chemCal 0.1-35.900

Calibration data from DIN 32645

Usage

data(din32645)

Description

Sample dataset to test the package.

Format

A dataframe containing 10 rows of x and y values.

References

DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994

Dintest. Plugin for MS Excel for evaluations of calibration data. Written by Georg Schmitt, University of Heidelberg. http://www.rzuser.uni-heidelberg.de/~df6/download/dintest.htm

Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC Recommendations 1995). Analytica Chimica Acta 391, 105 - 126.

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
[1] 0.1054792

$`Standard Error`
[1] 0.02215619

$Confidence
[1] 0.07434261

$`Confidence Limits`
[1] 0.03113656 0.17982178





# 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




## Critical value:
(crit <- lod(m, alpha = 0.01, beta = 0.5))



$x
[1] 0.0698127

$y
       1 
3155.393 





# 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




## 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




## 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))



$x
[1] 0.2119575

$y
       1 
4528.787 




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