diff options
Diffstat (limited to 'man')
| -rw-r--r-- | man/calplot.Rd | 73 | ||||
| -rw-r--r-- | man/calplot.lm.Rd | 72 | ||||
| -rw-r--r-- | man/din32645.Rd | 29 | ||||
| -rw-r--r-- | man/inverse.predict.Rd | 108 | ||||
| -rw-r--r-- | man/lod.Rd | 123 | ||||
| -rw-r--r-- | man/loq.Rd | 110 | ||||
| -rw-r--r-- | man/massart97ex1.Rd | 19 | ||||
| -rw-r--r-- | man/massart97ex3.Rd | 27 | ||||
| -rw-r--r-- | man/rl95_cadmium.Rd | 21 | ||||
| -rw-r--r-- | man/rl95_toluene.Rd | 22 | ||||
| -rw-r--r-- | man/utstats14.Rd | 23 |
11 files changed, 327 insertions, 300 deletions
diff --git a/man/calplot.Rd b/man/calplot.Rd new file mode 100644 index 0000000..440d469 --- /dev/null +++ b/man/calplot.Rd @@ -0,0 +1,73 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/calplot.R +\name{calplot} +\alias{calplot} +\alias{calplot.default} +\alias{calplot.lm} +\title{Plot calibration graphs from univariate linear models} +\usage{ +calplot( + object, + xlim = c("auto", "auto"), + ylim = c("auto", "auto"), + xlab = "Concentration", + ylab = "Response", + legend_x = "auto", + 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{legend_x}{An optional numeric value for adjusting the x coordinate of +the legend.} + +\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. +} +\description{ +Produce graphics of calibration data, the fitted model as well as +confidence, and, for unweighted regression, prediction bands. +} +\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 +} diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd deleted file mode 100644 index 39f20de..0000000 --- a/man/calplot.lm.Rd +++ /dev/null @@ -1,72 +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", legend_x = "auto", - 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{legend_x}{ - An optional numeric value for adjusting the x coordinate of the legend. - } - \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} -} -\keyword{regression} diff --git a/man/din32645.Rd b/man/din32645.Rd index ffcbaed..a8e6a31 100644 --- a/man/din32645.Rd +++ b/man/din32645.Rd @@ -1,15 +1,17 @@ -\name{din32645} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/chemCal-package.R \docType{data} +\name{din32645} \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. +A dataframe containing 10 rows of x and y values. +} +\description{ +Sample dataset to test the package. } \examples{ + m <- lm(y ~ x, data = din32645) calplot(m) @@ -45,16 +47,17 @@ 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 +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. Formerly available from - the Website of the University of Heidelberg. +Dintest. Plugin for MS Excel for evaluations of calibration data. Written by +Georg Schmitt, University of Heidelberg. Formerly available from the Website +of the University of Heidelberg. - Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including - detection and quantification capabilities (IUPAC Recommendations 1995). - Analytica Chimica Acta 391, 105 - 126. +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 index 373623e..08c24d7 100644 --- a/man/inverse.predict.Rd +++ b/man/inverse.predict.Rd @@ -1,67 +1,72 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/inverse.predict.lm.R \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") +\usage{ +inverse.predict( + object, + newdata, + ..., + ws = "auto", + 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}. - } +\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. +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. +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. +} +\details{ +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 } \note{ - The function was validated with examples 7 and 8 from Massart et al. (1997). - Note that the behaviour of inverse.predict changed with chemCal version - 0.2.1. Confidence intervals for x values obtained from calibrations with - replicate measurements did not take the variation about the means into account. - Please refer to the vignette for details.