The inverse prediction works in a variety of cases and is

tested with Examples 7 and 8 from Massart!

I need to compare with the DIN and draper examples, and finish
the package vignette.



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@6 5fad18fb-23f0-0310-ab10-e59a3bee62b4

24 files changed, 6067 insertions, 262 deletions

diff --git a/DESCRIPTION b/DESCRIPTION
index e19e179..556600c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: chemCal
-Version: 0.05-4
+Version: 0.05-6
 Date: 2006-05-09
 Title: Calibration functions for analytical chemistry
 Author: Johannes Ranke <jranke@uni-bremen.de>
diff --git a/INDEX b/INDEX
index ec2f1b4..3c17013 100644
--- a/INDEX
+++ b/INDEX
@@ -1,10 +1,7 @@
-calm					Generate a linear calibration model
-calplot                 Plot calibration graphs
-plot.calm				Plot prediction bands
-calpredict              Estimate measurement results including
-                        confidence intervals
-predict.calm			Estimate measurement results including confidence 
-						intervals
+calplot                 Plot calibration graphs from univariate linear
+                        models
 din32645                Calibration data from DIN 32645
-pahCalibration          Calibration data for HPLC measurement of 4 PAH
-pahMeasurements			Measurement data for HPLC measurement of 4 PAH
+draper                  Regression example with repeated measurements
+inverse.predict         Predict x from y for a linear calibration
+massart97ex3            Calibration data from Massart et al. (1997),
+                        example 3
diff --git a/R/calplot.R b/R/calplot.R
new file mode 100644
index 0000000..cea1149
--- /dev/null
+++ b/R/calplot.R
@@ -0,0 +1,52 @@
+calplot <- function(object, xlim = "auto", ylim = "auto", 
+  xlab = "Concentration", ylab = "Response", alpha=0.05)
+{
+  UseMethod("calplot")
+}
+
+calplot.default <- function(object, xlim = "auto", ylim = "auto", 
+  xlab = "Concentration", ylab = "Response", alpha=0.05)
+{
+  stop("Calibration plots only implemented for univariate lm objects.")
+}
+
+calplot.lm <- function(object, xlim = "auto", ylim = "auto", 
+  xlab = "Concentration", ylab = "Response", alpha=0.05)
+{
+  if (length(object$coef) > 2)
+    stop("More than one independent variable in your model - not implemented")
+
+  if (alpha <= 0 | alpha >= 1)
+    stop("Alpha should be between 0 and 1 (exclusive)")
+
+  m <- object
+  level <- 1 - alpha
+  x <- m$model$x
+  y <- m$model$y
+  newdata <- data.frame(x = seq(0,max(x),length=250))
+  pred.lim <- predict(m, newdata, interval = "prediction",level=level)
+  conf.lim <- predict(m, newdata, interval = "confidence",level=level)
+  if (xlim == "auto") xlim = c(0,max(x))
+  if (ylim == "auto") ylim = range(c(pred.lim,y))
+  plot(1,
+    type = "n", 
+    xlab = xlab,
+    ylab = ylab,
+    xlim = xlim,
+    ylim = ylim
+  )
+  points(x,y, pch = 21, bg = "yellow")
+  matlines(newdata$x, pred.lim, lty = c(1, 4, 4), 
+    col = c("black", "red", "red"))
+  matlines(newdata$x, conf.lim, lty = c(1, 3, 3), 
+    col = c("black", "green4", "green4"))
+
+  legend(min(x), 
+    max(pred.lim, na.rm = TRUE), 
+    legend = c("Fitted Line", "Confidence Bands", 
