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\name{calplot}
\alias{calplot}
\alias{calplot.default}
\alias{calplot.lm}
\title{Plot calibration graphs from univariate linear models}
\description{
Produce graphics of calibration data, the fitted model as well
as confidence, and, for unweighted regression, prediction bands.
}
\usage{
calplot(object, xlim = c("auto", "auto"), ylim = c("auto", "auto"),
xlab = "Concentration", ylab = "Response", alpha=0.05, varfunc = NULL)
}
\arguments{
\item{object}{
A univariate model object of class \code{\link{lm}} or
\code{\link[MASS:rlm]{rlm}}
with model formula \code{y ~ x} or \code{y ~ x - 1}.
}
\item{xlim}{
The limits of the plot on the x axis.
}
\item{ylim}{
The limits of the plot on the y axis.
}
\item{xlab}{
The label of the x axis.
}
\item{ylab}{
The label of the y axis.
}
\item{alpha}{
The error tolerance level for the confidence and prediction bands. Note that this
includes both tails of the Gaussian distribution, unlike the alpha and beta parameters
used in \code{\link{lod}} (see note below).
}
\item{varfunc}{
The variance function for generating the weights in the model.
Currently, this argument is ignored (see note below).
}
}
\value{
A plot of the calibration data, of your fitted model as well as lines showing
the confidence limits. Prediction limits are only shown for models from
unweighted regression.
}
\note{
Prediction bands for models from weighted linear regression require weights
for the data, for which responses should be predicted. Prediction intervals
using weights e.g. from a variance function are currently not supported by
the internally used function \code{\link{predict.lm}}, therefore,
\code{calplot} does not draw prediction bands for such models.
It is possible to compare the \code{\link{calplot}} prediction bands with the
\code{\link{lod}} values if the \code{lod()} alpha and beta parameters are
half the value of the \code{calplot()} alpha parameter.
}
\examples{
data(massart97ex3)
m <- lm(y ~ x, data = massart97ex3)
calplot(m)
}
\author{
Johannes Ranke
\email{jranke@uni-bremen.de}
}
\keyword{regression}
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