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authorranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2006-05-10 15:44:14 +0000
committerranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2006-05-10 15:44:14 +0000
commit513dfbdcdda94a901b5901b486ff5500c7d158b1 (patch)
treefefbf7daadbd7da71add3ed63b3d3b07c4c8e4df /man/inverse.predict.Rd
parent8d30b2cd951c992e4f9aa3055054091e18b8b4f0 (diff)
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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+\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}

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