From d8d6012e98fb4c7f158bcc7c173407c2b5f3e42e Mon Sep 17 00:00:00 2001 From: ranke Date: Sat, 22 Aug 2015 09:03:10 +0000 Subject: Get rid of the branched svn layout I never used git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@36 5fad18fb-23f0-0310-ab10-e59a3bee62b4 --- branches/0.1/chemCal/man/calplot.lm.Rd | 69 ---------------------- branches/0.1/chemCal/man/chemCal-package.Rd | 16 ------ branches/0.1/chemCal/man/din32645.Rd | 61 -------------------- branches/0.1/chemCal/man/inverse.predict.Rd | 69 ---------------------- branches/0.1/chemCal/man/lod.Rd | 88 ----------------------------- branches/0.1/chemCal/man/loq.Rd | 82 --------------------------- branches/0.1/chemCal/man/massart97ex1.Rd | 17 ------ branches/0.1/chemCal/man/massart97ex3.Rd | 51 ----------------- 8 files changed, 453 deletions(-) delete mode 100644 branches/0.1/chemCal/man/calplot.lm.Rd delete mode 100644 branches/0.1/chemCal/man/chemCal-package.Rd delete mode 100644 branches/0.1/chemCal/man/din32645.Rd delete mode 100644 branches/0.1/chemCal/man/inverse.predict.Rd delete mode 100644 branches/0.1/chemCal/man/lod.Rd delete mode 100644 branches/0.1/chemCal/man/loq.Rd delete mode 100644 branches/0.1/chemCal/man/massart97ex1.Rd delete mode 100644 branches/0.1/chemCal/man/massart97ex3.Rd (limited to 'branches/0.1/chemCal/man') diff --git a/branches/0.1/chemCal/man/calplot.lm.Rd b/branches/0.1/chemCal/man/calplot.lm.Rd deleted file mode 100644 index b60a032..0000000 --- a/branches/0.1/chemCal/man/calplot.lm.Rd +++ /dev/null @@ -1,69 +0,0 @@ -\name{calplot} -\alias{calplot} -\alias{calplot.default} -\alias{calplot.lm} -\title{Plot calibration graphs from univariate linear models} -\description{ - Produce graphics of calibration data, the fitted model as well - as confidence, and, for unweighted regression, prediction bands. -} -\usage{ - calplot(object, xlim = c("auto", "auto"), ylim = c("auto", "auto"), - xlab = "Concentration", ylab = "Response", alpha=0.05, varfunc = NULL) -} -\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}. - } - \item{xlim}{ - The limits of the plot on the x axis. - } - \item{ylim}{ - The limits of the plot on the y axis. - } - \item{xlab}{ - The label of the x axis. - } - \item{ylab}{ - The label of the y axis. - } - \item{alpha}{ - The error tolerance level for the confidence and prediction bands. Note that this - includes both tails of the Gaussian distribution, unlike the alpha and beta parameters - used in \code{\link{lod}} (see note below). - } - \item{varfunc}{ - The variance function for generating the weights in the model. - Currently, this argument is ignored (see note below). - } -} -\value{ - A plot of the calibration data, of your fitted model as well as lines showing - the confidence limits. Prediction limits are only shown for models from - unweighted regression. -} -\note{ - Prediction bands for models from weighted linear regression require weights - for the data, for which responses should be predicted. Prediction intervals - using weights e.g. from a variance function are currently not supported by - the internally used function \code{\link{predict.lm}}, therefore, - \code{calplot} does not draw prediction bands for such models. - - It is possible to compare the \code{\link{calplot}} prediction bands with the - \code{\link{lod}} values if the \code{lod()} alpha and beta parameters are - half the value of the \code{calplot()} alpha parameter. - -} -\examples{ -data(massart97ex3) -m <- lm(y ~ x, data = massart97ex3) -calplot(m) -} -\author{ - Johannes Ranke - \email{jranke@uni-bremen.de} - \url{http://www.uft.uni-bremen.de/chemie/ranke} -} -\keyword{regression} diff --git a/branches/0.1/chemCal/man/chemCal-package.Rd b/branches/0.1/chemCal/man/chemCal-package.Rd deleted file mode 100644 index fb09cb1..0000000 --- a/branches/0.1/chemCal/man/chemCal-package.Rd +++ /dev/null @@ -1,16 +0,0 @@ -\name{chemCal-package} -\alias{chemCal-package} -\docType{package} -\title{ - Calibration functions for analytical chemistry -} -\description{ - See \url{../DESCRIPTION} -} -\details{ - There is a package vignette located in \url{../doc/chemCal.pdf}. -} -\author{ - Author and Maintainer: Johannes Ranke -} -\keyword{manip} diff --git a/branches/0.1/chemCal/man/din32645.Rd b/branches/0.1/chemCal/man/din32645.Rd deleted file mode 100644 index cacbf07..0000000 --- a/branches/0.1/chemCal/man/din32645.Rd +++ /dev/null @@ -1,61 +0,0 @@ -\name{din32645} -\docType{data} -\alias{din32645} -\title{Calibration data from DIN 32645} -\description{ - Sample dataset to test the package. -} -\usage{data(din32645)} -\format{ - A dataframe containing 10 rows of x and y values. -} -\examples{ -data(din32645) -m <- lm(y ~ x, data = din32645) -calplot(m) - -## Prediction of x with confidence interval -(prediction <- inverse.predict(m, 3500, alpha = 0.01)) - -# This should give 0.07434 according to test data from Dintest, which -# was collected from Procontrol 3.1 (isomehr GmbH) in this case -round(prediction$Confidence,5) - -## Critical value: -(crit <- lod(m, alpha = 0.01, beta = 0.5)) - -# According to DIN 32645, we should get 0.07 for the critical value -# (decision limit, "Nachweisgrenze") -round(crit$x, 2) -# and according to Dintest test data, we should get 0.0698 from -round(crit$x, 4) - -## Limit of detection (smallest detectable value given alpha and beta) -# In German, the smallest detectable value is the "Erfassungsgrenze", and we -# should get 0.14 according to DIN, which we achieve by using the method -# described in it: -lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din") -round(lod.din$x, 2) - -## Limit of quantification -# This accords to the test data coming with the test data from Dintest again, -# except for the last digits of the value cited for Procontrol 3.1 (0.2121) -(loq <- loq(m, alpha = 0.01)) -round(loq$x,4) - -# A similar value is obtained using the approximation -# LQ = 3.04 * LC (Currie 1999, p. 120) -3.04 * lod(m,alpha = 0.01, beta = 0.5)$x -} -\references{ - DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994 - - Dintest. Plugin for MS Excel for evaluations of calibration data. Written - by Georg Schmitt, University of Heidelberg. - \url{http://www.rzuser.uni-heidelberg.de/~df6/download/dintest.htm} - - Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including - detection and quantification capabilities (IUPAC Recommendations 1995). - Analytica Chimica Acta 391, 105 - 126. -} -\keyword{datasets} diff --git a/branches/0.1/chemCal/man/inverse.predict.Rd b/branches/0.1/chemCal/man/inverse.predict.Rd deleted file mode 100644 index 347d670..0000000 --- a/branches/0.1/chemCal/man/inverse.predict.Rd +++ /dev/null @@ -1,69 +0,0 @@ -\name{inverse.predict} -\alias{inverse.predict} -\alias{inverse.predict.lm} -\alias{inverse.predict.rlm} -\alias{inverse.predict.default} -\title{Predict x from y for a linear calibration} -\usage{inverse.predict(object, newdata, \dots, - ws, alpha=0.05, var.s = "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}. - } - \item{newdata}{ - A vector of observed y values for one sample. - } - \item{\dots}{ - Placeholder for further arguments that might be needed by - future implementations. - } - \item{ws}{ - The weight attributed to the sample. This argument is obligatory - if \code{object} has weights. - } - \item{alpha}{ - The error tolerance level for the confidence interval to be reported. - } - \item{var.s}{ - The estimated variance of the sample measurements. The default is to take - the residual standard error from the calibration and to adjust it - using \code{ws}, if applicable. This means that \code{var.s} - overrides \code{ws}. - } -} -\value{ - A list containing the predicted x value, its