+        "Prediction Bands"), 
+    lty = c(1, 3, 4), 
+    lwd = 2, 
+    col = c("black", "green4", "red"), 
+    horiz = FALSE, cex = 0.9, bg = "gray95")
+}
diff --git a/R/chemCal.R b/R/chemCal.R
deleted file mode 100644
index fab7db4..0000000
--- a/R/chemCal.R
+++ /dev/null
@@ -1,95 +0,0 @@
-calm <- function(data)
-{
-    y <- data[[2]]
-    x <- data[[1]]
-    m <- lm(y ~ x)
-    s <- summary(m)
-    if (s$coefficients[1,4] > 0.05) 
-    {
-        m <- lm(y ~ x - 1)
-        s <- summary(m)
-        m$intercept <- FALSE
-    } else {
-        m$intercept <- TRUE
-    }
-    class(m) <- "calm"
-    m$yname <- names(data)[[1]]
-    m$xname <- names(data)[[2]]
-    return(m)    
-}
-predict.calm <- predict.lm
-print.calm <- print.lm
-summary.calm <- summary.lm
-plot.calm <- function(x,...,
-    xunit="",yunit="",measurand="",
-    level=0.95)
-{
-    m <- x
-    x <- m$model$x
-    y <- m$model$y
-    newdata <- data.frame(x = seq(0,max(x),length=250))
-    pred.lim <- predict(m, newdata, interval = "prediction",level=level)
-    conf.lim <- predict(m, newdata, interval = "confidence",level=level)
-    if (xunit!="") {
-        xlab <- paste("Concentration in ",xunit)
-    } else xlab <- m$xname
-    if (yunit=="") yunit <- m$yname
-    if (measurand!="") {
-        main <- paste("Calibration for",measurand)
-    } else main <- "Calibration"
-    plot(1,
-        xlab = xlab,
-        ylab = yunit,
-        type = "n", 
-        main = main,
-        xlim = c(0,max(x)), 
-        ylim = range(pred.lim)
-    )
-    points(x,y, pch = 21, bg = "yellow")
-    matlines(newdata$x, pred.lim, lty = c(1, 4, 4), 
-        col = c("black", "red", "red"))
-    matlines(newdata$x, 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")
-}
-predictx <- function(m,yobs,level=0.95)
-{
-    s <- summary(m)    
-    xi <- m$model$x
-    yi <- m$model$y
-    n <- length(yi)
-    p <- length(yobs)
-    if (p > 1) {
-        varyobs <- var(yobs)
-    } else {
-        varyobs <- 0
-    }
-    if (!m$intercept) {
-        b1 <- summary(m)$coef["x","Estimate"]
-        varb1 <- summary(m)$coef["x","Std. Error"]^2
-        xpred <- mean(yobs)/b1
-        varxpred <- (varyobs + xpred^2 * varb1) / b1^2
-        sdxpred <- sqrt(varxpred) 
-    } else 
-    {
-        b0 <- summary(m)$coef["(Intercept)","Estimate"]
-        b1 <- summary(m)$coef["x","Estimate"]
-        S <- summary(m)$sigma
-        xpred <- (mean(yobs) - b0)/b1
-        sumxxbar <- sum((xi - mean(xi))^2) 
-        yybar <- (mean(yobs) - mean(yi))^2
-        sdxpred <- (S/b1) * (1/p + 1/n + yybar/(b1^2 * sumxxbar))^0.5
-    }
-    t <- qt((1 + level)/2,n - 2)
-    confxpred <- t * sdxpred
-
-    result <- c(estimate=xpred,sdxpred=sdxpred,confxpred=confxpred)
-}
diff --git a/R/inverse.predict.lm.R b/R/inverse.predict.lm.R
index f438d99..f1921e4 100644
--- a/R/inverse.predict.lm.R
+++ b/R/inverse.predict.lm.R
@@ -1,40 +1,89 @@
 # This is an implementation of Equation (8.28) in the Handbook of Chemometrics
-# and Qualimetrics, Part A, Massart et al, page 200, validated with Example 8
-# on the same page
+# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
+# Example 8 on the same page
 
