Description Usage Arguments Value Note See Also Examples

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.

1 2 |

`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`

1 2 3 4 5 | ```
m <- lm(y ~ x, data = massart97ex1)
loq(m)
# We can get better by using replicate measurements
loq(m, n = 3)
``` |

```
$x
[1] 13.97764
$y
[1] 30.6235
$x
[1] 9.971963
$y
[1] 22.68539
```

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