Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

pyCompare

Build Status codecov PyPI - Python Version DOI PyPI Binder

A Python module for generating Bland-Altman plots to compare two sets of measurements.

You can try out the code using Binder.

Installation

To install via pip, run:

pip install pyCompare

Installation with pip allows the usage of the uninstall command:

pip uninstall pyCompare

Documentation

blandAltman( )

blandAltman(data1, data2,
            limitOfAgreement=1.96,
            confidenceInterval=95,
            confidenceIntervalMethod='approximate',
            detrend=None,
            percentage=False,
            **kwargs)

Generate a Bland-Altman plot to compare two sets of measurements of the same value.

data1 and data2 should be 1D numpy arrays of equal length containing the paired measurements.

If not None plot confidence interval over the x% range with confidenceInterval=x

Confidence intervals on the mean difference and limit of agreement may be calculated using:

  • 'exact paired' uses the exact paired method described by Carkeet
  • 'approximate' uses the approximate method described by Bland & Altman

The 'exact paired' method will give more accurate confidence intervals on the limits of agreement when the number of paired measurements is low (approx < 100), at the expense of much slower plotting time.

The detrend parameter supports the following options:

  • None do not attempt to detrend data - plots raw values
  • 'Linear' attempt to model and remove a multiplicative offset between each assay by linear regression
  • 'ODR' attempt to model and remove a multiplicative offset between each assay by orthogonal distance regression

'ODR' is the recommended method if you do not use None.

When True, the percentage option plots the difference between methods as a percentage, instead of in the units the methods were measured in.

Plots are displayed using the current matplotlib backend by default, or may be saved with the savePath= argument.

When saving, png format graphics are saved by default:

blandAltman(data1, data2,
            savePath='SavedFigure.png')

The save format type can be chosen from those known by matplotlib with the figureFormat= argument:

blandAltman(data1, data2,
            savePath='SavedFigure.svg',
            figureFormat='svg)

References

To cite pyCompare, use the Zendo DOI: 10.5281/zenodo.1238915.

  • Altman, D. G., and Bland, J. M. “Measurement in Medicine: The Analysis of Method Comparison Studies” Journal of the Royal Statistical Society. Series D (The Statistician), vol. 32, no. 3, 1983, pp. 307–317. JSTOR.
  • Altman, D. G., and Bland, J. M. “Measuring agreement in method comparison studies” Statistical Methods in Medical Research, vol. 8, no. 2, 1999, pp. 135–160. DOI.
  • Carkeet, A. "Exact Parametric Confidence Intervals for Bland-Altman Limits of Agreement" Optometry and Vision Science, vol. 92, no 3, 2015, pp. e71–e80 DOI.