-inverse.predict <- function(object, newdata, alpha=0.05)
+inverse.predict <- function(object, newdata, 
+  ws = ifelse(length(object$weights) > 0, mean(object$weights), 1),
+  alpha=0.05, ss = "auto")
 {
   UseMethod("inverse.predict")
 }
 
-inverse.predict.default <- function(object, newdata, alpha=0.05)
+inverse.predict.default <- function(object, newdata, 
+  ws = ifelse(length(object$weights) > 0, mean(object$weights), 1),
+  alpha=0.05, ss = "auto")
 {
   stop("Inverse prediction only implemented for univariate lm objects.")
 }
 
-inverse.predict.lm <- function(object, newdata, alpha=0.05)
+inverse.predict.lm <- function(object, newdata, 
+  ws = ifelse(length(object$weights) > 0, mean(object$weights), 1),
+  alpha=0.05, ss = "auto")
 {
-  if (is.list(newdata)) {
-    if (!is.null(newdata$y))
-      newdata <- newdata$y
-    else
-      stop("Newdata list should contain element newdata$y")
-  }
+  if (length(object$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)
 
-  if (is.matrix(newdata)) {
-    Y <- newdata
-    Ybar <- apply(Y,1,mean)
-    nrepl <- ncol(Y)
+  yx <- split(object$model$y,object$model$x)
+  n <- length(yx)
+  x <- as.numeric(names(yx))
+  ybar <- sapply(yx,mean)
+  if (length(object$weights) > 0) {
+    wx <- split(object$weights,object$model$x)
+    w <- sapply(wx,mean)
+  } else {
+    w <- rep(1,n)
   }
-  else {
-    Y <- as.vector(newdata)
-    Ybar <- Y
-    nrepl <- 1 
+  yhatx <- split(object$fitted.values,object$model$x)
+  yhat <- sapply(yhatx,mean)
+  se <- sqrt(sum(w*(ybar - yhat)^2)/(n-2))
+  if (ss == "auto") {
+    ss <- se
+  } else {
+    ss <- ss
   }
 
-  if (length(object$coef) > 2)
-    stop("Inverse prediction not yet implemented for more than one independent variable")
+  b1 <- object$coef[["x"]]
 
-  if (alpha <= 0 | alpha >= 1)
-    stop("Alpha should be between 0 and 1 (exclusive)")
+  ybarw <- sum(w * ybar)/sum(w)
+
+# The commented out code for sxhats is equation 8.28 without changes.  It has
+# been replaced by the code below, in order to be able to take into account a
+# precision in the sample measurements that differs from the precision in the
+# calibration samples.
+
+#  sxhats <- se/b1 * sqrt(
+#    1/(ws * m) + 
+#    1/sum(w) + 
+#    (ybars - ybarw)^2 * sum(w) /
+#      (b1^2 * (sum(w) * sum(w * x^2) - sum(w * x)^2))
+#  )
+
+# This is equation 8.28, but with the possibility to take into account a 
+# different precision measurement of the sample and standard solutions
+# in analogy to equation 8.26
+  sxhats <- 1/b1 * sqrt(
+    ss^2/(ws * 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/data/draper.R b/data/draper.R
new file mode 100644
index 0000000..5aa7ae9
--- /dev/null
+++ b/data/draper.R
@@ -0,0 +1,5 @@
+"draper" <-
+structure(list(x=c(1.3, 1.3, 2.0, 2.0, 2.7, 3.3, 3.3, 3.7, 3.7, 4.0,
+4.0, 4.0, 4.7, 4.7, 4.7, 5.0, 5.3, 5.3, 5.3, 5.7, 6.0, 6.0, 6.3, 6.7),
+y=c(2.3, 1.8, 2.8, 1.5, 2.2, 3.8, 1.8, 3.7, 1.7, 2.8, 2.8, 2.2, 5.4,
+3.2, 1.9, 1.8, 3.5, 2.8, 2.1, 3.4, 3.2, 3.0, 3.0, 5.9)))
diff --git a/demo/massart97ex3.R b/demo/massart97ex3.R
index 7bf9633..731aba6 100644
--- a/demo/massart97ex3.R
+++ b/demo/massart97ex3.R
@@ -1,12 +1,15 @@
 library(chemCal)
 data(massart97ex3)
 attach(massart97ex3)
-xi <- levels(factor(x))
 yx <- split(y,factor(x))
-ybari <- sapply(yx,mean)
-si <- round(sapply(yx,sd),digits=2)
-wi <- round(1/(si^2),digits=3)
-data.frame(xi,ybari,si,wi)
+ybar <- sapply(yx,mean)
+s <- round(sapply(yx,sd),digits=2)
+w <- round(1/(si^2),digits=3)
+data.frame(x=levels(factor(x)),ybar,s,w)
 
