From 14a5af60a36071f6a9b4471fdf183fd91e89e1cd Mon Sep 17 00:00:00 2001
From: ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>
Date: Mon, 1 Oct 2007 19:44:04 +0000
Subject: Moved everything into the trunk directory, in order to enable
 branching

git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@22 5fad18fb-23f0-0310-ab10-e59a3bee62b4
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 man/inverse.predict.Rd | 69 --------------------------------------------------
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 delete mode 100644 man/inverse.predict.Rd

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diff --git a/man/inverse.predict.Rd b/man/inverse.predict.Rd
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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}
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