From 280d36230052de4f94e384648c1283031fbc9840 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Tue, 17 Jul 2018 17:29:14 +0200
Subject: Fix inverse predictions for replicate measurements

For details, see NEWS.md
---
 man/massart97ex3.Rd | 31 +++++++++++++++++--------------
 1 file changed, 17 insertions(+), 14 deletions(-)

(limited to 'man/massart97ex3.Rd')

diff --git a/man/massart97ex3.Rd b/man/massart97ex3.Rd
index efdcf02..d7f8d00 100644
--- a/man/massart97ex3.Rd
+++ b/man/massart97ex3.Rd
@@ -5,29 +5,32 @@
 \description{
   Sample dataset from p. 188 to test the package.
 }
-\usage{data(massart97ex3)}
+\usage{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)
+# For reproducing the results for replicate standard measurements in example 8,
+# we need to do the calibration on the means when using chemCal > 0.2
+weights <- with(massart97ex3, {
+  yx <- split(y, x)
+  ybar <- sapply(yx, mean)
+  s <- round(sapply(yx, sd), digits = 2)
+  w <- round(1 / (s^2), digits = 3)
+})
+
+massart97ex3.means <- aggregate(y ~ x, massart97ex3, mean)
+
+m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means)
 
 # 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
+inverse.predict(m3.means, 15, ws = 1.67)  # 5.9 +- 2.5
+inverse.predict(m3.means, 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) 
+m0 <- lm(y ~ x, data = massart97ex3) 
 lod(m0)
 
 # Limit of quantification from unweighted regression
@@ -38,7 +41,7 @@ loq(m0)
 # 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)
+loq(m3.means, 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. 
-- 
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