-weights <- wi[factor(x)]
+weights <- w[factor(x)]
 m <- lm(y ~ x,w=weights)
+inverse.predict(m,15,ws=1.67)
+inverse.predict(m,90,ws=0.145)
+
+calplot(m)
diff --git a/inst/doc/Makefile b/inst/doc/Makefile
new file mode 100644
index 0000000..637b193
--- /dev/null
+++ b/inst/doc/Makefile
@@ -0,0 +1,27 @@
+# Makefile for Sweave documents containing both Latex and R code
+# Author:	Johannes Ranke <jranke@uni-bremen.de>
+# Last Change: 2006 Mai 10
+# based on the Makefile of Nicholas Lewin-Koh
+# in turn based on work of Rouben Rostmaian
+# SVN: $Id: Makefile.rnoweb 50 2006-04-18 11:13:52Z ranke $
+
+RNWFILES = $(wildcard *.Rnw)
+TARGETS = $(patsubst %.Rnw,%.tex,$(RNWFILE)) $(patsubst %.Rnw,%.pdf,$(RNWFILES))
+
+%.tex: %.Rnw
+	echo 'Sweave("$<")' | R --no-save --no-restore
+
+%.pdf: %.tex
+	pdflatex $<
+
+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/inst/doc/Rplots.ps b/inst/doc/Rplots.ps
new file mode 100644
index 0000000..654b7c3
--- /dev/null
+++ b/inst/doc/Rplots.ps
@@ -0,0 +1,1763 @@
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diff --git a/inst/doc/chemCal.Rnw b/inst/doc/chemCal.Rnw
new file mode 100644
index 0000000..2c902ab
--- /dev/null
+++ b/inst/doc/chemCal.Rnw
@@ -0,0 +1,87 @@
+\documentclass[a4paper]{article}
+%\VignetteIndexEntry{Short manual for the chemCal package}
+\newcommand{\chemCal}{{\tt chemCal}}
+\newcommand{\calplot}{{\tt calplot}}
+\newcommand{\calpredict}{{\tt calpredict}}
+\newcommand{\R}{{\tt R}}
+\usepackage{hyperref}
+
+\title{Basic calibration functions for analytical chemistry}
+\author{Johannes Ranke}
+
+\begin{document}
+\maketitle
+
+The \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 could be heavily improved if the
+inverse prediction method given in \cite{massart97} would be implemented,
+since it also covers the case of weighted regression.
+
+At the moment, the package only consists of two functions, working
+on univariate linear models of class \texttt{lm}.
+
+When calibrating an analytical method, the first task is to generate
+a suitable model. If we want to use the \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)
+attach(massart97ex3)
+yx <- split(y,factor(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)
+@
+
+This is a reproduction of Example 8 in \cite{massart97}. We can
+see the influence of the weighted regression on the confidence
+and prediction bands of the calibration.
+
+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)
+@
+
+The weight \texttt{ws} assigned to the measured y value has to be 
+given by the user in the case of weighted regression. By default, 
+the mean of the weights used in the linear regression is used.
+
+\section*{Theory}
+Equation 8.28 in \cite{massart97} gives a general equation for predicting x
+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})^2}{n - 2}}
+\end{equation}
+
+
+\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/inst/doc/chemCal.aux b/inst/doc/chemCal.aux
new file mode 100644
index 0000000..0eb51cc
--- /dev/null
+++ b/inst/doc/chemCal.aux
@@ -0,0 +1,17 @@
+\relax 
+\ifx\hyper@anchor\@undefined
+\global \let \oldcontentsline\contentsline
+\gdef \contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
+\global \let \oldnewlabel\newlabel
+\gdef \newlabel#1#2{\newlabelxx{#1}#2}
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+\AtEndDocument{\let \contentsline\oldcontentsline
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+\else
+\global \let \hyper@last\relax 
+\fi
+
+\citation{massart97}
+\citation{massart97}
+\citation{massart97}
+\bibcite{massart97}{1}
diff --git a/inst/doc/chemCal.bbl b/inst/doc/chemCal.bbl
