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-rw-r--r--trunk/chemCal/DESCRIPTION19
-rw-r--r--trunk/chemCal/INDEX9
-rw-r--r--trunk/chemCal/R/calfunctions.R2
-rw-r--r--trunk/chemCal/R/calplot.R80
-rw-r--r--trunk/chemCal/R/inverse.predict.lm.R115
-rw-r--r--trunk/chemCal/R/lod.R53
-rw-r--r--trunk/chemCal/R/loq.R40
-rw-r--r--trunk/chemCal/TODO5
-rw-r--r--trunk/chemCal/data/din32645.rdabin453 -> 0 bytes
-rw-r--r--trunk/chemCal/data/massart97ex1.rdabin193 -> 0 bytes
-rw-r--r--trunk/chemCal/data/massart97ex3.rdabin296 -> 0 bytes
-rw-r--r--trunk/chemCal/man/calplot.lm.Rd69
-rw-r--r--trunk/chemCal/man/chemCal-package.Rd16
-rw-r--r--trunk/chemCal/man/din32645.Rd61
-rw-r--r--trunk/chemCal/man/inverse.predict.Rd69
-rw-r--r--trunk/chemCal/man/ipowfunc.Rd33
-rw-r--r--trunk/chemCal/man/lod.Rd83
-rw-r--r--trunk/chemCal/man/loq.Rd77
-rw-r--r--trunk/chemCal/man/massart97ex1.Rd17
-rw-r--r--trunk/chemCal/man/massart97ex3.Rd51
-rw-r--r--trunk/chemCal/man/powfunc.Rd32
-rw-r--r--trunk/chemCal/tests/din32645.R7
-rw-r--r--trunk/chemCal/tests/din32645.Rout.save45
-rw-r--r--trunk/chemCal/tests/massart97.R25
-rw-r--r--trunk/chemCal/tests/massart97.Rout.save123
-rw-r--r--trunk/chemCal/vignettes/Makefile33
-rw-r--r--trunk/chemCal/vignettes/Rplots.pdfbin28827 -> 0 bytes
-rw-r--r--trunk/chemCal/vignettes/Rplots.ps1970
-rw-r--r--trunk/chemCal/vignettes/chemCal-001.eps1762
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-rw-r--r--trunk/chemCal/vignettes/chemCal.blg46
-rw-r--r--trunk/chemCal/vignettes/chemCal.log399
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diff --git a/trunk/chemCal/DESCRIPTION b/trunk/chemCal/DESCRIPTION
deleted file mode 100644
index a4c8e82..0000000
--- a/trunk/chemCal/DESCRIPTION
+++ /dev/null
@@ -1,19 +0,0 @@
-Package: chemCal
-Version: 0.2-02
-Date: 2012-09-11
-Title: Calibration functions for analytical chemistry
-Author: Johannes Ranke <jranke@uni-bremen.de>
-Maintainer: Johannes Ranke <jranke@uni-bremen.de>
-Depends: R (>= 2.4.0)
-Suggests: MASS
-Description: chemCal provides simple functions for plotting linear
- calibration functions and estimating standard errors for measurements
- according to the Handbook of Chemometrics and Qualimetrics: Part A
- by Massart et al. There are also functions estimating the limit
- of detection (LOD) and limit of quantification (LOQ).
- The functions work on model objects from - optionally weighted - linear
- regression (lm) or robust linear regression (rlm from the MASS package).
-License: GPL version 2 or newer
-URL: http://www.r-project.org,
- http://www.uft.uni-bremen.de/chemie/ranke/?page=chemCal,
- http://kriemhild.uft.uni-bremen.de/viewcvs/?root=chemCal
diff --git a/trunk/chemCal/INDEX b/trunk/chemCal/INDEX
deleted file mode 100644
index f880eb6..0000000
--- a/trunk/chemCal/INDEX
+++ /dev/null
@@ -1,9 +0,0 @@
-calplot Plot calibration graphs from univariate linear
- models
-chemCal-package Calibration functions for analytical chemistry
-din32645 Calibration data from DIN 32645
-inverse.predict Predict x from y for a linear calibration
-lod Estimate a limit of detection (LOD)
-loq Estimate a limit of quantification (LOQ)
-massart97ex3 Calibration data from Massart et al. (1997),
- example 3
diff --git a/trunk/chemCal/R/calfunctions.R b/trunk/chemCal/R/calfunctions.R
deleted file mode 100644
index 6ce29f7..0000000
--- a/trunk/chemCal/R/calfunctions.R
+++ /dev/null
@@ -1,2 +0,0 @@
-powfunc <- function(x,a,b) a * x^b
-ipowfunc <- function(y,a,b) (y/a)^1/b
diff --git a/trunk/chemCal/R/calplot.R b/trunk/chemCal/R/calplot.R
deleted file mode 100644
index 6aed9c0..0000000
--- a/trunk/chemCal/R/calplot.R
+++ /dev/null
@@ -1,80 +0,0 @@
-calplot <- function(object,
- xlim = c("auto", "auto"), ylim = c("auto", "auto"),
- xlab = "Concentration", ylab = "Response", alpha = 0.05,
- varfunc = NULL)
-{
- UseMethod("calplot")
-}
-
-calplot.default <- function(object,
- xlim = c("auto","auto"), ylim = c("auto","auto"),
- xlab = "Concentration", ylab = "Response",
- alpha=0.05, varfunc = NULL)
-{
- stop("Calibration plots only implemented for univariate lm objects.")
