6 files changed, 129 insertions, 128 deletions
diff --git a/man/calm.Rd b/man/calm.Rd
deleted file mode 100644
index c16f663..0000000
--- a/man/calm.Rd
+++ /dev/null
@@ -1,43 +0,0 @@
-\name{calm}
-\alias{calm}
-\alias{print.calm}
-\alias{predict.calm}
-\alias{summary.calm}
-\title{Generate a calibration model}
-\description{
-  This function fits a calibration model to the data 
-  frame.
-}
-\usage{
-  calm(data)
-}
-\arguments{
-  \item{data}{
-    A data frame with numeric x data in the first column and
-    numeric y data in the second column.
-    }
-}
-\value{
-  An object of class \code{calm}, which is derived from
-  a linear model \code{lm}, the only difference being that
-  it contains the additional attributes \code{xname},
-  \code{yname} and \code{intercept}, the latter being a 
-  boolean reporting wether the model uses an intercept or not.
-} 
-\note{
-  The decision if the returned model contains an intercept is taken based on
-  the significance of the fitted intercept on a significance level of 0.95.
-  The methods \code{\link{print.calm}}, \code{\link{predict.calm}}
-  \code{\link{summary.calm}} are just newly assigned names for the
-  corresponding methods from the class \code{\link{lm}}.
-}
-\examples{
-  data(din32645)
-  calm(din32645)
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}
diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
new file mode 100644
index 0000000..c2b8116
--- /dev/null
+++ b/man/calplot.lm.Rd
@@ -0,0 +1,55 @@
+\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}
diff --git a/man/draper.Rd b/man/draper.Rd
new file mode 100644
index 0000000..6a8de00
--- /dev/null
+++ b/man/draper.Rd
@@ -0,0 +1,9 @@
+\name{draper}
+\alias{draper}
+\title{Regression example with repeated measurements}
+\usage{data(draper)}
+\references{Draper and Smith, Applied Regression Analysis (1981), p. 38}
+\format{A dataframe with 24 observations on 2 variables}
+\description{An example of a regression with multiple measurements per
+factor level.}
+\keyword{datasets}
diff --git a/man/inverse.predict.Rd b/man/inverse.predict.Rd
new file mode 100644
index 0000000..48534c4
--- /dev/null
+++ b/man/inverse.predict.Rd
@@ -0,0 +1,65 @@
+\name{inverse.predict}
+\alias{inverse.predict}
+\alias{inverse.predict.lm}
+\alias{inverse.predict.default}
+\title{Predict x from y for a linear calibration}
+\usage{inverse.predict(object, newdata, 
+  ws = ifelse(length(object$weights) > 0, mean(object$weights), 1),
+  alpha=0.05, ss = "auto")
+}
+\arguments{
+  \item{object}{
+    A univariate model object of class \code{\link{lm}} with model formula
+    \code{y ~ x} or \code{y ~ x - 1}.
+  }
+  \item{newdata}{ 
+    A vector of observed y values for one sample.
+  }
+  \item{ws}{ 
+    The weight attributed to the sample. The default is to take the 
+    mean of the weights in the model, if there are any.
+  }
+  \item{alpha}{
+    The confidence level for the confidence interval to be reported.
+  }
+  \item{ss}{
+    The estimated standard error of the sample measurements. The 
+    default is to take the residual standard error from the calibration.
+  }
+}
+\value{
+  A list containing the predicted x value, its standard error and a
+  confidence interval.
+}
+\description{
+  This function predicts x values using a univariate linear model that has been
+  generated for the purpose of calibrating a measurement method. Prediction
+  intervals are given at the specified confidence level.
+  The calculation method was taken from Massart et al. (1997). In particular,
+  Equations 8.26 and 8.28 were combined in order to yield a general treatment
+  of inverse prediction for univariate linear models, taking into account 
+  weights that have been used to create the linear model, and at the same
+  time providing the possibility to specify a precision in sample measurements
+  differing from the precision in standard samples used for the calibration.
+  This is elaborated in the package vignette.
+}
+\note{
+  The function was validated with examples 7 and 8 from Massart et al. (1997).
+}
+\references{
+  Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
+  Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
+  p. 200
+}
+\examples{
+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)
+
+inverse.predict(m,c(15))
+}
+\keyword{manip}
diff --git a/man/plot.calm.Rd b/man/plot.calm.Rd
deleted file mode 100644
index bb302c7..0000000
--- a/man/plot.calm.Rd
+++ /dev/null
@@ -1,48 +0,0 @@
-\name{plot.calm}
-\alias{plot.calm}
-\title{Plot calibration graphs from calibration models}
-\description{
-	Produce graphics of calibration data, the fitted model as well
-  as prediction and confidence intervals.
-}
-\usage{
-  plot.calm(x,...,xunit="",yunit="",measurand="",level=0.95)
-}
-\arguments{
-  \item{x}{
-    A calibration model of type \code{\link{calm}}. It is named
-    x here because the generic plot method expects x to be its 
-    first argument.
-    }
-  \item{...}{
-    I just included this because I wanted to avoid the error messages
-    from R CMD check that tell me I should read "Writing R extensions" 
-    which I did ...
-  }
-  \item{xunit}{
-    The unit of the given values on the x axis as a character vector.
-    }
-  \item{yunit}{
-    The unit of the y axis as a character vector.
-    }
-  \item{measurand}{ 
-    The name of what is being measured as a character vector.
-    }
-  \item{level}{
-    The confidence level of the confidence and prediction bands. Defaults to
-    0.95.
-    }
-}
-\value{
-  A plot of the calibration data, of your fitted model as well as lines showing
-  the confidence limits and the prediction limits.
-} 
-\examples{
-
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}
diff --git a/man/predictx.Rd b/man/predictx.Rd
deleted file mode 100644
index a3946b0..0000000
--- a/man/predictx.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-\name{predictx}
-\alias{predictx}
-\title{Predict x from y values for calibration models}
-\description{
-  This function predicts x values from y values, as in classical calibration,
-  including a confindence interval.
-}
-\usage{
-  predictx(m,yobs,level=0.95)
-}
-\arguments{
-  \item{m}{
-    A calibration model of type \code{\link{calm}}.
-    }
-  \item{yobs}{ 
-    A vector of observed y values for one sample.
-    }
-  \item{level}{
-    The confidence level for the confidence interval to be reported.
-    }
-}
-\value{
-  A vector containing the estimate (\code{estimate}), its estimated standard
-  deviation (\code{sdxpred}), its estimated confidence (\code{confxpred}).
-} 
-\examples{
-  data(din32645)
-  m <- calm(din32645)
-  r <- predictx(m,3500,level=0.95)
-  cat("\nThe confidence interval is",r[["estimate"]],"+-",r[["confxpred"]],"\n")
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}