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/inst/doc/chemCal.bbl
diff --git a/inst/doc/chemCal.blg b/inst/doc/chemCal.blg
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diff --git a/inst/doc/chemCal.out b/inst/doc/chemCal.out
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/inst/doc/chemCal.out
diff --git a/inst/doc/chemCal.pdf b/inst/doc/chemCal.pdf
new file mode 100644
index 0000000..b0da323
--- /dev/null
+++ b/inst/doc/chemCal.pdf
diff --git a/man/calm.Rd b/man/calm.Rd
deleted file mode 100644
index c16f663..0000000
--- a/man/calm.Rd
+++ /dev/null
@@ -1,43 +0,0 @@
-\name{calm}
-\alias{calm}
-\alias{print.calm}
-\alias{predict.calm}
-\alias{summary.calm}
-\title{Generate a calibration model}
-\description{
-  This function fits a calibration model to the data 
-  frame.
-}
-\usage{
-  calm(data)
-}
-\arguments{
-  \item{data}{
-    A data frame with numeric x data in the first column and
-    numeric y data in the second column.
-    }
-}
-\value{
-  An object of class \code{calm}, which is derived from
-  a linear model \code{lm}, the only difference being that
-  it contains the additional attributes \code{xname},
-  \code{yname} and \code{intercept}, the latter being a 
-  boolean reporting wether the model uses an intercept or not.
-} 
-\note{
-  The decision if the returned model contains an intercept is taken based on
-  the significance of the fitted intercept on a significance level of 0.95.
-  The methods \code{\link{print.calm}}, \code{\link{predict.calm}}
-  \code{\link{summary.calm}} are just newly assigned names for the
-  corresponding methods from the class \code{\link{lm}}.
-}
-\examples{
-  data(din32645)
-  calm(din32645)
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}
diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
new file mode 100644
index 0000000..c2b8116
--- /dev/null
+++ b/man/calplot.lm.Rd
@@ -0,0 +1,55 @@
+\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 prediction and confidence bands. 
+}
+\usage{
+  calplot(object, xlim = "auto", ylim = "auto", 
+  xlab = "Concentration", ylab = "Response", alpha=0.05)
+}
+\arguments{
+  \item{object}{
+    A univariate model object of class \code{\link{lm}} 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 confidence level for the confidence and prediction bands.
+  }
+}
+\value{
+  A plot of the calibration data, of your fitted model as well as lines showing
+  the confidence limits as well as the prediction limits.
+} 
+\examples{
+# Example of a Calibration plot for a weighted regression
+data(massart97ex3)
+attach(massart97ex3)
+yx <- split(y,factor(x))
+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)
+}
+\author{
+  Johannes Ranke 
+  \email{jranke@uni-bremen.de} 
+  \url{http://www.uft.uni-bremen.de/chemie/ranke}
+}
+\keyword{regression}
diff --git a/man/draper.Rd b/man/draper.Rd
new file mode 100644
index 0000000..6a8de00
--- /dev/null
+++ b/man/draper.Rd
@@ -0,0 +1,9 @@
+\name{draper}
+\alias{draper}
+\title{Regression example with repeated measurements}
+\usage{data(draper)}
+\references{Draper and Smith, Applied Regression Analysis (1981), p. 38}
+\format{A dataframe with 24 observations on 2 variables}
+\description{An example of a regression with multiple measurements per
+factor level.}
+\keyword{datasets}
diff --git a/man/inverse.predict.Rd b/man/inverse.predict.Rd
new file mode 100644
index 0000000..48534c4
--- /dev/null
+++ b/man/inverse.predict.Rd
@@ -0,0 +1,65 @@
+\name{inverse.predict}
+\alias{inverse.predict}
+\alias{inverse.predict.lm}
+\alias{inverse.predict.default}
+\title{Predict x from y for a linear calibration}
