loq {chemCal} | R Documentation |
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.
loq(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto", var.loq = "auto", tol = "default")
object |
A univariate model object of class |
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 |
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. |
The estimated limit of quantification for a model used for calibration.
- 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 for din32645
m <- lm(y ~ x, data = massart97ex1) loq(m) # We can get better by using replicate measurements loq(m, n = 3)