).

maximum

an optional upper limit for the primary covariate. Defaults to max(primary).

length.out

an optional integer with the number of primary covariate values at which to evaluate the predictions. Defaults to 51.

...

some methods for the generic may require additional arguments.

object

a fitted model object from which predictions can be extracted, using a predict method.

primary

an optional one-sided formula specifying the primary covariate to be used to generate the augmented predictions. By default, if a covariate can be extracted from the data used to generate object (using getCovariate), it will be used as primary.

Value

Stacked data.frame with observations, individual/population predictions.

Author

Matthew L. Fidler