Timeline for How to find correlations of the features in a dataframe? The features are of mixed types (nominal, ordinal, discrete, and continuous)
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 8 at 14:24 | comment | added | Peter Flom | I prefer condition indexes for collinearity. PCA doesn't do it. VIFs are OK. (My dissertation compared VIF and condition indexes and favored the latter). For correlations, you could do one matrix (just with different correlations) or multiple matrices. For nominal-nominal you could use lambda. Also see the association measures tag stats.stackexchange.com/questions/tagged/association-measure | |
| Jan 8 at 13:44 | comment | added | letdatado | Coming back to correlations, does it mean that I should plot two correlation matrices. One for my continuous variables and other for my ordinal variables. And for nominal ones, I should do something else. Please advice that as well | |
| Jan 8 at 13:43 | comment | added | letdatado | VIF isn't standardized like correlations. In correlation I know lowest and highest values so its kinda more helpful. I know it can only handle colinearity (between pairs). To handle MC, I definitely go for VIF, or do a PCA. Is that correct? | |
| Jan 8 at 13:40 | history | answered | Peter Flom | CC BY-SA 4.0 |