diff options
Diffstat (limited to 'man/massart97ex3.Rd')
| -rw-r--r-- | man/massart97ex3.Rd | 31 |
1 files changed, 17 insertions, 14 deletions
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. |