} -\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 +The function was validated with examples 7 and 8 from Massart et al. +(1997). Note that the behaviour of inverse.predict changed with chemCal +version 0.2.1. Confidence intervals for x values obtained from calibrations +with replicate measurements did not take the variation about the means into +account. Please refer to the vignette for details. } \examples{ + # This is example 7 from Chapter 8 in Massart et al. (1997) m <- lm(y ~ x, data = massart97ex1) inverse.predict(m, 15) # 6.1 +- 4.9 @@ -84,5 +89,10 @@ m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means) inverse.predict(m3.means, 15, ws = 1.67) # 5.9 +- 2.5 inverse.predict(m3.means, 90, ws = 0.145) # 44.1 +- 7.9 + +} +\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 } -\keyword{manip} @@ -1,3 +1,5 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lod.R \name{lod} \alias{lod} \alias{lod.lm} @@ -5,83 +7,82 @@ \alias{lod.default} \title{Estimate a limit of detection (LOD)} \usage{ - lod(object, \dots, alpha = 0.05, beta = 0.05, method = "default", tol = "default") +lod( + object, + ..., + alpha = 0.05, + beta = 0.05, + method = "default", + tol = "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 \dQuote{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. - } - \item{tol}{ - When the \dQuote{default} method is used, the default tolerance - for the LOD on the x scale is the value of the smallest non-zero standard - divided by 1000. Can be set to a numeric value to override this. - } +\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 \dQuote{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.} + +\item{tol}{When the \dQuote{default} method is used, the default tolerance +for the LOD on the x scale is the value of the smallest non-zero standard +divided by 1000. Can be set to a numeric value to override this.} } \value{ - A list containig the corresponding x and y values of the estimated limit of - detection of a model used for calibration. +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). +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. +* 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. } \examples{ + 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) + +} +\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. } \seealso{ - Examples for \code{\link{din32645}} +Examples for \code{\link{din32645}} } -\keyword{manip} @@ -1,3 +1,5 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loq.R \name{loq} \alias{loq} \alias{loq.lm} @@ -5,76 +7,74 @@ \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", tol = "default") +loq( + object, + ..., + alpha = 0.05, + k = 3, + n = 1, + w.loq = "auto", + var.loq = "auto", + tol = "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. 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. - } - \item{tol}{ - The default tolerance for the LOQ on the x scale is the value of the - smallest non-zero standard divided by 1000. Can be set to a - numeric value to override this. - } +\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{\dots}{Placeholder for further arguments that might be needed by +future implementations.} + +\item{alpha}{The error tolerance for the prediction of x values in the +calculation.} + +\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.} + +\item{tol}{The default tolerance for the LOQ on the x scale is the value of +the smallest non-zero standard divided by 1000. Can be set to a numeric +value to override this.} } \value{ - The estimated limit of quantification for a model used for calibration. +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. +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. +* 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{ + m <- lm(y ~ x, data = massart97ex1) loq(m) # We can get better by using replicate measurements loq(m, n = 3) + } \seealso{ - Examples for \code{\link{din32645}} +Examples for \code{\link{din32645}} } -\keyword{manip} diff --git a/man/massart97ex1.Rd b/man/massart97ex1.Rd index 44e1b85..d154a9c 100644 --- a/man/massart97ex1.Rd +++ b/man/massart97ex1.Rd @@ -1,17 +1,18 @@ -\name{massart97ex1} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/chemCal-package.R \docType{data} +\name{massart97ex1} \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. +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. +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. +} +\description{ +Sample dataset from p. 175 to test the package. } \keyword{datasets} diff --git a/man/massart97ex3.Rd b/man/massart97ex3.Rd index d7f8d00..284a435 100644 --- a/man/massart97ex3.Rd +++ b/man/massart97ex3.Rd @@ -1,16 +1,23 @@ -\name{massart97ex3} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/chemCal-package.R \docType{data} +\name{massart97ex3} \alias{massart97ex3} \title{Calibration data from Massart et al. (1997), example 3} -\description{ - Sample dataset from p. 188 to test the