BivRegBLS (version 1.1.1)

DR: Deming Regression

Description

Estimate the Deming Regression (DR) with unreplicated or replicated data.

Usage

DR(data = NULL, xcol = 1, ycol = 2, ratio.var = NULL, conf.level = 0.95)

Arguments

data

a data set (data frame or matrix).

xcol

a numeric vector to specify the X column(s) or a character vector with the column names.

ycol

a numeric vector to specify the Y column(s) or a character vector with the column names.

ratio.var

a numeric value for the ratio of the measurement error variances (Y over X) if known. Otherwise, it may be estimated with replicated data.

conf.level

a numeric value for the confidence level (expressed between 0 and 1).

Value

A list including the following elements:

Ellipse.DR

a two columns matrix with the coordinates of the joint confidence interval (confidence region, ellipse) for the parameters (, ).

Estimate.DR

a table (data frame) with the estimates of the intercept and the slope, standard error, confidence interval and pvalue (null hypothesis: slope = 1, intercept = 0). The exact confidence interval for the slope is also given.

Details

The BLS regression is more general and includes the Deming Regression. The BLS regression provides more results and should, therefore, be used instead of DR.

References

Francq BG, Govaerts BB. Measurement methods comparison with errors-in-variables regressions. From horizontal to vertical OLS regression, review and new perspectives. Chemometrics and Intelligent Laboratory Systems 2014; 134:123-139. Tan CY, Iglewicz B. Measurement-methods comparisons and linear statistical relationship. Technometrics, 1999; 41(3):192-201.

See Also

BLS

Examples

# NOT RUN {
library(BivRegBLS)
data(SBP)
res.DR=DR(data=SBP,xcol=c("J1","J2","J3"),ycol=8:10)
res.DR$Estimate.DR
data(Aromatics)
res.DR=DR(data=Aromatics,xcol=3,ycol=4,ratio.var=2)
# }