Don't do calplot and lod for linear models from weighted

regression any more, since this is not supported (PR#8877).



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@13 5fad18fb-23f0-0310-ab10-e59a3bee62b4

4 files changed, 60 insertions, 17 deletions

diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
index 6d3f52d..734933d 100644
--- a/man/calplot.lm.Rd
+++ b/man/calplot.lm.Rd
@@ -39,13 +39,15 @@
 } 
 \examples{
 # Example of a Calibration plot for a weighted regression
+source("/home/ranke/tmp/r-base-2.3.0/src/library/stats/R/lm.R")
 data(massart97ex3)
 attach(massart97ex3)
 yx <- split(y,factor(x))
 s <- round(sapply(yx,sd),digits=2)
 w <- round(1/(s^2),digits=3)
 weights <- w[factor(x)]
-m <- lm(y ~ x,w=weights)
+m <- lm(y ~ x,w=10 * weights)
+calplot(m)
 calplot(m)
 }
 \author{
diff --git a/man/din32645.Rd b/man/din32645.Rd
index d251b7c..94486c4 100644
--- a/man/din32645.Rd
+++ b/man/din32645.Rd
@@ -14,18 +14,33 @@ data(din32645)
 m <- lm(y ~ x, data=din32645)
 calplot(m)
 (prediction <- inverse.predict(m,3500,alpha=0.01))
-# This should give 0.074 according to DIN (cited from the Dintest test data)
-round(prediction$Confidence,3)
+# This should give 0.07434 according to Dintest test data, as 
+# collected from Procontrol 3.1 (isomehr GmbH)
+round(prediction$Confidence,5)
 
-# According to Dintest, we should get 0.07, but we get 0.0759
-lod(m, alpha = 0.01)
+# According to Dintest test data, we should get 0.0698 for the critical value
+# (decision limit, "Nachweisgrenze")
+(lod <- lod(m, alpha = 0.01, beta = 0.5))
+round(lod$x,4)
 
-# In German, there is the "Erfassungsgrenze", with k = 2, 
-# and we should get 0.14 according to Dintest
-lod(m, k = 2, alpha = 0.01)
+# In German, the smallest detectable value is the "Erfassungsgrenze", and we
+# should get 0.140 according to Dintest test data, but with chemCal, we can't
+# reproduce this, 
+lod(m, alpha = 0.01, beta = 0.01) 
+# except by using an equivalent to the approximation
+# xD = 2 * Sc / A (Currie 1999, p. 118, or Orange Book, Chapter 18.4.3.7)
+lod.approx <- 2 * lod$x
+round(lod.approx, digits=3)
+# which seems to be the pragmatic definition in DIN 32645, as judging from
+# the Dintest test data. 
 
-# According to Dintest, we should get 0.21, we get 0.212
-loq(m, alpha = 0.01)
+# This accords to the test data from Dintest again, except for the last digit
+# of the value cited for Procontrol 3.1 (0.2121)
+(loq <- loq(m, alpha = 0.01))
+round(loq$x,4)
+# A similar value is obtained using the approximation 
+# LQ = 3.04 * LC (Currie 1999, p. 120)
+3.04 * lod(m,alpha = 0.01, beta = 0.5)$x
 }
 \references{
   DIN 32645 (equivalent to ISO 11843)
@@ -33,5 +48,9 @@ loq(m, alpha = 0.01)
   Dintest. Plugin for MS Excel for evaluations of calibration data. Written
   by Georg Schmitt, University of Heidelberg. 
   \url{http://www.rzuser.uni-heidelberg.de/~df6/download/dintest.htm}
+
+  Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
+  detection and quantification capabilities (IUPAC Recommendations 1995). 
+  Analytica Chimica Acta 391, 105 - 126.
 }
 \keyword{datasets}
diff --git a/man/lod.Rd b/man/lod.Rd
index 15f9603..fa8c8ad 100644
--- a/man/lod.Rd
+++ b/man/lod.Rd
@@ -38,18 +38,35 @@
   the analyte is present (type II or false negative error), is beta (also a
   one-sided significance test).
 }
+\note{
+  - The default values for alpha and beta are recommended by IUPAC.
+  - The estimation of the LOD in terms of the analyte amount/concentration
+    xD from the LOD in the signal domain SD is done by simply inverting the
+    calibration function (i.e. assuming a known calibration function).
+}
 \references{
+  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 13.7.8 
+
   J. Inczedy, T. Lengyel, and A.M. Ure (2002) International Union of Pure and
   Applied Chemistry Compendium of Analytical Nomenclature: Definitive Rules.
   Web edition.
+
+  Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
+  detection and quantification capabilities (IUPAC Recommendations 1995). 
+  Analytica Chimica Acta 391, 105 - 126.
 }
 \examples{
   data(din32645)
   m <- lm(y ~ x, data = din32645)
-  # The decision limit (critical value) is obtained by using beta = 0.5:
-  lod(m, alpha = 0.01, beta = 0.5) # approx. Nachweisgrenze in Dintest 2002
-  lod(m, alpha = 0.01, beta = 0.01) 
-  # In the latter case (Erfassungsgrenze), we get a slight deviation from 
-  # Dintest 2002 test data.
+  lod(m) 
+
+  # The critical value (decision limit, German Nachweisgrenze) can be obtained
+  # by using beta = 0.5:
+  lod(m, alpha = 0.01, beta = 0.5)
+  # or approximated by 
+  2 * lod(m, alpha = 0.01, beta = 0.5)$x
+  # for the case of known, constant variance (homoscedastic data)
 }
 \keyword{manip}
diff --git a/man/loq.Rd b/man/loq.Rd
index 1030399..4850487 100644
--- a/man/loq.Rd
+++ b/man/loq.Rd
@@ -49,6 +49,11 @@
   limit of detection is the x value, where the relative error
   of the quantification with the given calibration model is 1/k.
 }
+\note{
+  IUPAC recommends to base the LOQ on the standard deviation of the
+  signal where x = 0. The approach taken here is to my knowledge
+  original to the chemCal package.
+}
 \examples{
   data(massart97ex3)
   attach(massart97ex3)
@@ -68,9 +73,9 @@
   mwy <- lm(y ~ x, w = 1/y)
 
   # Let's do this with one iteration only
-  loq(mwy, w = 1 / predict(mwy,list(x = loq(mwy))))
+  loq(mwy, w = 1 / predict(mwy,list(x = loq(mwy)$x)))
 
   # We can get better by doing replicate measurements
-  loq(mwy, n = 3, w = 1 / predict(mwy,list(x = loq(mwy))))
+  loq(mwy, n = 3, w = 1 / predict(mwy,list(x = loq(mwy)$x)))
 }
 \keyword{manip}