From b31824f420c9d904ab5f46774183a59e3b86cedd Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 4 Oct 2016 08:45:23 +0200 Subject: Static documentation built using newer staticdocs::build_site() --- docs/loq.html | 191 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 191 insertions(+) create mode 100644 docs/loq.html (limited to 'docs/loq.html') diff --git a/docs/loq.html b/docs/loq.html new file mode 100644 index 0000000..352737f --- /dev/null +++ b/docs/loq.html @@ -0,0 +1,191 @@ + + + + +loq. chemCal 0.1-37 + + + + + + + + + + + + + + + +
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+ chemCal 0.1-37 +
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+ + +
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+ +
+ +

Estimate a limit of quantification (LOQ)

+ +
+
+

Usage

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

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. +
+
+ +
+

Value

+ +

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

+ +
+ +
+

Description

+ +

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.

+ +
+ +
+

Note

+ +

- 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.

+ +
+ +

Examples

+ 
data(massart97ex3)
+attach(massart97ex3)
+m <- lm(y ~ x)
+loq(m)

+
$x
+[1] 13.97764
+
+$y
+      1 
+30.6235 
+
+

+

+# We can get better by using replicate measurements
+loq(m, n = 3)

+
$x
+[1] 9.971963
+
+$y
+       1 
+22.68539 
+
+
+
+
+ + +

See also

+

Examples for din32645

+ + + + +
+
+ + +
+ + \ No newline at end of file -- cgit v1.2.1