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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 prediction and confidence bands.
}
\usage{
calplot(object, xlim = "auto", ylim = "auto",
xlab = "Concentration", ylab = "Response", alpha=0.05)
}
\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.
}
}
\value{
A plot of the calibration data, of your fitted model as well as lines showing
the confidence limits as well as the prediction limits.
}
\examples{
# Example of a Calibration plot for a weighted regression
source("/home/ranke/tmp/r-base-2.3.0/src/library/stats/R/lm.R")
data(massart97ex3)
attach(massart97ex3)
yx <- split(y,factor(x))
s <- round(sapply(yx,sd),digits=2)
w <- round(1/(s^2),digits=3)
weights <- w[factor(x)]
m <- lm(y ~ x,w=10 * weights)
calplot(m)
calplot(m)
}
\author{
Johannes Ranke
\email{jranke@uni-bremen.de}
\url{http://www.uft.uni-bremen.de/chemie/ranke}
}
\keyword{regression}
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