From 280d36230052de4f94e384648c1283031fbc9840 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 17 Jul 2018 17:29:14 +0200 Subject: Fix inverse predictions for replicate measurements For details, see NEWS.md --- docs/reference/massart97ex3.html | 63 +++++++++++++++++++++++----------------- 1 file changed, 36 insertions(+), 27 deletions(-) (limited to 'docs/reference/massart97ex3.html') diff --git a/docs/reference/massart97ex3.html b/docs/reference/massart97ex3.html index 434fc7b..e9fa06a 100644 --- a/docs/reference/massart97ex3.html +++ b/docs/reference/massart97ex3.html @@ -61,17 +61,26 @@ chemCal - 0.1.37.9001 + 0.2.1 - 
data(massart97ex3)
+ 
massart97ex3

Format

@@ -115,18 +124,21 @@

Examples

- 
data(massart97ex3)
-attach(massart97ex3)
#> The following objects are masked from massart97ex3 (pos = 3):
-#> 
-#>     x, y
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)
#> Warning: Assuming constant prediction variance even though model fit is weighted

+    
# 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
#> $Prediction
+inverse.predict(m3.means, 15, ws = 1.67)  # 5.9 +- 2.5
#> $Prediction
 #> [1] 5.865367
 #> 
 #> $`Standard Error`
@@ -137,7 +149,7 @@
 #> 
 #> $`Confidence Limits`
 #> [1] 3.387082 8.343652
-#> 
inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
#> $Prediction
+#> 
inverse.predict(m3.means, 90, ws = 0.145) # 44.1 +- 7.9
#> $Prediction
 #> [1] 44.06025
 #> 
 #> $`Standard Error`
@@ -151,33 +163,30 @@
 #> 

 # The LOD is only calculated for models from unweighted regression
 # with this version of chemCal
-m0 <- lm(y ~ x)
-lod(m0)
#> $x
+m0 <- lm(y ~ x, data = massart97ex3)
+lod(m0)
#> $x
 #> [1] 5.407085
 #> 
 #> $y
-#>        1 
-#> 13.63911 
+#> [1] 13.63911
 #> 

 # Limit of quantification from unweighted regression
-loq(m0)
#> $x
-#> [1] 13.97764
+loq(m0)
#> $x
+#> [1] 9.627349
 #> 
 #> $y
-#>       1 
-#> 30.6235 
+#> [1] 22.00246
 #> 

 # 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)
#> $x
+loq(m3.means, w.loq = 1.67)
#> $x
 #> [1] 7.346195
 #> 
 #> $y
-#>        1 
-#> 17.90777 
+#> [1] 17.90777
 #> 
# 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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