Moved everything into the trunk directory, in order to enable branching

git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@22 5fad18fb-23f0-0310-ab10-e59a3bee62b4

10 files changed, 0 insertions, 502 deletions

diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
deleted file mode 100644
index 6f6d584..0000000
--- a/man/calplot.lm.Rd
+++ /dev/null
@@ -1,62 +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.
-  }
-  \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.
-}
-\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/man/chemCal-package.Rd b/man/chemCal-package.Rd
deleted file mode 100644
index 4456150..0000000
--- a/man/chemCal-package.Rd
+++ /dev/null
@@ -1,17 +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/man/din32645.Rd b/man/din32645.Rd
deleted file mode 100644
index cacbf07..0000000
--- a/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/man/inverse.predict.Rd b/man/inverse.predict.Rd
deleted file mode 100644
index 347d670..0000000
--- a/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/man/ipowfunc.Rd b/man/ipowfunc.Rd
deleted file mode 100644
index e09e590..0000000
--- a/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/man/lod.Rd b/man/lod.Rd
deleted file mode 100644
index e468e1d..0000000
--- a/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/man/loq.Rd b/man/loq.Rd
deleted file mode 100644
index 7541e77..0000000
--- a/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/man/massart97ex1.Rd b/man/massart97ex1.Rd
deleted file mode 100644
index 44e1b85..0000000
--- a/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/man/massart97ex3.Rd b/man/massart97ex3.Rd
deleted file mode 100644
index efdcf02..0000000
--- a/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/man/powfunc.Rd b/man/powfunc.Rd
deleted file mode 100644
index 73fe3b0..0000000
--- a/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}