-}
-
-calplot.lm <- function(object,
- xlim = c("auto","auto"), ylim = c("auto","auto"),
- xlab = "Concentration", ylab = "Response", alpha=0.05,
- varfunc = NULL)
-{
- if (length(object$coef) > 2)
- stop("More than one independent variable in your model - not implemented")
-
- if (alpha <= 0 | alpha >= 1)
- stop("Alpha should be between 0 and 1 (exclusive)")
-
- m <- object
- level <- 1 - alpha
- y <- m$model[[1]]
- x <- m$model[[2]]
- if (xlim[1] == "auto") xlim[1] <- 0
- if (xlim[2] == "auto") xlim[2] <- max(x)
- xlim <- as.numeric(xlim)
- newdata <- list(
- x = seq(from = xlim[[1]], to = xlim[[2]], length=250))
- names(newdata) <- names(m$model)[[2]]
- if (is.null(varfunc)) {
- varfunc <- if (length(m$weights)) {
- function(variable) mean(m$weights)
- } else function(variable) rep(1,250)
- }
- pred.lim <- predict(m, newdata, interval = "prediction",
- level=level, weights.newdata = varfunc(m))
- conf.lim <- predict(m, newdata, interval = "confidence",
- level=level)
- yrange.auto <- range(c(0,pred.lim))
- if (ylim[1] == "auto") ylim[1] <- yrange.auto[1]
- if (ylim[2] == "auto") ylim[2] <- yrange.auto[2]
- plot(1,
- type = "n",
- xlab = xlab,
- ylab = ylab,
- xlim = as.numeric(xlim),
- ylim = as.numeric(ylim)
- )
- points(x,y, pch = 21, bg = "yellow")
- matlines(newdata[[1]], pred.lim, lty = c(1, 4, 4),
- col = c("black", "red", "red"))
- if (length(object$weights) > 0) {
- legend(min(x),
- max(pred.lim, na.rm = TRUE),
- legend = c("Fitted Line", "Confidence Bands"),
- lty = c(1, 3),
- lwd = 2,
- col = c("black", "green4"),
- horiz = FALSE, cex = 0.9, bg = "gray95")
- } else {
- matlines(newdata[[1]], conf.lim, lty = c(1, 3, 3),
- col = c("black", "green4", "green4"))
- legend(min(x),
- max(pred.lim, na.rm = TRUE),
- legend = c("Fitted Line", "Confidence Bands",
- "Prediction Bands"),
- lty = c(1, 3, 4),
- lwd = 2,
- col = c("black", "green4", "red"),
- horiz = FALSE, cex = 0.9, bg = "gray95")
- }
-}
diff --git a/trunk/chemCal/R/inverse.predict.lm.R b/trunk/chemCal/R/inverse.predict.lm.R
deleted file mode 100644
index d57275c..0000000
--- a/trunk/chemCal/R/inverse.predict.lm.R
+++ /dev/null
@@ -1,115 +0,0 @@
-# This is an implementation of Equation (8.28) in the Handbook of Chemometrics
-# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
-# Example 8 on the same page, extended as specified in the package vignette
-
-inverse.predict <- function(object, newdata, ...,
- ws = "auto", alpha = 0.05, var.s = "auto")
-{
- UseMethod("inverse.predict")
-}
-
-inverse.predict.default <- function(object, newdata, ...,
- ws = "auto", alpha = 0.05, var.s = "auto")
-{
- stop("Inverse prediction only implemented for univariate lm and nls objects.")
