From f4fcef8228ebd5a1a73bc6edc47b5efa259c2e20 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 23 Mar 2022 10:32:36 +0100 Subject: Use 'investr' conditionally in tests, updates Most prominently, a README was added, giving a nice overview for the people visiting the github page, the package page on CRAN, or the online docs at pkgdown.jrwb.de. The maintainer e-mail address was also updated. --- docs/index.html | 159 +++++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 129 insertions(+), 30 deletions(-) (limited to 'docs/index.html') diff --git a/docs/index.html b/docs/index.html index a724173..4019484 100644 --- a/docs/index.html +++ b/docs/index.html @@ -25,6 +25,8 @@ + +
-
-

-chemCal - Calibration functions for analytical chemistry

+
+

chemCal - Calibration functions for analytical chemistry +

-

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, get the official chemCal release using

+
+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 +

+
+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.

+
+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.

+
+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).

+
+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.

+
+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.

+
+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.

+