The inverse prediction works in a variety of cases and is

tested with Examples 7 and 8 from Massart!

I need to compare with the DIN and draper examples, and finish
the package vignette.



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@6 5fad18fb-23f0-0310-ab10-e59a3bee62b4

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diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
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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}} 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 confidence 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
+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=weights)
+calplot(m)
+}
+\author{
+  Johannes Ranke 
+  \email{jranke@uni-bremen.de} 
+  \url{http://www.uft.uni-bremen.de/chemie/ranke}
+}
+\keyword{regression}