DR {BivRegBLS} | R Documentation |

## 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 λ 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). |

### 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.

### 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 (β, α). |

`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. |

### Author(s)

Bernard G FRANCQ

### 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

### Examples

```
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)
```

*BivRegBLS*version 1.1.1 Index]