-
Notifications
You must be signed in to change notification settings - Fork 26
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Help with understanding the homogeneity test #11
Comments
First, if you are obtaining such high p-values for clearly distinct distributions, maybe there is a bug in the code or maybe you are calling the method with wrong parameters, because that should not happen. Can you provide an example of how are you using the method? As for the explanation and understanding, the complete procedure is explained in the original article of Székely and Rizzo. I will summarize the method:
|
I will close this as there is no answer from @srujan741. |
I have an experiment wherein i have two groups of customers with the same attributes. I wanted to do a multivariate homogeneity test for this and used the dcor.homogeneity.energy_test() method on both the groups. My question is that i always end up with a p value of 1 or close to 1. I simulated a 2 d dataset in two cases a.) There are two distinct clusters seperated b.) The data clusters are overlapping. The p value in both the cases came out to be 1 although the test statistic value was different. I want to understand how the homogeneity test works? Help is much appreciated.
The text was updated successfully, but these errors were encountered: