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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", legend_x = "auto",
-    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{legend_x}{
-    An optional numeric value for adjusting the x coordinate of the legend.
-  }
-  \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}