From 513dfbdcdda94a901b5901b486ff5500c7d158b1 Mon Sep 17 00:00:00 2001 From: ranke Date: Wed, 10 May 2006 15:44:14 +0000 Subject: 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 --- DESCRIPTION | 2 +- INDEX | 15 +- R/calplot.R | 52 ++ R/chemCal.R | 95 --- R/inverse.predict.lm.R | 95 ++- data/draper.R | 5 + demo/massart97ex3.R | 15 +- inst/doc/Makefile | 27 + inst/doc/Rplots.ps | 1763 ++++++++++++++++++++++++++++++++++++++++++++++ inst/doc/chemCal-001.eps | 1762 +++++++++++++++++++++++++++++++++++++++++++++ inst/doc/chemCal-001.pdf | 1727 +++++++++++++++++++++++++++++++++++++++++++++ inst/doc/chemCal.Rnw | 87 +++ inst/doc/chemCal.aux | 17 + inst/doc/chemCal.bbl | 0 inst/doc/chemCal.blg | 46 ++ inst/doc/chemCal.log | 364 ++++++++++ inst/doc/chemCal.out | 0 inst/doc/chemCal.pdf | Bin 0 -> 105421 bytes man/calm.Rd | 43 -- man/calplot.lm.Rd | 55 ++ man/draper.Rd | 9 + man/inverse.predict.Rd | 65 ++ man/plot.calm.Rd | 48 -- man/predictx.Rd | 37 - 24 files changed, 6067 insertions(+), 262 deletions(-) create mode 100644 R/calplot.R delete mode 100644 R/chemCal.R create mode 100644 data/draper.R create mode 100644 inst/doc/Makefile create mode 100644 inst/doc/Rplots.ps create mode 100644 inst/doc/chemCal-001.eps create mode 100644 inst/doc/chemCal-001.pdf create mode 100644 inst/doc/chemCal.Rnw create mode 100644 inst/doc/chemCal.aux create mode 100644 inst/doc/chemCal.bbl create mode 100644 inst/doc/chemCal.blg create mode 100644 inst/doc/chemCal.log create mode 100644 inst/doc/chemCal.out create mode 100644 inst/doc/chemCal.pdf delete mode 100644 man/calm.Rd create mode 100644 man/calplot.lm.Rd create mode 100644 man/draper.Rd create mode 100644 man/inverse.predict.Rd delete mode 100644 man/plot.calm.Rd delete mode 100644 man/predictx.Rd 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 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 +# 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 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n +0000000375 00000 n +0000028188 00000 n +0000028385 00000 n +0000028642 00000 n +trailer +<< +/Size 11 +/Info 1 0 R +/Root 2 0 R +>> +startxref +28739 +%%EOF 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: + +<>= +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} +\gdef \newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}} +\AtEndDocument{\let \contentsline\oldcontentsline +\let \newlabel\oldnewlabel} +\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 diff --git a/inst/doc/chemCal.blg b/inst/doc/chemCal.blg new file mode 100644 index 0000000..72f5a0e --- /dev/null +++ b/inst/doc/chemCal.blg @@ -0,0 +1,46 @@ +This is BibTeX, Version 0.99c (Web2C 7.5.4) +The top-level auxiliary file: chemCal.aux +I found no \bibdata command---while reading file chemCal.aux +I found no \bibstyle 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Rerun to get cross-references right. + + ) +Here is how much of TeX's memory you used: + 3752 strings out of 94499 + 51374 string characters out of 1175814 + 95260 words of memory out of 1000000 + 6850 multiletter control sequences out of 10000+50000 + 24598 words of font info for 57 fonts, out of 500000 for 2000 + 580 hyphenation exceptions out of 8191 + 35i,6n,21p,255b,255s stack positions out of 1500i,500n,5000p,200000b,5000s +PDF statistics: + 70 PDF objects out of 300000 + 9 named destinations out of 131072 + 22 words of extra memory for PDF output out of 65536 + +Output written on chemCal.pdf (3 pages, 105421 bytes). diff --git a/inst/doc/chemCal.out b/inst/doc/chemCal.out new file mode 100644 index 0000000..e69de29 diff --git a/inst/doc/chemCal.pdf b/inst/doc/chemCal.pdf new file mode 100644 index 0000000..b0da323 Binary files /dev/null and b/inst/doc/chemCal.pdf differ 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} -- cgit v1.2.1