-}
-
-inverse.predict.lm <- function(object, newdata, ...,
- ws = "auto", alpha = 0.05, var.s = "auto")
-{
- yname = names(object$model)[[1]]
- xname = names(object$model)[[2]]
- if (ws == "auto") {
- ws <- ifelse(length(object$weights) > 0, mean(object$weights), 1)
- }
- if (length(object$weights) > 0) {
- wx <- split(object$weights,object$model[[xname]])
- w <- sapply(wx,mean)
- } else {
- w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
- }
- .inverse.predict(object = object, newdata = newdata,
- ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
-}
-
-inverse.predict.rlm <- function(object, newdata, ...,
- ws = "auto", alpha = 0.05, var.s = "auto")
-{
- yname = names(object$model)[[1]]
- xname = names(object$model)[[2]]
- if (ws == "auto") {
- ws <- mean(object$w)
- }
- wx <- split(object$weights,object$model[[xname]])
- w <- sapply(wx,mean)
- .inverse.predict(object = object, newdata = newdata,
- ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
-}
-
-inverse.predict.nls <- function(object, newdata, ...,
- ws = "auto", alpha = 0.05, var.s = "auto")
-{
- yname = names(object$model)[[1]]
- xname = names(object$model)[[2]]
- if (ws == "auto") {
- ws <- ifelse(length(object$weights) > 0, mean(object$weights), 1)
- }
- if (length(object$weights) > 0) {
- wx <- split(object$weights,object$model[[xname]])
- w <- sapply(wx,mean)
- } else {
- w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
- }
- if (length(object$coef) > 2)
- stop("More than one independent variable in your model - not implemented")
-}
-
-.inverse.predict <- function(object, newdata, ws, alpha, var.s, w, xname, yname)
-{
- if (length(object$coef) > 2)
- stop("More than one independent variable in your model - not implemented")
-
- if (alpha <= 0 | alpha >= 1)
- stop("Alpha should be between 0 and 1 (exclusive)")
-
- ybars <- mean(newdata)
- m <- length(newdata)
-
- yx <- split(object$model[[yname]], object$model[[xname]])
- n <- length(yx)
- df <- n - length(object$coef)
- x <- as.numeric(names(yx))
- ybar <- sapply(yx,mean)
- yhatx <- split(object$fitted.values, object$model[[xname]])
- yhat <- sapply(yhatx, mean)
- se <- sqrt(sum(w * (ybar - yhat)^2)/df)
-
- if (var.s == "auto") {
- var.s <- se^2/ws
- }
-
- b1 <- object$coef[[xname]]
-
- ybarw <- sum(w * ybar)/sum(w)
-
-# This is the adapted form of equation 8.28 (see package vignette)
- sxhats <- 1/b1 * sqrt(
- (var.s / m) +
- se^2 * (1/sum(w) +
- (ybars - ybarw)^2 * sum(w) /
- (b1^2 * (sum(w) * sum(w * x^2) - sum(w * x)^2)))
- )
-
- if (names(object$coef)[1] == "(Intercept)") {
- b0 <- object$coef[["(Intercept)"]]
- } else {
- b0 <- 0
- }
-
- xs <- (ybars - b0) / b1
- t <- qt(1-0.5*alpha, n - 2)
- conf <- t * sxhats
- result <- list("Prediction"=xs,"Standard Error"=sxhats,
- "Confidence"=conf, "Confidence Limits"=c(xs - conf, xs + conf))
- return(result)
-}
diff --git a/trunk/chemCal/R/lod.R b/trunk/chemCal/R/lod.R
deleted file mode 100644
index f5bbb7d..0000000
--- a/trunk/chemCal/R/lod.R
+++ /dev/null
@@ -1,53 +0,0 @@
-lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
-{
- UseMethod("lod")
-}
-
-lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
-{
- stop("lod is only implemented for univariate lm objects.")
-}
-
-lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
-{
- if (length(object$weights) > 0) {
- stop(paste(
- "\nThe detemination of a lod from calibration models obtained by",
- "weighted linear regression requires confidence intervals for",
- "predicted y values taking into account weights for the x values",
- "from which the predictions are to be generated.",
- "This is not supported by the internally used predict.lm method.",
- sep = "\n"
- ))
- }
- xname <- names(object$model)[[2]]
- yname <- names(object$model)[[1]]
- newdata <- data.frame(0)
- names(newdata) <- xname
- y0 <- predict(object, newdata, interval = "prediction",
- level = 1 - 2 * alpha)
- yc <- y0[[1,"upr"]]
- if (method == "din") {
- y0.d <- predict(object, newdata, interval = "prediction",
- level = 1 - 2 * beta)
- deltay <- y0.d[[1, "upr"]] - y0.d[[1, "fit"]]
- lod.y <- yc + deltay
- lod.x <- inverse.predict(object, lod.y)$Prediction
- } else {
- f <- function(x) {
- newdata <- data.frame(x)
- names(newdata) <- xname
- pi.y <- predict(object, newdata, interval = "prediction",
- level = 1 - 2 * beta)
- yd <- pi.y[[1,"lwr"]]
- (yd - yc)^2
- }
- lod.x <- optimize(f,interval=c(0,max(object$model[[xname]])))$minimum
- newdata <- data.frame(x = lod.x)
- names(newdata) <- xname
- lod.y <- predict(object, newdata)
- }
- lod <- list(lod.x, lod.y)
- names(lod) <- c(xname, yname)
- return(lod)
-}
diff --git a/trunk/chemCal/R/loq.R b/trunk/chemCal/R/loq.R
deleted file mode 100644
index 5776096..0000000
--- a/trunk/chemCal/R/loq.R
+++ /dev/null
@@ -1,40 +0,0 @@
-loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
- var.loq = "auto")
-{
- UseMethod("loq")
-}
-
-loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
- var.loq = "auto")
-{
- stop("loq is only implemented for univariate lm objects.")