standard error and a - confidence interval. -} -\description{ - This function predicts x values using a univariate linear model that has been - generated for the purpose of calibrating a measurement method. Prediction - intervals are given at the specified confidence level. - The calculation method was taken from Massart et al. (1997). In particular, - Equations 8.26 and 8.28 were combined in order to yield a general treatment - of inverse prediction for univariate linear models, taking into account - weights that have been used to create the linear model, and at the same - time providing the possibility to specify a precision in sample measurements - differing from the precision in standard samples used for the calibration. - This is elaborated in the package vignette. -} -\note{ - The function was validated with examples 7 and 8 from Massart et al. (1997). -} -\references{ - Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., - Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, - p. 200 -} -\examples{ -# This is example 7 from Chapter 8 in Massart et al. (1997) -data(massart97ex1) -m <- lm(y ~ x, data = massart97ex1) -inverse.predict(m, 15) # 6.1 +- 4.9 -inverse.predict(m, 90) # 43.9 +- 4.9 -inverse.predict(m, rep(90,5)) # 43.9 +- 3.2 -} -\keyword{manip} diff --git a/branches/0.1/chemCal/man/lod.Rd b/branches/0.1/chemCal/man/lod.Rd deleted file mode 100644 index 04aac1f..0000000 --- a/branches/0.1/chemCal/man/lod.Rd +++ /dev/null @@ -1,88 +0,0 @@ -\name{lod} -\alias{lod} -\alias{lod.lm} -\alias{lod.rlm} -\alias{lod.default} -\title{Estimate a limit of detection (LOD)} -\usage{ - lod(object, \dots, alpha = 0.05, beta = 0.05, method = "default", 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. - } - \item{\dots}{ - Placeholder for further arguments that might be needed by - future implementations. - } - \item{alpha}{ - The error tolerance for the decision limit (critical value). - } - \item{beta}{ - The error tolerance beta for the detection limit. - } - \item{method}{ - The \dQuote{default} method uses a prediction interval at the LOD - for the estimation of the LOD, which obviously requires - iteration. This is described for example in Massart, p. 432 ff. - The \dQuote{din} method uses the prediction interval at - x = 0 as an approximation. - } - \item{tol}{ - When the \dQuote{default} method is used, the default tolerance - for the LOD 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{ - A list containig the corresponding x and y values of the estimated limit of - detection of a model used for calibration. -} -\description{ - The decision limit (German: Nachweisgrenze) is defined as the signal or - analyte concentration that is significantly different from the blank signal - with a first order error alpha (one-sided significance test). - The detection limit, or more precise, the minimum detectable value - (German: Erfassungsgrenze), is then defined as the signal or analyte - concentration where the probability that the signal is not detected although - the analyte is present (type II or false negative error), is beta (also a - one-sided significance test). -} -\note{ - - The default values for alpha and beta are the ones recommended by IUPAC. - - The estimation of the LOD in terms of the analyte amount/concentration - xD from the LOD in the signal domain SD is done by simply inverting the - calibration function (i.e. assuming a known calibration function). - - The calculation of a LOD from weighted calibration models requires - a weights argument for the internally used \code{\link{predict.lm}} - function, which is currently not supported in R. -} -\references{ - Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., - Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, - Chapter 13.7.8 - - J. Inczedy, T. Lengyel, and A.M. Ure (2002) International Union