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authorJohannes Ranke <jranke@uni-bremen.de>2022-03-31 19:21:03 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2022-03-31 19:59:10 +0200
commit08465d77a6ca5a9656ac86047c6008f1e7f3e9c7 (patch)
treef27a775e146748881eb6526ed57298f4bdc40c2f /man/loq.Rd
parentf4fcef8228ebd5a1a73bc6edc47b5efa259c2e20 (diff)
Fix URLs in README, convert to roxygenv0.2.3
- The roxygen conversion was done using Rd2roxygen - Also edit _pkgdown.yml to group the reference - Use markdown bullet lists for lod and loq docs
Diffstat (limited to 'man/loq.Rd')
-rw-r--r--man/loq.Rd110
1 files changed, 55 insertions, 55 deletions
diff --git a/man/loq.Rd b/man/loq.Rd
index c247f34..390d3a8 100644
--- a/man/loq.Rd
+++ b/man/loq.Rd
@@ -1,3 +1,5 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/loq.R
\name{loq}
\alias{loq}
\alias{loq.lm}
@@ -5,76 +7,74 @@
\alias{loq.default}
\title{Estimate a limit of quantification (LOQ)}
\usage{
- loq(object, \dots, alpha = 0.05, k = 3, n = 1, w.loq = "auto",
- var.loq = "auto", tol = "default")
+loq(
+ object,
+ ...,
+ alpha = 0.05,
+ k = 3,
+ n = 1,
+ w.loq = "auto",
+ var.loq = "auto",
+ tol = "default"
+)
}
\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. If weights are specified
- in the model, either \code{w.loq} or \code{var.loq} have to
- be specified.
- }
- \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 LOQ.
- }
- \item{n}{
- The number of replicate measurements for which the LOQ should be
- specified.
- }
- \item{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 \code{\link{massart97ex3}} for
- an example how to take advantage of knowledge about the
- variance function.
- }
- \item{var.loq}{
- The approximate variance at the LOQ. The default value is
- calculated from the model.
- }
- \item{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.
- }
+\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. If weights are specified in the
+model, either \code{w.loq} or \code{var.loq} have to be specified.}
+
+\item{\dots}{Placeholder for further arguments that might be needed by
+future implementations.}
+
+\item{alpha}{The error tolerance for the prediction of x values in the
+calculation.}
+
+\item{k}{The inverse of the maximum relative error tolerated at the desired
+LOQ.}
+
+\item{n}{The number of replicate measurements for which the LOQ should be
+specified.}
+
+\item{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 \code{\link{massart97ex3}} for an example how to take
+advantage of knowledge about the variance function.}
+
+\item{var.loq}{The approximate variance at the LOQ. The default value is
+calculated from the model.}
+
+\item{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.
+The estimated limit of quantification for a model used for
+calibration.
}
\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
- \deqn{L = k c(L)}{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
- \code{\link{inverse.predict}}, and L is obtained by iteration.
+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 \deqn{L = k c(L)}{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
+\code{\link{inverse.predict}}, and L is obtained by iteration.
}
\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.
+* 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{
+
m <- lm(y ~ x, data = massart97ex1)
loq(m)
# We can get better by using replicate measurements
loq(m, n = 3)
+
}
\seealso{
- Examples for \code{\link{din32645}}
+Examples for \code{\link{din32645}}
}
-\keyword{manip}

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