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+#' Calibration data from DIN 32645
+#' 
+#' Sample dataset to test the package.
+#' 
+#' 
+#' @name din32645
+#' @docType data
+#' @format A dataframe containing 10 rows of x and y values.
+#' @references DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994
+#' 
+#' Dintest. Plugin for MS Excel for evaluations of calibration data. Written by
+#' Georg Schmitt, University of Heidelberg. 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.
+#' @keywords datasets
+#' @examples
+#' 
+#' m <- lm(y ~ x, data = din32645)
+#' calplot(m)
+#' 
+#' ## Prediction of x with confidence interval
+#' prediction <- inverse.predict(m, 3500, alpha = 0.01)
+#' 
+#' # This should give 0.07434 according to test data from Dintest, which 
+#' # was collected from Procontrol 3.1 (isomehr GmbH) in this case
+#' round(prediction$Confidence, 5)
+#' 
+#' ## Critical value:
+#' crit <- lod(m, alpha = 0.01, beta = 0.5)
+#' 
+#' # According to DIN 32645, we should get 0.07 for the critical value
+#' # (decision limit, "Nachweisgrenze")
+#' round(crit$x, 2)
+#' # and according to Dintest test data, we should get 0.0698 from
+#' round(crit$x, 4)
+#' 
+#' ## Limit of detection (smallest detectable value given alpha and beta)
+#' # In German, the smallest detectable value is the "Erfassungsgrenze", and we
+#' # should get 0.14 according to DIN, which we achieve by using the method 
+#' # described in it:
+#' lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
+#' round(lod.din$x, 2)
+#' 
+#' ## Limit of quantification
+#' # This accords to the test data coming with the test data from Dintest again, 
+#' # except for the last digits of the value cited for Procontrol 3.1 (0.2121)
+#' loq <- loq(m, alpha = 0.01)
+#' round(loq$x, 4)
+#' 
+#' # A similar value is obtained using the approximation 
+#' # LQ = 3.04 * LC (Currie 1999, p. 120)
+#' 3.04 * lod(m, alpha = 0.01, beta = 0.5)$x
+#' 
+NULL
+
+
+
+
+
+#' Calibration data from Massart et al. (1997), example 1
+#' 
+#' Sample dataset from p. 175 to test the package.
+#' 
+#' 
+#' @name massart97ex1
+#' @docType data
+#' @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.
+#' @keywords datasets
+NULL
+
+
+
+
+
+#' Calibration data from Massart et al. (1997), example 3
+#' 
+#' Sample dataset from p. 188 to test the package.
+#' 
+#' 
+#' @name massart97ex3
+#' @docType data
+#' @format 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.
+#' @keywords datasets
+#' @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, {
+#'   yx <- split(y, x)
+#'   ybar <- sapply(yx, mean)
+#'   s <- round(sapply(yx, sd), digits = 2)
+#'   w <- round(1 / (s^2), digits = 3)
+#' })
+#' 
+#' massart97ex3.means <- aggregate(y ~ x, massart97ex3, mean)
+#' 
+#' m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means)
+#' 
+#' # The following concords with the book p. 200
+#' inverse.predict(m3.means, 15, ws = 1.67)  # 5.9 +- 2.5
+#' inverse.predict(m3.means, 90, ws = 0.145) # 44.1 +- 7.9
+#' 
+#' # The LOD is only calculated for models from unweighted regression
+#' # with this version of chemCal
+#' m0 <- lm(y ~ x, data = massart97ex3) 
+#' lod(m0)
+#' 
+#' # Limit of quantification from unweighted regression
+#' loq(m0)
+#' 
+#' # For calculating the limit of quantification from a model from weighted
+#' # regression, we need to supply weights, internally used for inverse.predict
+#' # If we are not using a variance function, we can use the weight from
+#' # the above example as a first approximation (x = 15 is close to our
+#' # loq approx 14 from above).
+#' loq(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. 
+#' 
+NULL
+
+
+
+
+
+#' Cadmium concentrations measured by AAS as reported by Rocke and Lorenzato
+#' (1995)
+#' 
+#' Dataset reproduced from Table 1 in Rocke and Lorenzato (1995).
+#' 
+#' 
+#' @name rl95_cadmium
+#' @docType data
+#' @format 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.
+#' @keywords datasets
+NULL
+
+
+
+
+
+#' Toluene amounts measured by GC/MS as reported by Rocke and Lorenzato (1995)
+#' 
+#' 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.
+#' 
+#' 
+#' @name rl95_toluene
+#' @docType data
+#' @format 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.
+#' @keywords datasets
+NULL
+
+
+
+
+
+#' Example data for calibration with replicates from University of Toronto
+#' 
+#' Dataset read into R from
+#' \url{https://sites.chem.utoronto.ca/chemistry/coursenotes/analsci/stats/files/example14.xls}.
+#' 
+#' 
+#' @name utstats14
+#' @docType data
+#' @format 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}
+#' @keywords datasets
+NULL
+
+
+