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Have you considered adding support for sampling weights to the package? This would help when dealing with weighted survey samples, especially with the MCA (since most surveys consist of multiple choice questions).
Thanks!
The text was updated successfully, but these errors were encountered:
Hey there. Yes, this is very relevant. It's a reasonably big undertaking though. It took me time to figure out and test the current non-weighted implementations. But I'm sure it's doable. One would have to start with PCA, then CA, then MCA.
The following might (or might not) be helpful to start:
Mathematical notation and some examples of how to implement weighted and eigenvalue PCA, relying only on numpy and scikit-learn. However, there is nothing for CA and MCA, and I believe (but don't quote me on this) that it is possible to conduct WPCA without repeating rows, since it is possible to calculate weighted variance without repeating rows. https://github.com/nogilnick/WeightedPCA
Let me know if there's anything else that could help!
Have you considered adding support for sampling weights to the package? This would help when dealing with weighted survey samples, especially with the MCA (since most surveys consist of multiple choice questions).
Thanks!
The text was updated successfully, but these errors were encountered: