How to combine PCA and MCA on mixed data?

Question

Suppose I have mixed data and (python) code which is capable of doing PCA (principal component analysis) on continuous predictors and MCA (multiple correspondence analysis) on nominal predictors. Is it possible to combine results from PCA and MCA into one?

score 8 · Answer 1 · answered Jan 19 '16 at 13:55

You may want to use Factor analysis of mixed data.

It allows you to do dimension reduction on a complete data set.

A R implementation could be found in the FactoMineR package. But this function struggle when you have a high number of data/columns.

I am not aware of the existence of the equivalent in python.

score 3 · Answer 2 · answered Aug 20 '18 at 15:47

3

You can find FAMD implementation for Python here.

answered Aug 20 '18 at 15:47

Hajar Homayouni

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score 1 · Answer 3 · edited Mar 02 '18 at 14:59

1

I was looking for the same thing in Python and I came the prince package that has FAMD implemented.

edited Mar 02 '18 at 14:59

Stephen Rauch

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answered Mar 02 '18 at 14:42

mendo

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score 1 · Answer 4 · answered Jun 13 '18 at 22:07

I don't have enough points to comment so answering here @Edo prince Package has only CA, MCA and PCA PAckages in it. I do not see any FAMD in here.

dir(prince)

['CA', 'MCA', 'PCA', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', 'ca', 'mca', 'pca', 'plot', 'svd']

How to combine PCA and MCA on mixed data?

4 Answers4