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Diffstat (limited to 'man/loq.Rd')
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@@ -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} |