Calibration data from DIN 32645 — din32645 • chemCal

m <- lm(y ~ x, data = din32645) +
m <- lm(y ~ x, data = din32645)
 calplot(m)

 ## Prediction of x with confidence interval
 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)
#> [1] 0.07434

+round(prediction$Confidence, 5)
#> [1] 0.07434

 ## Critical value:
 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")
-round(crit$x, 2)
#> [1] 0.07
# and according to Dintest test data, we should get 0.0698 from
-round(crit$x, 4)
#> [1] 0.0698

+round(crit$x, 2)
#> [1] 0.07
# and according to Dintest test data, we should get 0.0698 from
+round(crit$x, 4)
#> [1] 0.0698

 ## Limit of detection (smallest detectable value given alpha and beta)
 # In German, the smallest detectable value is the "Erfassungsgrenze", and we
 # should get 0.14 according to DIN, which we achieve by using the method 
 # described in it:
 lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
-round(lod.din$x, 2)
#> [1] 0.14

+round(lod.din$x, 2)
#> [1] 0.14

 ## 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)
#> [1] 0.212

+round(loq$x, 4)
#> [1] 0.212

 # 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
#> [1] 0.2122306
Format

References

Examples

Contents
