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-\name{massart97ex3}
-\docType{data}
-\alias{massart97ex3}
-\title{Calibration data from Massart et al. (1997), example 3}
-\description{
-  Sample dataset from p. 188 to test the package.
-}
-\usage{data(massart97ex3)}
-\format{
-  A dataframe containing 6 levels of x values with 5
-  observations of y for each level.
-}
-\examples{
-data(massart97ex3)
-attach(massart97ex3)
-yx <- split(y, x)
-ybar <- sapply(yx, mean)
-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)
-
-# The following concords with the book p. 200
-inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
-inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-
-# The LOD is only calculated for models from unweighted regression
-# with this version of chemCal
-m0 <- lm(y ~ x) 
-lod(m0)
-
-# Limit of quantification from unweighted regression
-loq(m0)
-
-# For calculating the limit of quantification from a model from weighted
-# regression, we need to supply weights, internally used for inverse.predict
-# If we are not using a variance function, we can use the weight from
-# the above example as a first approximation (x = 15 is close to our
-# loq approx 14 from above).
-loq(m, w.loq = 1.67)
-# The weight for the loq should therefore be derived at x = 7.3 instead
-# of 15, but the graphical procedure of Massart (p. 201) to derive the 
-# variances on which the weights are based is quite inaccurate anyway. 
-}
-\source{
-  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,
-  Chapter 8.
-}
-\keyword{datasets}