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\name{lod}
\alias{lod}
\alias{lod.lm}
\alias{lod.rlm}
\alias{lod.default}
\alias{loq}
\alias{loq.lm}
\alias{loq.rlm}
\alias{loq.default}
\title{Estimate a limit of detection (LOD) or quantification (LOQ)}
\usage{
lod(object, \dots, alpha = 0.05, k = 1, n = 1, w = "auto")
loq(object, \dots, alpha = 0.05, k = 3, n = 1, w = "auto")
}
\arguments{
\item{object}{
A univariate model object of class \code{\link{lm}} or
\code{\link[MASS:rlm]{rlm}}
with model formula \code{y ~ x} or \code{y ~ x - 1},
optionally from a weighted regression.
}
\item{alpha}{
The error tolerance for the prediction of x values in the calculation.
}
\item{\dots}{
Placeholder for further arguments that might be needed by
future implementations.
}
\item{k}{
The inverse of the maximum relative error tolerated at the
desired LOD/LOQ.
}
\item{n}{
The number of replicate measurements for which the LOD/LOQ should be
specified.
}
\item{w}{
The weight that should be attributed to the LOD/LOQ. Defaults
to one for unweighted regression, and to the mean of the weights
for weighted regression. See \code{\link{massart97ex3}} for
an example how to take advantage of knowledge about the variance function.
}
}
\value{
The estimated limit of detection for a model used for calibration.
}
\description{
A useful operationalisation of a lower limit L of a measurement method is
simply the solution of the equation
\deqn{L = k c(L)}{L = k * c(L)}
where c(L) is half of the lenght of the confidence interval at the limit L.
}
\examples{
data(din32645)
m <- lm(y ~ x, data = din32645)
lod(m)
}
\keyword{manip}
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