# (Slightly extended) Bland-Altman plots BlandAltmanLeh

### Description

Bland-Altman Plots for assessing agreement between two methods of clinical measurement and returning associated statistics. Plots are optionally extended by confidence intervals as described in "J. Martin Bland, Douglas G. Altman (1986): Statistical Methods For Assessing Agreement Between Two Methods Of Clinical Measurement" but not included in the graphics of that publication. Either base graphics or ggplot2 can be used.

### Details

Package: | BlandAltmanLeh |

Type: | Package |

Version: | 0.3.1 |

Date: | 2015-12-23 |

License: | GPL |

Bland Altman plots are a diagnostic tool for assessing the agreement between two methods of measurement or assessing retest reliability from two measurements. This package offers plots in base and ggplot2 graphics as well as detailed descriptive statistics, thus supporting the construction of individual plots based on Bland Altman plots. Bland and Altman describe a way for constructing confidence intervals. This package computes these confidence intervals and includes them into the plots. It also invents the Sunflower-Bland-Altman plot for data with ties.

### Author(s)

Bernhard Lehnert

Maintainer: Bernhard K. Lehnert <bernhard.lehnert@uni-greifswald.de>

### References

Bland JM, Altman DG, Statistical Methods For Assessing Agreement Between Two Methods Of Clinical Measurement, Lancet, 1986; 307-310.

Altman DG, Bland JM, Measurement in medicine: the analysis of method comparison studies, The Statistician 1983; 32, 307-317.

Vaz S et al., The Case for Using the Repeatability Coeffcient When Calculating Test-Retest Reliability, PLOS ONE, Sept. 2013, Vol 8, Issue 9.

### See Also

`bland.altman.plot,bland.altman.stats`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# simple basic Bland Altman plot
a <- rnorm(40,30,10)
b <- 1.01*a + rnorm(40)
bland.altman.plot(a,b, xlab="mean", ylab="difference")
# to get all the data for further analysis
bland.altman.plot(a,b, xlab="mean", ylab="difference", silent=FALSE)
# to include confidence intervals into the plot
bland.altman.plot(a,b, xlab="mean", ylab="difference", conf.int=.95)
# to plot in ggplot2
bland.altman.plot(a,b, graph.sys="ggplot2")
# to mark ties in a Sunflower-Bland-Altman plot
a <- sample(1:5, 40, replace=TRUE)
b <- rep(c(1,2,3,3,5,5,5,5),5)
bland.altman.plot(a, b, sunflower=TRUE)
``` |