| Title: | Static Univariate Frequentist and Bayesian Linear Calibration |
|---|---|
| Description: | Estimate and confidence/credible intervals for an unknown regressor x0 given an observed y0. |
| Authors: | Derick L. Rivers <[email protected]> and Edward L. Boone |
| Maintainer: | Derick L. Rivers <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.1 |
| Built: | 2024-10-31 20:43:28 UTC |
| Source: | CRAN |
A collection of R functions for conducting linear statistical calibration.
| Package: | LinCal |
| Type: | Package |
| Version: | 1.0.1 |
| Date: | 2022-04-27 |
| License: | GPL-2 |
Derick L. Rivers and Edward L. Boone
Maintainer: Derick L. Rivers <[email protected]>
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.
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)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)
class.calib uses the classical frequentist approach to estimate an unknown X given observed vector y0 and calculates confidence interval estimates.
class.calib(x, y, alpha, y0)class.calib(x, y, alpha, y0)
x |
numerical vector of regressor measurments |
y |
numerical vector of observation measurements |
alpha |
the confidence interval to be calculated |
y0 |
vector of observed calibration value |
Eisenhart, C. (1939). The interpretation of certain regression methods and their use in biological and industrial research. Annals of Mathematical Statistics. 10, 162-186.
X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) class.calib(X,Y,0.05,6)X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) class.calib(X,Y,0.05,6)
hoad.calib uses an inverse Bayesian approach to estimate an unknown X given observed vector y0 and calculates credible interval estimates.
hoad.calib(x, y, alpha, y0)hoad.calib(x, y, alpha, y0)
x |
numerical vector of regressor measurments |
y |
numerical vector of observation measurements |
alpha |
the confidence interval to be calculated |
y0 |
vector of observed calibration value |
Hoadley, B. (1970). A Bayesian look at Inverse Linear Regression. Journal of the American Statistical Association. 65, 356-369.
X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) hoad.calib(X,Y,0.05,6)X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) hoad.calib(X,Y,0.05,6)
huntlam.calib uses the classical Bayesian approach to estimate an unknown X given observed vector y0 and calculates credible interval estimates.
huntlam.calib(x, y, alpha, y0)huntlam.calib(x, y, alpha, y0)
x |
numerical vector of regressor measurments |
y |
numerical vector of observation measurements |
alpha |
the confidence interval to be calculated |
y0 |
vector of observed calibration value |
Hunter, W., and Lamboy, W. (1981). A Bayesian Analysis of the Linear Calibration Problem. Technometrics. 3, 323-328.
X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) huntlam.calib(X,Y,0.05,6)X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) huntlam.calib(X,Y,0.05,6)
inver.calib uses the inverse frequentist approach to estimate an unknown X given observed vector y0 and calculates confidence interval estimates.
inver.calib(x, y, alpha, y0)inver.calib(x, y, alpha, y0)
x |
numerical vector of regressor measurments |
y |
numerical vector of observation measurements |
alpha |
the confidence interval to be calculated |
y0 |
vector of observed calibration value |
Krutchkoff, R. G. (1967). Classical and Inverse Regression Methods of Calibration. Technometrics. 9, 425-439.
X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) inver.calib(X,Y,0.05,6)X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6) inver.calib(X,Y,0.05,6)
A dataset containing 21 samples of hard wheat. The variables are as follows:
data("wheat")data("wheat")
A data frame with 21 observations on the following 6 variables.
Y1infrared reflectance vector
Y2infrared reflectance vector
Y3infrared reflectance vector
Y4infrared reflectance vector
X1percentage water vector
X2percentage protein vector
Brown, P. J. (1982). Multivariate calibration. Journal of the Royal Statistical Society B. 44, 287-321.
data(wheat) ## maybe str(wheat) ; plot(wheat) ...data(wheat) ## maybe str(wheat) ; plot(wheat) ...