Timeline for Can principal component analysis be applied to datasets containing a mix of continuous and categorical variables?
Current License: CC BY-SA 4.0
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| Nov 19, 2020 at 10:01 | history | edited | Nick Cox | CC BY-SA 4.0 |
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| Dec 13, 2015 at 19:32 | comment | added | Mandar | thnak you @ttnphns. I agree to your point about the binary variables as for binary variable, any assumption does not matter. Otherwise I was actually referring to a book chapter from "Introduction to nonlinear PCA" [link] (openaccess.leidenuniv.nl/bitstream/handle/1887/12386/…). It refers to CATPCA mainly and PRINQUAL packages from SAS. | |
| Dec 13, 2015 at 16:14 | comment | added | ttnphns | Welcoming your answer, Mandar. Are you referring to nonlinear PCA by CATPCA method or another nonlinear PCA (what method, then?). Note also that for binary variables, CATPCA is, say, useless or trivial because a the dichotomous scale cannot be quantified other than to... dichotomous! | |
| Dec 13, 2015 at 14:32 | review | Late answers | |||
| Dec 13, 2015 at 16:27 | |||||
| Dec 13, 2015 at 14:17 | history | answered | Mandar | CC BY-SA 3.0 |