-}
-
-loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
- var.loq = "auto")
-{
- if (length(object$weights) > 0 && var.loq == "auto" && w.loq == "auto") {
- stop(paste("If you are using a model from weighted regression,",
- "you need to specify a reasonable approximation for the",
- "weight (w.loq) or the variance (var.loq) at the",
- "limit of quantification"))
- }
- xname <- names(object$model)[[2]]
- yname <- names(object$model)[[1]]
- f <- function(x) {
- newdata <- data.frame(x = x)
- names(newdata) <- xname
- y <- predict(object, newdata)
- p <- inverse.predict(object, rep(y, n), ws = w.loq,
- var.s = var.loq, alpha = alpha)
- (p[["Prediction"]] - k * p[["Confidence"]])^2
- }
- tmp <- optimize(f,interval=c(0,max(object$model[[2]])))
- loq.x <- tmp$minimum
- newdata <- data.frame(x = loq.x)
- names(newdata) <- xname
- loq.y <- predict(object, newdata)
- loq <- list(loq.x, loq.y)
- names(loq) <- c(xname, yname)
- return(loq)
-}
diff --git a/trunk/chemCal/TODO b/trunk/chemCal/TODO
deleted file mode 100644
index 1fc44a7..0000000
--- a/trunk/chemCal/TODO
+++ /dev/null
@@ -1,5 +0,0 @@
-- lod(): At the moment, it is not possible to calculate an lod for the
- case of more than one replicates (m is fixed to 1). The formula
- for the prediction of y from mean(x) in Massart et al, p. 433
- could be used to generalize this.
-- Write methods for nonlinear calibration functions
diff --git a/trunk/chemCal/data/din32645.rda b/trunk/chemCal/data/din32645.rda
deleted file mode 100644
index 4e2913a..0000000
--- a/trunk/chemCal/data/din32645.rda
+++ /dev/null
Binary files differ
diff --git a/trunk/chemCal/data/massart97ex1.rda b/trunk/chemCal/data/massart97ex1.rda
deleted file mode 100644
index 1a6fd4b..0000000
--- a/trunk/chemCal/data/massart97ex1.rda
+++ /dev/null
Binary files differ
diff --git a/trunk/chemCal/data/massart97ex3.rda b/trunk/chemCal/data/massart97ex3.rda
deleted file mode 100644
index 7f60f3a..0000000
--- a/trunk/chemCal/data/massart97ex3.rda
+++ /dev/null
Binary files differ
diff --git a/trunk/chemCal/man/calplot.lm.Rd b/trunk/chemCal/man/calplot.lm.Rd
deleted file mode 100644
index b60a032..0000000
--- a/trunk/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/trunk/chemCal/man/chemCal-package.Rd b/trunk/chemCal/man/chemCal-package.Rd
deleted file mode 100644
index fb09cb1..0000000
--- a/trunk/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 <jranke@uni-bremen.de>
-}
-\keyword{manip}
diff --git a/trunk/chemCal/man/din32645.Rd b/trunk/chemCal/man/din32645.Rd
deleted file mode 100644
index cacbf07..0000000
--- a/trunk/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/trunk/chemCal/man/inverse.predict.Rd b/trunk/chemCal/man/inverse.predict.Rd
deleted file mode 100644
index 347d670..0000000
--- a/trunk/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/trunk/chemCal/man/ipowfunc.Rd b/trunk/chemCal/man/ipowfunc.Rd
deleted file mode 100644
index e09e590..0000000
--- a/trunk/chemCal/man/ipowfunc.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-\name{ipowfunc}
-\alias{ipowfunc}
-\title{Power function}
-\description{
- Inverse of the arithmetic power function \code{\link{powfunc}} used for
- modelling univariate nonlinear calibration data. }
-\usage{
- ipowfunc(x,a,b)
-}
-\arguments{
- \item{x}{
- Independent variable}
- \item{a}{
- Coefficient}
- \item{b}{
- Exponent}
-}
-\value{
- The result of evaluating the function
- \deqn{f(x) = \frac{y}{a}^\frac{1}{b}}{f(x) = y/a^1/b}
- which is the inverse of the function defined by \code{\link{powfunc}}
-}
-\author{
- Johannes Ranke
- \email{jranke@uni-bremen.de}
- \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\seealso{
- The original function \code{\link{powfunc}}.
