Imported revision 18, because this was last released on CRAN

with version number 0.1-18



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@24 5fad18fb-23f0-0310-ab10-e59a3bee62b4

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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}