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authorranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2006-06-23 16:42:10 +0000
committerranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2006-06-23 16:42:10 +0000
commitf4443942f10740ecc62b928181a1911ef14eeb04 (patch)
tree9f88ae546732b6db48db96c3372f394665684988
parent7889cda8d4133e68b07e7b204cd75295e0b348ee (diff)
Added the data from Massart example 1, and one more
tests showing the validity of inverse.predict. git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@18 5fad18fb-23f0-0310-ab10-e59a3bee62b4
-rw-r--r--DESCRIPTION2
-rw-r--r--data/massart97ex1.rdabin0 -> 193 bytes
-rw-r--r--inst/doc/Rplots.ps3977
-rw-r--r--inst/doc/chemCal-001.eps3561
-rw-r--r--inst/doc/chemCal-001.pdf42
-rw-r--r--inst/doc/chemCal-002.eps613
-rw-r--r--inst/doc/chemCal-002.pdf46
-rw-r--r--inst/doc/chemCal.log4
-rw-r--r--inst/doc/chemCal.pdfbin123693 -> 123694 bytes
-rw-r--r--inst/doc/chemCal.tex338
-rw-r--r--man/calplot.lm.Rd6
-rw-r--r--man/inverse.predict.Rd16
-rw-r--r--man/massart97ex1.Rd17
-rw-r--r--tests/massart97.R6
-rw-r--r--tests/massart97.Rout.save42
15 files changed, 4312 insertions, 4358 deletions
diff --git a/DESCRIPTION b/DESCRIPTION
index 8188543..07ee5a0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
Package: chemCal
-Version: 0.1-17
+Version: 0.1-18
Date: 2006-06-23
Title: Calibration functions for analytical chemistry
Author: Johannes Ranke <jranke@uni-bremen.de>
diff --git a/data/massart97ex1.rda b/data/massart97ex1.rda
new file mode 100644
index 0000000..1a6fd4b
--- /dev/null
+++ b/data/massart97ex1.rda
Binary files differ
diff --git a/inst/doc/Rplots.ps b/inst/doc/Rplots.ps
index c48ffb2..c5e68ec 100644
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diff --git a/inst/doc/chemCal.log b/inst/doc/chemCal.log
index c3428c6..a21c788 100644
--- a/inst/doc/chemCal.log
+++ b/inst/doc/chemCal.log
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+This is pdfeTeX, Version 3.141592-1.21a-2.2 (Web2C 7.5.4) (format=pdflatex 2006.5.30) 23 JUN 2006 18:41
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**chemCal.tex
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+Output written on chemCal.pdf (5 pages, 123694 bytes).
diff --git a/inst/doc/chemCal.pdf b/inst/doc/chemCal.pdf
index 799fe9b..c7a01d7 100644
--- a/inst/doc/chemCal.pdf
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diff --git a/inst/doc/chemCal.tex b/inst/doc/chemCal.tex
index 32c5143..9617cda 100644
--- a/inst/doc/chemCal.tex
+++ b/inst/doc/chemCal.tex
@@ -1,169 +1,169 @@
-\documentclass[a4paper]{article}
-%\VignetteIndexEntry{Short manual for the chemCal package}
-\usepackage{hyperref}
-
-\title{Basic calibration functions for analytical chemistry}
-\author{Johannes Ranke}
-
-\usepackage{d:/Programme/R/R-2.3.1/share/texmf/Sweave}
-\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/cgi-bin/R/wishlst-fulfilled?id=8877;user=guest}{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:
-
-\begin{Schunk}
-\begin{Sinput}
-> library(chemCal)
-> data(massart97ex3)
-> m0 <- lm(y ~ x, data = massart97ex3)
-> calplot(m0)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{chemCal-001}
-
-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:
-
-\begin{Schunk}
-\begin{Sinput}
-> plot(m0, which = 3)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{chemCal-002}
-
-Therefore, in Example 8 in \cite{massart97} weighted regression
-is proposed which can be reproduced by
-
-\begin{Schunk}
-\begin{Sinput}
-> 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)
-\end{Sinput}
-\end{Schunk}
-
-If we now want to predict a new x value from measured y values,
-we use the \texttt{inverse.predict} function:
-
-\begin{Schunk}
-\begin{Sinput}
-> inverse.predict(m, 15, ws = 1.67)
-\end{Sinput}
-\begin{Soutput}
-$Prediction
-[1] 5.865367
-
-$`Standard Error`
-[1] 0.892611
-
-$Confidence
-[1] 2.478285
-
-$`Confidence Limits`
-[1] 3.387082 8.343652
-\end{Soutput}
-\begin{Sinput}
-> inverse.predict(m, 90, ws = 0.145)
-\end{Sinput}
-\begin{Soutput}
-$Prediction
-[1] 44.06025
-
-$`Standard Error`
-[1] 2.829162
-
-$Confidence
-[1] 7.855012
-
-$`Confidence Limits`
-[1] 36.20523 51.91526
-\end{Soutput}
-\end{Schunk}
-
-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}.
