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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} 
+  \url{http://www.uft.uni-bremen.de/chemie/ranke}
+}
+\keyword{regression}