loq. chemCal 0.1-37

Usage

loq(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto", var.loq = "auto", tol = "default")

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Arguments

object: - A univariate model object of class lm or - rlm - with model formula y ~ x or y ~ x - 1, - optionally from a weighted regression. If weights are specified - in the model, either w.loq or var.loq have to - be specified. -
alpha: - The error tolerance for the prediction of x values in the calculation. -
...: - Placeholder for further arguments that might be needed by - future implementations. -
k: - The inverse of the maximum relative error tolerated at the - desired LOQ. -
n: - The number of replicate measurements for which the LOQ should be - specified. -
w.loq: - The weight that should be attributed to the LOQ. Defaults - to one for unweighted regression, and to the mean of the weights - for weighted regression. See massart97ex3 for - an example how to take advantage of knowledge about the - variance function. -
var.loq: - The approximate variance at the LOQ. The default value is - calculated from the model. -
tol: - The default tolerance for the LOQ on the x scale is the value of the - smallest non-zero standard divided by 1000. Can be set to a - numeric value to override this. -

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Value

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- The estimated limit of quantification for a model used for calibration. -

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Description

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The limit of quantification is the x value, where the relative error - of the quantification given the calibration model reaches a prespecified - value 1/k. Thus, it is the solution of the equation - $$L = k c(L)$$ - where c(L) is half of the length of the confidence interval at the limit L - (DIN 32645, equivalent to ISO 11843). c(L) is internally estimated by - inverse.predict, and L is obtained by iteration.

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Note

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- IUPAC recommends to base the LOQ on the standard deviation of the signal - where x = 0. - - The calculation of a LOQ based on weighted regression is non-standard - and therefore not tested. Feedback is welcome.

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Examples

data(massart97ex3)
-attach(massart97ex3)
-m <- lm(y ~ x)
-loq(m)
-
-$x
-[1] 13.97764
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-$y
-      1 
-30.6235 
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-
-
-# We can get better by using replicate measurements
-loq(m, n = 3)
-
-$x
-[1] 9.971963
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-$y
-       1 
-22.68539 
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-

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