# LinCal-package: Static Univariate Frequentist and Bayesian Linear Calibration In LinCal: Static Univariate Frequentist and Bayesian Linear Calibration

## Description

A collection of R functions for conducting linear statistical calibration.

## Details

 Package: LinCal Type: Package Version: 1.0 Date: 2014-11-06 License: GPL-2

## Author(s)

Derick L. Rivers and Edward L. Boone

Maintainer: Derick L. Rivers <riversdl@vcu.edu>

## References

Eisenhart, C. (1939). The interpretation of certain regression methods and their use in biological and industrial research. Annals of Mathematical Statistics. 10, 162-186.

Krutchkoff, R. G. (1967). Classical and Inverse Regression Methods of Calibration. Technometrics. 9, 425-439.

Hoadley, B. (1970). A Bayesian look at Inverse Linear Regression. Journal of the American Statistical Association. 65, 356-369.

Hunter, W., and Lamboy, W. (1981). A Bayesian Analysis of the Linear Calibration Problem. Technometrics. 3, 323-328.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```library(LinCal) data(wheat) plot(wheat[,6],wheat[,2]) ## Classical Approach class.calib(wheat[,6],wheat[,2],0.05,105) ## Inverse Approach inver.calib(wheat[,6],wheat[,2],0.05,105) ## Bayesian Inverse Approach hoad.calib(wheat[,6],wheat[,2],0.05,105) ##Bayesian Classical Approach huntlam.calib(wheat[,6],wheat[,2],0.05,105) ```

### Example output

```\$x.pre
(Intercept)
11.09548

\$lim
[,1]     [,2]
[1,] 10.66133 11.52824

\$x.pre
(Intercept)
11.1027

\$lim
[,1]     [,2]
[1,] 10.70394 11.50146

\$x.pre
[1] 11.1027

\$sd
[1] 0.318569

\$lim
lower    upper
[1,] 10.67973 11.52567

\$x.pre
(Intercept)
11.09548

\$sd
x
0.3256772

\$lim
[,1]     [,2]
[1,] 10.55979 11.63117
```

LinCal documentation built on May 1, 2019, 10:11 p.m.