Metadata-Version: 2.1
Name: pyCompare
Version: 1.5.1
Summary: Bland-Altman plots for Python
Home-page: https://github.com/jaketmp/pyCompare
Author: Jake TM Pearce
License: MIT
Description: # pyCompare <img src="https://github.com/jaketmp/pyCompare/raw/master/docs/_static/pyCompare.png" width="200" style="max-width: 30%;" align="right" />
        
        [![Build Status](https://travis-ci.org/jaketmp/pyCompare.svg?branch=master)](https://travis-ci.org/jaketmp/pyCompare) [![codecov](https://codecov.io/gh/jaketmp/pyCompare/branch/master/graph/badge.svg)](https://codecov.io/gh/jaketmp/pyCompare) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyCompare.svg) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1238915.svg)](https://doi.org/10.5281/zenodo.1238915) [![PyPI](https://img.shields.io/pypi/v/pyCompare.svg)](https://pypi.org/project/pyCompare/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jaketmp/pyCompare/master?filepath=pyCompare-Demo.ipynb)
        
        
        
        A Python module for generating [Bland-Altman](https://en.wikipedia.org/wiki/Bland–Altman_plot) plots to compare two sets of measurements.
        
        You can try out the code using [Binder](https://mybinder.org/v2/gh/jaketmp/pyCompare/master?filepath=pyCompare-Demo.ipynb).
        
        <img src="https://github.com/jaketmp/pyCompare/raw/master/docs/_static/bland_altman.png" style="max-width: 60%;" align="center" />
        
        ## Installation
        
        To install _via_ [pip](https://pypi.org/project/pyCompare/), run:
        
            pip install pyCompare
        
        Installation with pip allows the usage of the uninstall command:
        
            pip uninstall pyCompare
        
        
        ## Documentation
        
        ### blandAltman(&nbsp;)
        
            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](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html) with the `figureFormat=` argument:
        
            blandAltman(data1, data2,
                        savePath='SavedFigure.svg',
                        figureFormat='svg)
        
        #### References
        
        To cite `pyCompare`, use the Zendo DOI: [10.5281/zenodo.1238915](https://doi.org/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](https://www.jstor.org/stable/2987937).
        - 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](https://doi.org/10.1177/096228029900800204).
        - 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](https://doi.org/10.1097/OPX.0000000000000513).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Description-Content-Type: text/markdown