-
-\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}
+\documentclass[a4paper]{article}
+%\VignetteIndexEntry{Short manual for the chemCal package}
+\usepackage{hyperref}
+
+\title{Basic calibration functions for analytical chemistry}
+\author{Johannes Ranke}
+
+\usepackage{/usr/share/R/share/texmf/Sweave}
+\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/cgi-bin/R/wishlst-fulfilled?id=8877;user=guest}{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:
+
+\begin{Schunk}
+\begin{Sinput}
+> library(chemCal)
+> data(massart97ex3)
+> m0 <- lm(y ~ x, data = massart97ex3)
+> calplot(m0)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{chemCal-001}
+
+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:
+
+\begin{Schunk}
+\begin{Sinput}
+> plot(m0, which = 3)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{chemCal-002}
+
+Therefore, in Example 8 in \cite{massart97} weighted regression
+is proposed which can be reproduced by
+
+\begin{Schunk}
+\begin{Sinput}
+> 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)
+\end{Sinput}
+\end{Schunk}
+
+If we now want to predict a new x value from measured y values,
+we use the \texttt{inverse.predict} function:
+
+\begin{Schunk}
+\begin{Sinput}
+> inverse.predict(m, 15, ws = 1.67)
+\end{Sinput}
+\begin{Soutput}
+$Prediction
+[1] 5.865367
+
+$`Standard Error`
+[1] 0.892611
+
+$Confidence
+[1] 2.478285
+
+$`Confidence Limits`
+[1] 3.387082 8.343652
+\end{Soutput}
+\begin{Sinput}
+> inverse.predict(m, 90, ws = 0.145)
+\end{Sinput}
+\begin{Soutput}
+$Prediction
+[1] 44.06025
+
+$`Standard Error`
+[1] 2.829162
+
+$Confidence
+[1] 7.855012
+
+$`Confidence Limits`
+[1] 36.20523 51.91526
+\end{Soutput}
+\end{Schunk}
+
+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}.
+
+\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/man/calplot.lm.Rd b/man/calplot.lm.Rd
index bf3f616..6f6d584 100644
--- a/man/calplot.lm.Rd
+++ b/man/calplot.lm.Rd
@@ -45,9 +45,9 @@
\note{
Prediction bands for models from weighted linear regression require weights
for the data, for which responses should be predicted. Prediction intervals
- for weighted models are not currently supported by the internally used
- function \code{\link{predict.lm}}, therefore, \code{calplot} refuses to work
- for such models.
+ 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)
diff --git a/man/inverse.predict.Rd b/man/inverse.predict.Rd
index 6bea72c..347d670 100644
--- a/man/inverse.predict.Rd
+++ b/man/inverse.predict.Rd
@@ -59,15 +59,11 @@
p. 200
}
\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)
-
-inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
+# 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/massart97ex1.Rd b/man/massart97ex1.Rd
new file mode 100644
index 0000000..44e1b85
--- /dev/null
+++ b/man/massart97ex1.Rd
@@ -0,0 +1,17 @@
+\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/tests/massart97.R b/tests/massart97.R
index 58119d9..00f837f 100644
--- a/tests/massart97.R
+++ b/tests/massart97.R
@@ -1,4 +1,10 @@
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)
diff --git a/tests/massart97.Rout.save b/tests/massart97.Rout.save
index 9386a11..cb113d0 100644
--- a/tests/massart97.Rout.save
+++ b/tests/massart97.Rout.save
@@ -18,6 +18,48 @@ 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)

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