Static Univariate Frequentist and Bayesian Linear Calibration

Description

A collection of R functions for conducting linear statistical calibration.

Details

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

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(LinCal)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LinCal/LinCal-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: LinCal-package
> ### Title: Static Univariate Frequentist and Bayesian Linear Calibration
> ### Aliases: LinCal-package LinCal
> ### Keywords: package
> 
> ### ** Examples
> 
> library(LinCal)
> 
> data(wheat)
> 
> plot(wheat[,6],wheat[,2])
> 
> ## Classical Approach
> class.calib(wheat[,6],wheat[,2],0.05,105)
$x.pre
(Intercept) 
   11.09548 

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

> 
> ## Inverse Approach
> inver.calib(wheat[,6],wheat[,2],0.05,105)
$x.pre
(Intercept) 
    11.1027 

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

> 
> ## Bayesian Inverse Approach
> hoad.calib(wheat[,6],wheat[,2],0.05,105)
$x.pre
[1] 11.1027

$sd
[1] 0.318569

$lim
        lower    upper
[1,] 10.67973 11.52567

> 
> ##Bayesian Classical Approach
> huntlam.calib(wheat[,6],wheat[,2],0.05,105)
$x.pre
(Intercept) 
   11.09548 

$sd
        x 
0.3256772 

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

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>