1 files changed, 32 insertions, 27 deletions

diff --git a/man/massart97ex3.Rd b/man/massart97ex3.Rd
index eb00e79..e7cd383 100644
--- a/man/massart97ex3.Rd
+++ b/man/massart97ex3.Rd
@@ -3,7 +3,7 @@
 \alias{massart97ex3}
 \title{Calibration data from Massart et al. (1997), example 3}
 \description{
-  Sample dataset to test the package.
+  Sample dataset from p. 188 to test the package.
 }
 \usage{data(massart97ex3)}
 \format{
@@ -11,37 +11,42 @@
   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)
-# The following concords with the book
-inverse.predict(m, 15, ws = 1.67)
-inverse.predict(m, 90, ws = 0.145)
+  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)
 
-# Some of the following examples are commented out, because the require
-# prediction intervals from predict.lm for weighted models, which is not
-# available in R at the moment.
+  # 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
 
-#calplot(m)
+  # The LOD is only calculated for models from unweighted regression
+  # with this version of chemCal
+  m0 <- lm(y ~ x) 
+  lod(m0)
 
-m0 <- lm(y ~ x)
-lod(m0)
-#lod(m)
+  # Limit of quantification from unweighted regression
+  m0 <- lm(y ~ x)
+  loq(m0)
 
-# Now we want to take advantage of the lower weights at lower y values
-#m2 <- lm(y ~ x, w = 1/y)
-# To get a reasonable weight for the lod, we need to estimate it and predict 
-# a y value for it
-#yhat.lod <- predict(m,data.frame(x = lod(m2)))
-#lod(m2,w=1/yhat.lod,k=3)
+  # 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, p. 188
+  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}