+\usage{inverse.predict(object, newdata, 
+  ws = ifelse(length(object$weights) > 0, mean(object$weights), 1),
+  alpha=0.05, ss = "auto")
+}
+\arguments{
+  \item{object}{
+    A univariate model object of class \code{\link{lm}} with model formula
+    \code{y ~ x} or \code{y ~ x - 1}.
+  }
+  \item{newdata}{ 
+    A vector of observed y values for one sample.
+  }
+  \item{ws}{ 
+    The weight attributed to the sample. The default is to take the 
+    mean of the weights in the model, if there are any.
+  }
+  \item{alpha}{
+    The confidence level for the confidence interval to be reported.
+  }
+  \item{ss}{
+    The estimated standard error of the sample measurements. The 
+    default is to take the residual standard error from the calibration.
+  }
+}
+\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{
+data(massart97ex3)
+attach(massart97ex3)
+yx <- split(y,factor(x))
+s <- round(sapply(yx,sd),digits=2)
+w <- round(1/(s^2),digits=3)
+weights <- w[factor(x)]
+m <- lm(y ~ x,w=weights)
+
+inverse.predict(m,c(15))
+}
+\keyword{manip}
diff --git a/man/plot.calm.Rd b/man/plot.calm.Rd
deleted file mode 100644
index bb302c7..0000000
--- a/man/plot.calm.Rd
+++ /dev/null
@@ -1,48 +0,0 @@
-\name{plot.calm}
-\alias{plot.calm}
-\title{Plot calibration graphs from calibration models}
-\description{
-	Produce graphics of calibration data, the fitted model as well
-  as prediction and confidence intervals.
-}
-\usage{
-  plot.calm(x,...,xunit="",yunit="",measurand="",level=0.95)
-}
-\arguments{
-  \item{x}{
-    A calibration model of type \code{\link{calm}}. It is named
-    x here because the generic plot method expects x to be its 
-    first argument.
-    }
-  \item{...}{
-    I just included this because I wanted to avoid the error messages
-    from R CMD check that tell me I should read "Writing R extensions" 
-    which I did ...
-  }
-  \item{xunit}{
-    The unit of the given values on the x axis as a character vector.
-    }
-  \item{yunit}{
-    The unit of the y axis as a character vector.
-    }
-  \item{measurand}{ 
-    The name of what is being measured as a character vector.
-    }
-  \item{level}{
-    The confidence level of the confidence and prediction bands. Defaults to
-    0.95.
-    }
-}
-\value{
-  A plot of the calibration data, of your fitted model as well as lines showing
-  the confidence limits and the prediction limits.
-} 
-\examples{
-
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}
diff --git a/man/predictx.Rd b/man/predictx.Rd
deleted file mode 100644
index a3946b0..0000000
--- a/man/predictx.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-\name{predictx}
-\alias{predictx}
-\title{Predict x from y values for calibration models}
-\description{
-  This function predicts x values from y values, as in classical calibration,
-  including a confindence interval.
-}
-\usage{
-  predictx(m,yobs,level=0.95)
-}
-\arguments{
-  \item{m}{
-    A calibration model of type \code{\link{calm}}.
-    }
-  \item{yobs}{ 
-    A vector of observed y values for one sample.
-    }
-  \item{level}{
-    The confidence level for the confidence interval to be reported.
-    }
-}
-\value{
-  A vector containing the estimate (\code{estimate}), its estimated standard
-  deviation (\code{sdxpred}), its estimated confidence (\code{confxpred}).
-} 
-\examples{
-  data(din32645)
-  m <- calm(din32645)
-  r <- predictx(m,3500,level=0.95)
-  cat("\nThe confidence interval is",r[["estimate"]],"+-",r[["confxpred"]],"\n")
-}
-\author{
-  Johannes Ranke 
-  \email{jranke@uni-bremen.de} 
-  \url{http://www.uft.uni-bremen.de/chemie/ranke}
-}
-\keyword{regression}