package. -} -\usage{massart97ex3} \format{ - A dataframe containing 6 levels of x values with 5 - observations of y for each level. +A dataframe containing 6 levels of x values with 5 observations of y +for each level. +} +\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. +} +\description{ +Sample dataset from p. 188 to test the package. } \examples{ + # For reproducing the results for replicate standard measurements in example 8, # we need to do the calibration on the means when using chemCal > 0.2 weights <- with(massart97ex3, { @@ -45,10 +52,6 @@ loq(m3.means, 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/rl95_cadmium.Rd b/man/rl95_cadmium.Rd index 7ee4222..8e0b02c 100644 --- a/man/rl95_cadmium.Rd +++ b/man/rl95_cadmium.Rd @@ -1,16 +1,19 @@ -\name{rl95_cadmium} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/chemCal-package.R \docType{data} +\name{rl95_cadmium} \alias{rl95_cadmium} -\title{Cadmium concentrations measured by AAS as reported by Rocke and Lorenzato (1995)} -\description{ - Dataset reproduced from Table 1 in Rocke and Lorenzato (1995). -} +\title{Cadmium concentrations measured by AAS as reported by Rocke and Lorenzato +(1995)} \format{ - A dataframe containing four replicate observations for each - of the six calibration standards. +A dataframe containing four replicate observations for each of the +six calibration standards. } \source{ - Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for - measurement error in analytical chemistry. Technometrics 37(2), 176-184. +Rocke, David M. und Lorenzato, Stefan (1995) A two-component model +for measurement error in analytical chemistry. Technometrics 37(2), 176-184. +} +\description{ +Dataset reproduced from Table 1 in Rocke and Lorenzato (1995). } \keyword{datasets} diff --git a/man/rl95_toluene.Rd b/man/rl95_toluene.Rd index 21fea0f..1f8836a 100644 --- a/man/rl95_toluene.Rd +++ b/man/rl95_toluene.Rd @@ -1,18 +1,20 @@ -\name{rl95_toluene} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/chemCal-package.R \docType{data} +\name{rl95_toluene} \alias{rl95_toluene} \title{Toluene amounts measured by GC/MS as reported by Rocke and Lorenzato (1995)} -\description{ - Dataset reproduced from Table 4 in Rocke and Lorenzato (1995). The toluene - amount in the calibration samples is given in picograms per 100 µL. - Presumably this is the volume that was injected into the instrument. -} \format{ - A dataframe containing four replicate observations for each - of the six calibration standards. +A dataframe containing four replicate observations for each of the +six calibration standards. } \source{ - Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for - measurement error in analytical chemistry. Technometrics 37(2), 176-184. +Rocke, David M. und Lorenzato, Stefan (1995) A two-component model +for measurement error in analytical chemistry. Technometrics 37(2), 176-184. +} +\description{ +Dataset reproduced from Table 4 in Rocke and Lorenzato (1995). The toluene +amount in the calibration samples is given in picograms per 100 µL. +Presumably this is the volume that was injected into the instrument. } \keyword{datasets} diff --git a/man/utstats14.Rd b/man/utstats14.Rd index ec41bd5..1b739d4 100644 --- a/man/utstats14.Rd +++ b/man/utstats14.Rd @@ -1,18 +1,21 @@ -\name{utstats14} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/chemCal-package.R \docType{data} +\name{utstats14} \alias{utstats14} \title{Example data for calibration with replicates from University of Toronto} -\description{ - Dataset read into R from - \url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/files/example14.xls}. -} \format{ - A tibble containing three replicate observations of the response for five - calibration concentrations. +A tibble containing three replicate observations of the response for +five calibration concentrations. } \source{ - David Stone and Jon Ellis (2011) Statistics in Analytical Chemistry. Tutorial website - maintained by the Departments of Chemistry, University of Toronto. - \url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/index.html} +David Stone and Jon Ellis (2011) Statistics in Analytical Chemistry. +Tutorial website maintained by the Departments of Chemistry, University of +Toronto. +\url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/index.html} +} +\description{ +Dataset read into R from +\url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/files/example14.xls}. } \keyword{datasets} |