-}
-\keyword{models}
-\keyword{regression}
-\keyword{nonlinear}
diff --git a/trunk/chemCal/man/lod.Rd b/trunk/chemCal/man/lod.Rd
deleted file mode 100644
index e468e1d..0000000
--- a/trunk/chemCal/man/lod.Rd
+++ /dev/null
@@ -1,83 +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")
-}
-\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 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.
- }
-}
-\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/trunk/chemCal/man/loq.Rd b/trunk/chemCal/man/loq.Rd
deleted file mode 100644
index 7541e77..0000000
--- a/trunk/chemCal/man/loq.Rd
+++ /dev/null
@@ -1,77 +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")
-}
-\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.
- }
-}
-\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/trunk/chemCal/man/massart97ex1.Rd b/trunk/chemCal/man/massart97ex1.Rd
deleted file mode 100644
index 44e1b85..0000000
--- a/trunk/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/trunk/chemCal/man/massart97ex3.Rd b/trunk/chemCal/man/massart97ex3.Rd
deleted file mode 100644
index efdcf02..0000000
--- a/trunk/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}
diff --git a/trunk/chemCal/man/powfunc.Rd b/trunk/chemCal/man/powfunc.Rd
deleted file mode 100644
index 73fe3b0..0000000
--- a/trunk/chemCal/man/powfunc.Rd
+++ /dev/null
@@ -1,32 +0,0 @@
-\name{powfunc}
-\alias{powfunc}
-\title{Power function}
-\description{
- Arithmetic power function for modelling univariate nonlinear calibration data.
-}
-\usage{
- powfunc(x,a,b)
-}
-\arguments{
- \item{x}{
- Independent variable}
- \item{a}{
- Coefficient}
- \item{b}{
- Exponent}
-}
-\value{
- The result of evaluating the function
- \deqn{f(x) = a x^b}{f(x) = a * x^b}
-}
-\author{
- Johannes Ranke
- \email{jranke@uni-bremen.de}
- \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\seealso{
- The inverse of this function \code{\link{ipowfunc}}.
-}
-\keyword{models}
-\keyword{regression}
-\keyword{nonlinear}
diff --git a/trunk/chemCal/tests/din32645.R b/trunk/chemCal/tests/din32645.R
deleted file mode 100644
index e5ffed7..0000000
--- a/trunk/chemCal/tests/din32645.R
+++ /dev/null
@@ -1,7 +0,0 @@
-require(chemCal)
-data(din32645)
-m <- lm(y ~ x, data = din32645)
-inverse.predict(m, 3500, alpha = 0.01)
-lod <- lod(m, alpha = 0.01, beta = 0.5)
-lod(m, alpha = 0.01, beta = 0.01)
-loq <- loq(m, alpha = 0.01)
diff --git a/trunk/chemCal/tests/din32645.Rout.save b/trunk/chemCal/tests/din32645.Rout.save
deleted file mode 100644
index c5ed5a7..0000000
--- a/trunk/chemCal/tests/din32645.Rout.save
+++ /dev/null
@@ -1,45 +0,0 @@
-
-R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.1 (2006-06-01)
-ISBN 3-900051-07-0
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> require(chemCal)
-Loading required package: chemCal
-[1] TRUE
-> data(din32645)
-> m <- lm(y ~ x, data = din32645)
-> inverse.predict(m, 3500, alpha = 0.01)
-$Prediction
-[1] 0.1054792
-
-$`Standard Error`
-[1] 0.02215619
-
-$Confidence
-[1] 0.07434261
-
-$`Confidence Limits`
-[1] 0.03113656 0.17982178
-
-> lod <- lod(m, alpha = 0.01, beta = 0.5)
-> lod(m, alpha = 0.01, beta = 0.01)
-$x
-[1] 0.132904
-
-$y
-[1] 3764.977
-
-> loq <- loq(m, alpha = 0.01)
->
diff --git a/trunk/chemCal/tests/massart97.R b/trunk/chemCal/tests/massart97.R
deleted file mode 100644
index 00f837f..0000000
--- a/trunk/chemCal/tests/massart97.R
+++ /dev/null
@@ -1,25 +0,0 @@
-require(chemCal)
-data(massart97ex1)
-m <- lm(y ~ x, data = massart97ex1)
-inverse.predict(m, 15) # 6.1 +- 4.9
-inverse.predict(m, 90) # 43.9 +- 4.9
-inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-
-data(massart97ex3)
-attach(massart97ex3)
-yx <- split(y, x)
-ybar <- sapply(yx, mean)
-s <- round(sapply(yx, sd), digits = 2)
-w <- round(1 / (s^2), digits = 3)
