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nlmixrAugPred.Rd
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nlmixrAugPred.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulate.R
\name{nlmixrAugPred}
\alias{nlmixrAugPred}
\alias{augPred.nlmixrFitData}
\title{Augmented Prediction for nlmixr fit}
\usage{
nlmixrAugPred(object, ..., covsInterpolation = c("linear", "locf",
"nocb", "midpoint"), primary = NULL, minimum = NULL,
maximum = NULL, length.out = 51L)
\method{augPred}{nlmixrFitData}(object, primary = NULL,
minimum = min(primary), maximum = max(primary), length.out = 51,
...)
}
\arguments{
\item{object}{Nlmixr fit object}
\item{...}{some methods for the generic may require additional
arguments.}
\item{covsInterpolation}{specifies the interpolation method for
time-varying covariates. When solving ODEs it often samples
times outside the sampling time specified in \code{events}.
When this happens, the time varying covariates are
interpolated. Currently this can be:
\itemize{
\item \code{"linear"} interpolation (the default), which interpolates the covariate
by solving the line between the observed covariates and extrapolating the new
covariate value.
\item \code{"constant"} -- Last observation carried forward.
\item \code{"NOCB"} -- Next Observation Carried Backward. This is the same method
that NONMEM uses.
\item \code{"midpoint"} Last observation carried forward to midpoint; Next observation
carried backward to midpoint.
}}
\item{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
\code{object} (using \code{getCovariate}), it will be used as
\code{primary}.}
\item{minimum}{an optional lower limit for the primary
covariate. Defaults to \code{min(primary)}.}
\item{maximum}{an optional upper limit for the primary
covariate. Defaults to \code{max(primary)}.}
\item{length.out}{an optional integer with the number of primary
covariate values at which to evaluate the predictions. Defaults to
51.}
}
\value{
Stacked data.frame with observations, individual/population predictions.
}
\description{
Augmented Prediction for nlmixr fit
}
\author{
Matthew L. Fidler
}