From d202b127909669c484bc74bb87d629f9e3bea299 Mon Sep 17 00:00:00 2001 From: Ranke Johannes Date: Mon, 22 Jul 2024 18:38:23 +0200 Subject: Update travis build status badge --- README.html | 174 +++++++++++++++++++++++++++++++++++++----------------------- README.md | 84 +++++++++++++---------------- README.rmd | 2 +- build.log | 2 +- 4 files changed, 148 insertions(+), 114 deletions(-) diff --git a/README.html b/README.html index 3bb6cbd..3d9793c 100644 --- a/README.html +++ b/README.html @@ -587,91 +587,135 @@ code .in { color: #008080; } + -

chemCal - Calibration functions for analytical chemistry

chemCal - +Calibration functions for analytical chemistry

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, get the official chemCal release using

install.packages("chemCal")

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:

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.

diff --git a/README.md b/README.md index b7b4db6..fcb931f 100644 --- a/README.md +++ b/README.md @@ -1,43 +1,39 @@ ---- -output: github_document ---- - - # 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) +[![Build +Status](https://app.travis-ci.com/jranke/chemCal.svg?token=Sq9VuYWyRz2FbBLxu6DK&branch=main)](https://app.travis-ci.com/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. +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)) #> $Prediction #> [1] 43.93983 @@ -136,8 +129,5 @@ inverse.predict(m0, c(91, 89, 87, 93, 90)) ## Reference -You can use the R help system to view documentation, or you can -have a look at the [online documentation][pd-site]. - -[r-project]: https://www.r-project.org/ -[pd-site]: https://pkgdown.jrwb.de/chemCal/ +You can use the R help system to view documentation, or you can have a +look at the [online documentation](https://pkgdown.jrwb.de/chemCal/). diff --git a/README.rmd b/README.rmd index 361b4c0..5acc1a6 100644 --- a/README.rmd +++ b/README.rmd @@ -16,7 +16,7 @@ knitr::opts_chunk$set( [![](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) +[![Build Status](https://app.travis-ci.com/jranke/chemCal.svg?token=Sq9VuYWyRz2FbBLxu6DK&branch=main)](https://app.travis-ci.com/jranke/chemCal) [![codecov](https://codecov.io/github/jranke/chemCal/branch/master/graphs/badge.svg)](https://codecov.io/github/jranke/chemCal) diff --git a/build.log b/build.log index 2fe8e6c..14f0a29 100644 --- a/build.log +++ b/build.log @@ -6,5 +6,5 @@ * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories * re-saving image files -* building ‘chemCal_0.2.3.tar.gz’ +* building ‘chemCal_0.2.3.9000.tar.gz’ -- cgit v1.2.1