From d36f7665da7ed855885bbbcd17b203d3e8804bab Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 18 Nov 2024 19:04:11 +0100 Subject: Update badges in README.rmd --- README.html | 127 +++++++++++++++++++++++++----------------------------------- README.md | 69 +++++++++++++++++++-------------- README.rmd | 4 +- 3 files changed, 94 insertions(+), 106 deletions(-) diff --git a/README.html b/README.html index 3d9793c..c4809fd 100644 --- a/README.html +++ b/README.html @@ -587,12 +587,12 @@ code .in { color: #008080; } @@ -607,28 +607,7 @@ padding-top: 0px; Calibration functions for analytical chemistry -

buildbuildpassingpassing - - - - - - - - - - - - - - codecov - codecov - 53% - 53% - - - -

+

Codecov test coverage R-CMD-check

Overview

@@ -638,25 +617,25 @@ variable.

Installation

From within R, get the official chemCal release using

-
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:

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
+
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 @@ -665,53 +644,53 @@ beta (default is 0.05 following the IUPAC recommendation).

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
+
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
+
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
+
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
+
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 diff --git a/README.md b/README.md index cf11326..ee2568b 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,28 @@ +--- +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://app.travis-ci.com/jranke/chemCal.svg?token=Sq9VuYWyRz2FbBLxu6DK&branch=main)](https://app.travis-ci.com/jranke/chemCal) [![Codecov test coverage](https://codecov.io/gh/jranke/chemCal/graph/badge.svg)](https://app.codecov.io/gh/jranke/chemCal) [![R-CMD-check](https://github.com/jranke/chemCal/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jranke/chemCal/actions/workflows/R-CMD-check.yaml) ## 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](https://www.r-project.org/), get the official chemCal -release using +From within [R][r-project], get the official chemCal release using + ``` r install.packages("chemCal") @@ -28,12 +30,13 @@ 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 library(chemCal) m0 <- lm(y ~ x, data = massart97ex3) @@ -44,8 +47,9 @@ 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 lod(m0) @@ -55,16 +59,16 @@ 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). +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. +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) @@ -76,9 +80,9 @@ 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 loq(m0) @@ -91,9 +95,10 @@ 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 inverse.predict(m0, 90) @@ -110,8 +115,9 @@ 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 inverse.predict(m0, c(91, 89, 87, 93, 90)) @@ -130,5 +136,8 @@ 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](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][pd-site]. + +[r-project]: https://www.r-project.org/ +[pd-site]: https://pkgdown.jrwb.de/chemCal/ diff --git a/README.rmd b/README.rmd index 5acc1a6..b0af5a4 100644 --- a/README.rmd +++ b/README.rmd @@ -16,8 +16,8 @@ knitr::opts_chunk$set( [![](https://www.r-pkg.org/badges/version/chemCal)](https://cran.r-project.org/package=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) +[![Codecov test coverage](https://codecov.io/gh/jranke/chemCal/graph/badge.svg)](https://app.codecov.io/gh/jranke/chemCal) +[![R-CMD-check](https://github.com/jranke/chemCal/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jranke/chemCal/actions/workflows/R-CMD-check.yaml) ## Overview -- cgit v1.2.1