-weights <- w[factor(x)]
-m <- lm(y ~ x, w = weights)
-#calplot(m)
-
-inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
-inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-
-m0 <- lm(y ~ x)
-lod(m0)
-
-loq(m0)
-loq(m, w.loq = 1.67)
diff --git a/trunk/chemCal/tests/massart97.Rout.save b/trunk/chemCal/tests/massart97.Rout.save
deleted file mode 100644
index cb113d0..0000000
--- a/trunk/chemCal/tests/massart97.Rout.save
+++ /dev/null
@@ -1,123 +0,0 @@
-
-R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.1 (2006-06-01)
-ISBN 3-900051-07-0
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> require(chemCal)
-Loading required package: chemCal
-[1] TRUE
-> data(massart97ex1)
-> m <- lm(y ~ x, data = massart97ex1)
-> inverse.predict(m, 15) # 6.1 +- 4.9
-$Prediction
-[1] 6.09381
-
-$`Standard Error`
-[1] 1.767278
-
-$Confidence
-[1] 4.906751
-
-$`Confidence Limits`
-[1] 1.187059 11.000561
-
-> inverse.predict(m, 90) # 43.9 +- 4.9
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.767747
-
-$Confidence
-[1] 4.908053
-
-$`Confidence Limits`
-[1] 39.03178 48.84788
-
-> inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.141204
-
-$Confidence
-[1] 3.168489
-
-$`Confidence Limits`
-[1] 40.77134 47.10832
-
->
-> data(massart97ex3)
-> attach(massart97ex3)
-> yx <- split(y, x)
-> ybar <- sapply(yx, mean)
-> s <- round(sapply(yx, sd), digits = 2)
-> w <- round(1 / (s^2), digits = 3)
-> weights <- w[factor(x)]
-> m <- lm(y ~ x, w = weights)
-> #calplot(m)
->
-> inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
-$Prediction
-[1] 5.865367
-
-$`Standard Error`
-[1] 0.892611
-
-$Confidence
-[1] 2.478285
-
-$`Confidence Limits`
-[1] 3.387082 8.343652
-
-> inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-$Prediction
-[1] 44.06025
-
-$`Standard Error`
-[1] 2.829162
-
-$Confidence
-[1] 7.855012
-
-$`Confidence Limits`
-[1] 36.20523 51.91526
-
->
-> m0 <- lm(y ~ x)
-> lod(m0)
-$x
-[1] 5.406637
-
-$y
-[1] 13.63822
-
->
-> loq(m0)
-$x
-[1] 13.97767
-
-$y
-[1] 30.62355
-
-> loq(m, w.loq = 1.67)
-$x
-[1] 7.346231
-
-$y
-[1] 17.90784
-
->
diff --git a/trunk/chemCal/vignettes/Makefile b/trunk/chemCal/vignettes/Makefile
deleted file mode 100644
index 8eca69e..0000000
--- a/trunk/chemCal/vignettes/Makefile
+++ /dev/null
@@ -1,33 +0,0 @@
-# Makefile for Sweave documents containing both Latex and R code
-# Author: Johannes Ranke <jranke@uni-bremen.de>
-# Last Change: 2006 Jun 23
-# based on the Makefile of Nicholas Lewin-Koh
-# in turn based on work of Rouben Rostmaian
-# SVN: $Id: Makefile.rnoweb 62 2006-05-24 08:30:59Z ranke $
-
-RNWFILES = $(wildcard *.Rnw)
-TARGETS = $(patsubst %.Rnw,%.tex,$(RNWFILE)) $(patsubst %.Rnw,%.pdf,$(RNWFILES))
-
-RERUN = "(There were undefined references|Rerun to get (cross-references|the bars) right|Table widths have changed. Rerun LaTeX.|Linenumber reference failed)"
-RERUNBIB = "No file.*\.bbl|Citation.*undefined"
-
-%.tex: %.Rnw
- echo 'Sweave("$<")' | R --no-save --no-restore
-
-%.pdf: %.tex
- pdflatex $<
- egrep -c $(RERUNBIB) $*.log && (bibtex $*;pdflatex $<); true
- egrep $(RERUN) $*.log && (pdflatex $<) ; true
- egrep $(RERUN) $*.log && (pdflatex $<) ; true
-
-all: all-recursive $(TARGETS)
-
-clean: clean-recursive
- rm -f *.aux *.log *.bbl *.blg *.brf *.cb *.ind *.idx *.ilg \
- *.inx *.ps *.dvi *.toc *.out *.lot *~ *.lof *.ttt *.fff
-
-all-recursive:
- for dir in $(wildcard *); do if [ -d $$dir ] && [ -f $$dir/Makefile ]; then cd $$dir; $(MAKE) all; cd ..; fi; done
-
-clean-recursive:
- for dir in $(wildcard *); do if [ -d $$dir ] && [ -f $$dir/Makefile ]; then cd $$dir; $(MAKE) clean; cd ..; fi; done
diff --git a/trunk/chemCal/vignettes/Rplots.pdf b/trunk/chemCal/vignettes/Rplots.pdf
deleted file mode 100644
index 4029222..0000000
--- a/trunk/chemCal/vignettes/Rplots.pdf
