# chemCal - Calibration functions for analytical chemistry [](https://cran.r-project.org/package=chemCal) [](https://app.travis-ci.com/jranke/chemCal) [](https://app.codecov.io/gh/jranke/chemCal) [](https://github.com/jranke/chemCal/actions/workflows/R-CMD-check.yaml) ## 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](https://www.r-project.org/), 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](https://pkgdown.jrwb.de/chemCal/).