From 9e0dae397df8c18e7333d2604cae96b2a7927420 Mon Sep 17 00:00:00 2001 From: ranke Date: Fri, 23 Jun 2006 15:33:27 +0000 Subject: - inverse.predict now has a var.s argument instead of the never tested ss argument. This is documented in the updated vignette - loq() now has w.loq and var.loq arguments, and stops with a message if neither are specified and the model has weights. - calplot doesn't stop any more for weighted regression models, but only refrains from drawing prediction bands - Added method = "din" to lod(), now that I actually have it (DIN 32645) and was able to see which approximation is used therein. - removed the demos, as the examples and tests are already partially duplicated - The vignette is more of a collection of various notes, but should certainly be helpful for the user. - Version bump to 0.1-xxx git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@16 5fad18fb-23f0-0310-ab10-e59a3bee62b4 --- man/massart97ex3.Rd | 59 +++++++++++++++++++++++++++++------------------------ 1 file changed, 32 insertions(+), 27 deletions(-) (limited to 'man/massart97ex3.Rd') 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} -- cgit v1.2.1