+++ /dev/null
Binary files differ
diff --git a/trunk/chemCal/vignettes/Rplots.ps b/trunk/chemCal/vignettes/Rplots.ps
deleted file mode 100644
index c5e68ec..0000000
--- a/trunk/chemCal/vignettes/Rplots.ps
+++ /dev/null
@@ -1,1970 +0,0 @@
-%!PS-Adobe-3.0
-%%DocumentNeededResources: font Helvetica
-%%+ font Helvetica-Bold
-%%+ font Helvetica-Oblique
-%%+ font Helvetica-BoldOblique
-%%+ font Symbol
-%%DocumentMedia: a4 595 841 0 () ()
-%%Title: R Graphics Output
-%%Creator: R Software
-%%Pages: (atend)
-%%Orientation: Landscape
-%%BoundingBox: 18 18 577 824
-%%EndComments
-%%BeginProlog
-/bp { gs 595.00 0 translate 90 rotate gs } def
-% begin .ps.prolog
-/gs { gsave } def
-/gr { grestore } def
-/ep { showpage gr gr } def
-/m { moveto } def
-/l { rlineto } def
-/np { newpath } def
-/cp { closepath } def
-/f { fill } def
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diff --git a/trunk/chemCal/vignettes/chemCal-002.pdf b/trunk/chemCal/vignettes/chemCal-002.pdf
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-\documentclass[a4paper]{article}
-%\VignetteIndexEntry{Short manual for the chemCal package}
-\usepackage{hyperref}
-
-\title{Basic calibration functions for analytical chemistry}
-\author{Johannes Ranke}
-
-\begin{document}
-\maketitle
-
-The \texttt{chemCal} package was first designed in the course of a lecture and lab
-course on "analytics of organic trace contaminants" at the University of Bremen
-from October to December 2004. In the fall 2005, an email exchange with
-Ron Wehrens led to the belief that it would be desirable to implement the
-inverse prediction method given in \cite{massart97} since it also covers the
-case of weighted regression. Studies of the IUPAC orange book and of DIN 32645
-as well as publications by Currie and the Analytical Method Committee of the
-Royal Society of Chemistry and a nice paper by Castillo and Castells provided
-further understanding of the matter.
-
-At the moment, the package consists of four functions, working on univariate
-linear models of class \texttt{lm} or \texttt{rlm}, plus to datasets for
-validation.
-
-A \href{http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=8877}{bug
-report (PR\#8877)} and the following e-mail exchange on the r-devel mailing list about
-prediction intervals from weighted regression entailed some further studies
-on this subject. However, I did not encounter any proof or explanation of the
-formula cited below yet, so I can't really confirm that Massart's method is correct.
-
-When calibrating an analytical method, the first task is to generate a suitable
-model. If we want to use the \texttt{chemCal} functions, we will have to
-restrict ourselves to univariate, possibly weighted, linear regression so far.
-
-Once such a model has been created, the calibration can be graphically
-shown by using the \texttt{calplot} function:
-
-<<echo=TRUE,fig=TRUE>>=
-library(chemCal)
-data(massart97ex3)
-m0 <- lm(y ~ x, data = massart97ex3)
-calplot(m0)
-@
-
-As we can see, the scatter increases with increasing x. This is also
-illustrated by one of the diagnostic plots for linear models
-provided by R:
-
-<<echo=TRUE,fig=TRUE>>=
-plot(m0,which=3)
-@
-
-Therefore, in Example 8 in \cite{massart97} weighted regression
-is proposed which can be reproduced by
-
-<<>>=
-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)
-@
-
-If we now want to predict a new x value from measured y values,
-we use the \texttt{inverse.predict} function:
-
-<<>>=
-inverse.predict(m, 15, ws=1.67)
-inverse.predict(m, 90, ws = 0.145)
-@
-
-The weight \texttt{ws} assigned to the measured y value has to be
-given by the user in the case of weighted regression, or alternatively,
-the approximate variance \texttt{var.s} at this location.
