loq {chemCal} R Documentation

## Estimate a limit of quantification (LOQ)

### 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.

### 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.

### 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.

### See Also

Examples for `din32645`

### Examples

```m <- lm(y ~ x, data = massart97ex1)
loq(m)

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

[Package chemCal version 0.2.2 Index]