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---
output: github_document
---
<!-- README.md is generated from README.rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# chemCal - Calibration functions for analytical chemistry
<!-- badges: start -->
[](https://cran.r-project.org/package=chemCal)
[](https://app.travis-ci.com/github/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.
## Installation
From within [R][r-project], get the official chemCal release using
```{r, eval = FALSE}
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
```{r calplot}
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.
```{r}
lod(m0)
```
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}
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).
```{r}
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`.
```{r}
inverse.predict(m0, 90)
```
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))
```
## 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/
|
