Solved – Logistic Regression: multicollinearity and Kappa statistics

caretlogisticmulticollinearityrregression

I may be wrong but from my understanding logistic regression requires there to be little or no multicollinearity among the independent variables, and yet Kappa statistics as part of postResample() function in caret library (r) is a measure of reliability of the model. If we all agree that in Kappa 0 represents the amount of agreement that can be expected from random chance and that with 0 our model accuracy test is inconclusive, how can we start from the assumption of little or no multicollinearity among independent variables? Have I misread the entire model and failed to understand its basic pillars? Any useful answer is highly appreciated. Thanks

Best Answer

Kappa is a measure of inter-rater agreement. Kappa is 0 when

Rating 1: 1, 2, 3, 2, 1

Rating 2: 0, 1, 2, 1, 0

because the two do not agree at all. But the two ratings have a correlation of 1.0, because they perfectly covary.

Multicollinearity is about the correlation (or covariance) among X's. Kappa is irrelevant here.

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