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+\name{inverse.predict}
+\alias{inverse.predict}
+\alias{inverse.predict.lm}
+\alias{inverse.predict.rlm}
+\alias{inverse.predict.default}
+\title{Predict x from y for a linear calibration}
+\usage{inverse.predict(object, newdata, \dots,
+  ws, alpha=0.05, var.s = "auto")
+}
+\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{newdata}{ 
+    A vector of observed y values for one sample.
+  }
+  \item{\dots}{
+    Placeholder for further arguments that might be needed by 
+    future implementations.
+  }
+  \item{ws}{ 
+    The weight attributed to the sample. This argument is obligatory
+    if \code{object} has weights.
+  }
+  \item{alpha}{
+    The error tolerance level for the confidence interval to be reported.
+  }
+  \item{var.s}{
+    The estimated variance of the sample measurements. The default is to take
+    the residual standard error from the calibration and to adjust it
+    using \code{ws}, if applicable. This means that \code{var.s}
+    overrides \code{ws}.
+  }
+}
+\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{
+# This is example 7 from Chapter 8 in Massart et al. (1997)
+data(massart97ex1)
+m <- lm(y ~ x, data = massart97ex1)
+inverse.predict(m, 15)        #  6.1 +- 4.9
+inverse.predict(m, 90)        # 43.9 +- 4.9
+inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
+}
+\keyword{manip}