--- output: github_document --- ```{r, echo = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-" ) ``` # chemCal - Calibration functions for analytical chemistry [![](https://www.r-pkg.org/badges/version/chemCal)](https://cran.r-project.org/package=chemCal) [![Build Status](https://travis-ci.com/jranke/chemCal.svg?branch=master)](https://app.travis-ci.com/github/jranke/chemCal) [![codecov](https://codecov.io/github/jranke/chemCal/branch/master/graphs/badge.svg)](https://codecov.io/github/jranke/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, eval = FALSE} 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 calplot} 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) ``` 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) ``` 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) ``` ### 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) ``` 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)) ``` ## 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://www.r-project.org/ [pd-site]: https://pkgdown.jrwb.de/chemCal/