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

install.packages("chemCal")

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

library(chemCal)
-m0 <- lm(y ~ x, data = massart97ex3)
-calplot(m0)

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

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.

You can use the R help system to view documentation, or you can have +a look at the online +documentation.

chemCal - Calibration functions for analytical chemistry

chemCal - +Calibration functions for analytical chemistry