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@@ -1,43 +1,39 @@ ---- -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://app.travis-ci.com/github/jranke/chemCal) +[](https://app.travis-ci.com/jranke/chemCal) [](https://codecov.io/github/jranke/chemCal) <!-- badges: end --> ## Overview -chemCal is an R package providing some basic functions for conveniently working -with linear calibration curves with one explanatory variable. +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 +From within [R](https://www.r-project.org/), get the official chemCal +release using - -```r +``` 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: +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 +``` r library(chemCal) m0 <- lm(y ~ x, data = massart97ex3) calplot(m0) @@ -47,11 +43,10 @@ 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. - +If you use unweighted regression, as in the above example, we can +calculate a Limit Of Detection (LOD) from the calibration data. -```r +``` r lod(m0) #> $x #> [1] 5.407085 @@ -59,18 +54,18 @@ lod(m0) #> $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. +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 +``` r lod(m0, beta = 0.5) #> $x #> [1] 2.720388 @@ -80,11 +75,11 @@ 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). - +defined as the value where the relative error of the quantification +given the calibration model reaches a prespecified value (default is +1/3). -```r +``` r loq(m0) #> $x #> [1] 9.627349 @@ -95,12 +90,11 @@ 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`. +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 +``` r inverse.predict(m0, 90) #> $Prediction #> [1] 43.93983 @@ -115,11 +109,10 @@ inverse.predict(m0, 90) #> [1] 40.70952 47.17014 ``` -If you have replicate measurements of the same sample, -you can also give a vector of numbers. - +If you have replicate measurements of the same sample, you can also give +a vector of numbers. -```r +``` r inverse.predict(m0, c(91, 89, 87, 93, 90)) #> $Prediction #> [1] 43.93983 @@ -136,8 +129,5 @@ 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/ +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/). |