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author | ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4> | 2006-05-12 21:59:33 +0000 |
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committer | ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4> | 2006-05-12 21:59:33 +0000 |
commit | 69504b635d388507bce650c44b3bfe17cad3383e (patch) | |
tree | 120114ff6dc2d1aeb4716efef90d71257ac47501 /man/lod.Rd | |
parent | 6d118690c0cae02fc5cd4b28c1a67eecde4d9f60 (diff) |
- Fixed the inverse prediction
- Now I have a working approach for the calculation of LOD and LOQ,
but it seems to be different from what everybody else is doing
(e.g. Massart chaper 13). I like it, however. Maybe it even
yields a paper.
git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@8 5fad18fb-23f0-0310-ab10-e59a3bee62b4
Diffstat (limited to 'man/lod.Rd')
-rw-r--r-- | man/lod.Rd | 58 |
1 files changed, 58 insertions, 0 deletions
diff --git a/man/lod.Rd b/man/lod.Rd new file mode 100644 index 0000000..e6ce345 --- /dev/null +++ b/man/lod.Rd @@ -0,0 +1,58 @@ +\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} |