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@@ -1,10 +1,143 @@ +--- +output: github_document +--- + +<!-- README.md is generated from README.rmd. Please edit that file --> + + + # chemCal - Calibration functions for analytical chemistry <!-- badges: start --> [](https://cran.r-project.org/package=chemCal) -[](https://travis-ci.com/jranke/chemCal) -[](https://codecov.io/github/jranke/chemCal) +[](https://app.travis-ci.com/github/jranke/chemCal) +[](https://codecov.io/github/jranke/chemCal) <!-- badges: end --> -Static documentation of this R package can be found at -https://pkgdown.jrwb.de/chemCal +## Overview + +chemCal is an R package providing some basic functions for conveniently working +with linear calibration curves with one explanatory variable. + +## Installation + +From within [R][r-project], get the official chemCal release using + + +```r +install.packages("chemCal") +``` + +## Usage + +chemCal works with univariate linear models of class `lm`. Working with one of +the datasets coming with chemCal, we can produce a calibration plot using the +`calplot` function: + +### Plotting a calibration + + +```r +library(chemCal) +m0 <- lm(y ~ x, data = massart97ex3) +calplot(m0) +``` + +<!-- --> + +### LOD and LOQ + +If you use unweighted regression, as in the above example, we can calculate a +Limit Of Detection (LOD) from the calibration data. + + +```r +lod(m0) +#> $x +#> [1] 5.407085 +#> +#> $y +#> [1] 13.63911 +``` +This is the minimum detectable value (German: Erfassungsgrenze), i.e. the +value where the probability that the signal is not detected although the +analyte is present is below a specified error tolerance beta (default is 0.05 +following the IUPAC recommendation). + +You can also calculate the decision limit (German: Nachweisgrenze), i.e. +the value that is significantly different from the blank signal +with an error tolerance alpha (default is 0.05, again following +IUPAC recommendations) by setting beta to 0.5. + + +```r +lod(m0, beta = 0.5) +#> $x +#> [1] 2.720388 +#> +#> $y +#> [1] 8.314841 +``` + +Furthermore, you can calculate the Limit Of Quantification (LOQ), being +defined as the value where the relative error of the quantification given the +calibration model reaches a prespecified value (default is 1/3). + + +```r +loq(m0) +#> $x +#> [1] 9.627349 +#> +#> $y +#> [1] 22.00246 +``` + +### Confidence intervals for measured values + +Finally, you can get a confidence interval for the values +measured using the calibration curve, i.e. for the inverse +predictions using the function `inverse.predict`. + + +```r +inverse.predict(m0, 90) +#> $Prediction +#> [1] 43.93983 +#> +#> $`Standard Error` +#> [1] 1.576985 +#> +#> $Confidence +#> [1] 3.230307 +#> +#> $`Confidence Limits` +#> [1] 40.70952 47.17014 +``` + +If you have replicate measurements of the same sample, +you can also give a vector of numbers. + + +```r +inverse.predict(m0, c(91, 89, 87, 93, 90)) +#> $Prediction +#> [1] 43.93983 +#> +#> $`Standard Error` +#> [1] 0.796884 +#> +#> $Confidence +#> [1] 1.632343 +#> +#> $`Confidence Limits` +#> [1] 42.30749 45.57217 +``` + +## Reference + +You can use the R help system to view documentation, or you can +have a look at the [online documentation][pd-site]. + +[r-project]: https://r-project.org +[pd-site]: https://pkgdown.jrwb.de/chemCal |