Python implementation of the R package IRR
Python implementation of the R package IRR, all credit goes to the original authors . The package contains functions to calculate coefficients of Interrater Reliability and Agreement for interval, ordinal and nominal data: intraclass-correlations, Finn-Coefficient, Robinson’s A, Kendall’s W, Cohen’s Kappa, and others. This is a straight line-for-line port from the R-package, so it is not particularly Pythonic and mainly made as an exercise to learn more about R. For documentation I highly recommend you head over to the R package page, they put in a lot of effort for the documentation!
How to install
The package is available on the Python Package Index (PyPI). To install it you can run:
pip install pyirr
How to use
A simple example:
from pyirr import read_data, intraclass_correlation data = read_data("anxiety") # loads example data intraclass_correlation(data, "twoway", "agreement")
================================================== Intraclass Correlation Results ================================================== Model: twoway Type: agreement Subjects = 20 Raters = 3 ICC(A,1) = 0.20 F-Test, H0: r0 = 0 ; H1 : r0 > 0 F(19.00,39.75) = 1.83, p = 0.0543 95%-Confidence Interval for ICC Population Values: -0.039 < ICC < 0.494 ==================================================
Another simple example:
from pyirr import read_data, kappam_fleiss data = read_data("anxiety") # loads example data kappam_fleiss(data, detail=True)
================================================== Fleiss` Kappa for m Raters ================================================== Subjects = 30 Raters = 6 Kappa = 0.430 z = 17.652 p-value = 0.000 Kappa z p.value 1. Depression 0.245 5.192 0.0 2. Personality Disorder 0.245 5.192 0.0 3. Schizophrenia 0.520 11.031 0.0 4. Neurosis 0.471 9.994 0.0 5. Other 0.566 12.009 0.0 ==================================================
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