-
-\section*{Theory for \texttt{inverse.predict}}
-Equation 8.28 in \cite{massart97} gives a general equation for predicting the
-standard error $s_{\hat{x_s}}$ for an x value predicted from measurements of y
-according to the linear calibration function $ y = b_0 + b_1 \cdot x$:
-
-\begin{equation}
-s_{\hat{x_s}} = \frac{s_e}{b_1} \sqrt{\frac{1}{w_s m} + \frac{1}{\sum{w_i}} +
- \frac{(\bar{y_s} - \bar{y_w})^2 \sum{w_i}}
- {{b_1}^2 \left( \sum{w_i} \sum{w_i {x_i}^2} -
- {\left( \sum{ w_i x_i } \right)}^2 \right) }}
-\end{equation}
-
-with
-
-\begin{equation}
-s_e = \sqrt{ \frac{\sum w_i (y_i - \hat{y_i})^2}{n - 2}}
-\end{equation}
-
-where $w_i$ is the weight for calibration standard $i$, $y_i$ is the mean $y$
-value (!) observed for standard $i$, $\hat{y_i}$ is the estimated value for
-standard $i$, $n$ is the number calibration standards, $w_s$ is the weight
-attributed to the sample $s$, $m$ is the number of replicate measurements of
-sample $s$, $\bar{y_s}$ is the mean response for the sample,
-$\bar{y_w} = \frac{\sum{w_i y_i}}{\sum{w_i}}$ is the weighted mean of responses
-$y_i$, and $x_i$ is the given $x$ value for standard $i$.
-
-The weight $w_s$ for the sample should be estimated or calculated in accordance
-to the weights used in the linear regression.
-
-I adjusted the above equation in order to be able to take a different
-precisions in standards and samples into account. In analogy to Equation 8.26
-from \cite{massart97} we get
-
-\begin{equation}
-s_{\hat{x_s}} = \frac{1}{b_1} \sqrt{\frac{{s_s}^2}{w_s m} +
- {s_e}^2 \left( \frac{1}{\sum{w_i}} +
- \frac{(\bar{y_s} - \bar{y_w})^2 \sum{w_i}}
- {{b_1}^2 \left( \sum{w_i} \sum{w_i {x_i}^2} - {\left( \sum{ w_i x_i } \right)}^2 \right) } \right) }
-\end{equation}
-
-where I interpret $\frac{{s_s}^2}{w_s}$ as an estimator of the variance at location
-$\hat{x_s}$, which can be replaced by a user-specified value using the argument
-\texttt{var.s} of the function \texttt{inverse.predict}.
-
-\section*{Fitting and using a variance function}
-
-In the R package \texttt{nlme} variance functions are used for weighted
-regressions. But since the \texttt{predict.nlme} method does not calculate
-prediction intervals, this is not useful for the \texttt{calplot} function.
-
-Two approaches could be used for fitting variance functions, one based on
-residuals from an unweighted fit, and one based on just the variances
-of the different samples along the x axis. If we used the residuals for
-fitting, a bias of the model in a certain area would result in a higher
-variance, so it seems preferable to choose the second approach. Of course,
-a prerequisite is to have sufficient repetitions for each sample in any
-case.
-
-Let's take the above example and estimate a variance function
-
-<<>>=
-library(doBy)
-massart97ex3
-massart97ex3$x <- factor(massart97ex3$x)
-summary <- summaryBy(y~x, data = massart97ex3,FUN=c(mean,sd,var))
-summary$x <- as.numeric(as.vector((summary$x)))
-plot(summary$x, summary$y.var,
- xlim=c(0,50),
- ylim=c(0,max(summary$y.var)))
-varModel <- lm(y.var ~ I(x^2) + x, data=summary)
-varCurve <- predict(varModel, newdata=data.frame(x=0:5000/100))
-lines(0:5000/100,varCurve)
-@
-
-
-\begin{thebibliography}{1}
-\bibitem{massart97}
-Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
-Smeyers-Verbeke, J.
-\newblock Handbook of Chemometrics and Qualimetrics: Part A,
-\newblock Elsevier, Amsterdam, 1997
-\end{thebibliography}
-
-\end{document}
diff --git a/trunk/chemCal/vignettes/chemCal.aux b/trunk/chemCal/vignettes/chemCal.aux
deleted file mode 100644
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--- a/trunk/chemCal/vignettes/chemCal.aux
+++ /dev/null
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-\relax
-\ifx\hyper@anchor\@undefined
-\global \let \oldcontentsline\contentsline
-\gdef \contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
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-\AtEndDocument{\let \contentsline\oldcontentsline
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-\fi
-
-\citation{massart97}
-\citation{massart97}
-\citation{massart97}
-\citation{massart97}
-\bibcite{massart97}{1}
diff --git a/trunk/chemCal/vignettes/chemCal.bbl b/trunk/chemCal/vignettes/chemCal.bbl
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diff --git a/trunk/chemCal/vignettes/chemCal.out b/trunk/chemCal/vignettes/chemCal.out
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diff --git a/trunk/chemCal/vignettes/chemCal.pdf b/trunk/chemCal/vignettes/chemCal.pdf
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--- a/trunk/chemCal/vignettes/chemCal.pdf
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