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Chance-corrected Agreement Coefficients
I am trying to construct a simple example for annotator agreement using e.g. Fleiss Kappa. I am doing the following:
from irrCAC.raw import CAC
rater_df = pd.DataFrame()
rater_df['annotator1'] = [0,0,0,1,0]
rater_df['annotator2'] = [0,1,0,1,0]
cac2raters = CAC(rater_df)
print(cac2raters.krippendorff())
print(cac2raters.fleiss())
However I keep getting the error rv_generic.interval() missing 1 required positional argument: 'confidence'
no matter what statistic I make use off. What could be going on here?
Custom weights are not supported currently and the documentation does not reflect it. I keep getting:
ValueError: weights values can be any of ('identity', 'quadratic', 'ordinal', 'linear', 'radical', 'ratio', 'circular', 'bipolar')
when I supply an ndarray as a weight.
How should the results be interpreted?
For example, in the following outputs of .gwet
function:
What is the meaning of confidence value, interval, p_value, se, pa, z, etc?
Which of these variables tell me about gwet value?
{'coefficient_value': 0.118, 'coefficient_name': 'AC1', 'confidence_interval': (0.1021, 0.1339), 'p_value': 0.0, 'z': 14.91258, 'se': 0.00791, 'pa': 0.2766, 'pe': 0.17981}, 'weights': array([[1., 0., 0., 0., 0.],
Thank you.
First of all, thank you for porting the package to Python. Much appreciated.
That is not a bug that I have faced, but I noticed that the pa
value for the Kruppendorff's alpha in the Raw Ratings Documentation is different from every other kappa. I also noticed that the formula in the code differs from the others.
My apologies for not having time to assert which formula is right or wrong. Thank you for checking that out.
Best,
Regards
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