diff options
Diffstat (limited to 'man')
| -rw-r--r-- | man/din32645.Rd | 13 | ||||
| -rw-r--r-- | man/inverse.predict.Rd | 23 | ||||
| -rw-r--r-- | man/lod.Rd | 1 | ||||
| -rw-r--r-- | man/loq.Rd | 4 | ||||
| -rw-r--r-- | man/massart97ex3.Rd | 31 | ||||
| -rw-r--r-- | man/rl95_toluene.Rd | 16 |
6 files changed, 61 insertions, 27 deletions
diff --git a/man/din32645.Rd b/man/din32645.Rd index 12c641a..ffcbaed 100644 --- a/man/din32645.Rd +++ b/man/din32645.Rd @@ -10,19 +10,18 @@ A dataframe containing 10 rows of x and y values. } \examples{ -data(din32645) m <- lm(y ~ x, data = din32645) calplot(m) ## Prediction of x with confidence interval -(prediction <- inverse.predict(m, 3500, alpha = 0.01)) +prediction <- inverse.predict(m, 3500, alpha = 0.01) # This should give 0.07434 according to test data from Dintest, which # was collected from Procontrol 3.1 (isomehr GmbH) in this case -round(prediction$Confidence,5) +round(prediction$Confidence, 5) ## Critical value: -(crit <- lod(m, alpha = 0.01, beta = 0.5)) +crit <- lod(m, alpha = 0.01, beta = 0.5) # According to DIN 32645, we should get 0.07 for the critical value # (decision limit, "Nachweisgrenze") @@ -40,12 +39,12 @@ round(lod.din$x, 2) ## Limit of quantification # This accords to the test data coming with the test data from Dintest again, # except for the last digits of the value cited for Procontrol 3.1 (0.2121) -(loq <- loq(m, alpha = 0.01)) -round(loq$x,4) +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 +3.04 * lod(m, alpha = 0.01, beta = 0.5)$x } \references{ DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994 diff --git a/man/inverse.predict.Rd b/man/inverse.predict.Rd index 26ba6b8..373623e 100644 --- a/man/inverse.predict.Rd +++ b/man/inverse.predict.Rd @@ -52,7 +52,10 @@ } \note{ The function was validated with examples 7 and 8 from Massart et al. (1997). -} + Note that the behaviour of inverse.predict changed with chemCal version + 0.2.1. Confidence intervals for x values obtained from calibrations with + replicate measurements did not take the variation about the means into account. + Please refer to the vignette for details.} \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, @@ -60,10 +63,26 @@ } \examples{ # This is example 7 from Chapter 8 in Massart et al. (1997) -data(massart97ex1) m <- lm(y ~ x, data = massart97ex1) inverse.predict(m, 15) # 6.1 +- 4.9 inverse.predict(m, 90) # 43.9 +- 4.9 inverse.predict(m, rep(90,5)) # 43.9 +- 3.2 + +# 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) + +inverse.predict(m3.means, 15, ws = 1.67) # 5.9 +- 2.5 +inverse.predict(m3.means, 90, ws = 0.145) # 44.1 +- 7.9 + } \keyword{manip} @@ -74,7 +74,6 @@ Analytica Chimica Acta 391, 105 - 126. } \examples{ -data(din32645) m <- lm(y ~ x, data = din32645) lod(m) @@ -68,9 +68,7 @@ and therefore not tested. Feedback is welcome. } \examples{ -data(massart97ex3) -attach(massart97ex3) -m <- lm(y ~ x) +m <- lm(y ~ x, data = massart97ex1) loq(m) # We can get better by using replicate measurements 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. diff --git a/man/rl95_toluene.Rd b/man/rl95_toluene.Rd new file mode 100644 index 0000000..1faafdc --- /dev/null +++ b/man/rl95_toluene.Rd @@ -0,0 +1,16 @@ +\name{rl95_toluene} +\docType{data} +\alias{rl95_toluene} +\title{Toluene amounts by GC/MS as reported by Rocke and Lorenzato (1995)} +\description{ + Dataset reproduced from Table 4 in Rocke and Lorenzato (1995). +} +\format{ + A dataframe containing four replicate observations for each + of the six calibration standards. +} +\source{ + Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for + measurement error in analytical chemistry. Technometrics 37(2), 176-184. +} +\keyword{datasets} |