From cf329866248fab96ea60d1d7ee20562a3da2eb54 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 4 Oct 2016 08:46:58 +0200 Subject: Remove old version of static documentation --- inst/web/loq.html | 189 ------------------------------------------------------ 1 file changed, 189 deletions(-) delete mode 100644 inst/web/loq.html (limited to 'inst/web/loq.html') diff --git a/inst/web/loq.html b/inst/web/loq.html deleted file mode 100644 index f628808..0000000 --- a/inst/web/loq.html +++ /dev/null @@ -1,189 +0,0 @@ - - - - -loq. chemCal 0.1-37 - - - - - - - - - - - - - - - - - -
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Estimate a limit of quantification (LOQ)

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Usage

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

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object
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- 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. -
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alpha
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- The error tolerance for the prediction of x values in the calculation. -
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...
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- Placeholder for further arguments that might be needed by - future implementations. -
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k
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- The inverse of the maximum relative error tolerated at the - desired LOQ. -
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n
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- The number of replicate measurements for which the LOQ should be - specified. -
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w.loq
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- 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. -
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var.loq
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- The approximate variance at the LOQ. The default value is - calculated from the model. -
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tol
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- 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

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

- - Examples for din32645 - - -
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