of Pure and - Applied Chemistry Compendium of Analytical Nomenclature: Definitive Rules. - Web edition. - - Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including - detection and quantification capabilities (IUPAC Recommendations 1995). - Analytica Chimica Acta 391, 105 - 126. -} -\examples{ -data(din32645) -m <- lm(y ~ x, data = din32645) -lod(m) - -# The critical value (decision limit, German Nachweisgrenze) can be obtained -# by using beta = 0.5: -lod(m, alpha = 0.01, beta = 0.5) -} -\seealso{ - Examples for \code{\link{din32645}} -} -\keyword{manip} diff --git a/branches/0.1/chemCal/man/loq.Rd b/branches/0.1/chemCal/man/loq.Rd deleted file mode 100644 index 082cf34..0000000 --- a/branches/0.1/chemCal/man/loq.Rd +++ /dev/null @@ -1,82 +0,0 @@ -\name{loq} -\alias{loq} -\alias{loq.lm} -\alias{loq.rlm} -\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") -} -\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. - } -} -\value{ - 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. -} -\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. -} -\examples{ -data(massart97ex3) -attach(massart97ex3) -m <- lm(y ~ x) -loq(m) - -# We can get better by using replicate measurements -loq(m, n = 3) -} -\seealso{ - Examples for \code{\link{din32645}} -} -\keyword{manip} diff --git a/branches/0.1/chemCal/man/massart97ex1.Rd b/branches/0.1/chemCal/man/massart97ex1.Rd deleted file mode 100644 index 44e1b85..0000000 --- a/branches/0.1/chemCal/man/massart97ex1.Rd +++ /dev/null @@ -1,17 +0,0 @@ -\name{massart97ex1} -\docType{data} -\alias{massart97ex1} -\title{Calibration data from Massart et al. (1997), example 1} -\description{ - Sample dataset from p. 175 to test the package. -} -\usage{data(massart97ex1)} -\format{ - A dataframe containing 6 observations of x and y data. -} -\source{ - Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., - Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, - Chapter 8. -} -\keyword{datasets} diff --git a/branches/0.1/chemCal/man/massart97ex3.Rd b/branches/0.1/chemCal/man/massart97ex3.Rd deleted file mode 100644 index efdcf02..0000000 --- a/branches/0.1/chemCal/man/massart97ex3.Rd +++ /dev/null @@ -1,51 +0,0 @@ -\name{massart97ex3} -\docType{data} -\alias{massart97ex3} -\title{Calibration data from Massart et al. (1997), example 3} -\description{ - Sample dataset from p. 188 to test the package. -} -\usage{data(massart97ex3)} -\format{ - A dataframe containing 6 levels of x values with 5 - observations of y for each level. -} -\examples{ -data(massart97ex3) -attach(massart97ex3) -yx <- split(y, x) -ybar <- sapply(yx, mean) -s <- round(sapply(yx, sd), digits = 2) -w <- round(1 / (s^2), digits = 3) -weights <- w[factor(x)] -m <- lm(y ~ x, w = weights) -calplot(m) - -# The following concords with the book p. 200 -inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5 -inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9 - -# The LOD is only calculated for models from unweighted regression -# with this version of chemCal -m0 <- lm(y ~ x) -lod(m0) - -# Limit of quantification from unweighted regression -loq(m0) - -# For calculating the limit of quantification from a model from weighted -# regression, we need to supply weights, internally used for inverse.predict -# If we are not using a variance function, we can use the weight from -# the above example as a first approximation (x = 15 is close to our -# loq approx 14 from above). -loq(m, w.loq = 1.67) -# The weight for the loq should therefore be derived at x = 7.3 instead -# of 15, but the graphical procedure of Massart (p. 201) to derive the -# variances on which the weights are based is quite inaccurate anyway. -} -\source{ - Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., - Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, - Chapter 8. -} -\keyword{datasets} -- cgit v1.2.1