- Fixed the inverse prediction

- Now I have a working approach for the calculation of LOD and LOQ,
  but it seems to be different from what everybody else is doing
  (e.g. Massart chaper 13). I like it, however. Maybe it even 
  yields a paper.



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@8 5fad18fb-23f0-0310-ab10-e59a3bee62b4

1 files changed, 26 insertions, 0 deletions

diff --git a/man/massart97ex3.Rd b/man/massart97ex3.Rd
index 1dabf90..2618709 100644
--- a/man/massart97ex3.Rd
+++ b/man/massart97ex3.Rd
@@ -10,6 +10,32 @@
   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)
+# The following concords with the book
+inverse.predict(m, 15, ws = 1.67)
+inverse.predict(m, 90, ws = 0.145)
+
+calplot(m)
+
+m0 <- lm(y ~ x)
+lod(m0)
+lod(m)
+
+# 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)
+}
 \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