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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}