Timeline for How to identify variable (from many variables) which is able to discriminate between groups?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Oct 29, 2013 at 7:19 | comment | added | Ladislav Naďo | you may earn bounty also... | |
| Oct 29, 2013 at 7:18 | comment | added | Ladislav Naďo | @FairMiles thank you for your comment. I agree that focusing ONLY on p-values is not good and MANOVA is probably appropriate in this particular case. Could you please write an answer in which you will use DCA (in R)? Such answer/example will be very welcome. | |
| Oct 28, 2013 at 22:42 | comment | added | FairMiles | There is no automagic shortcut to dataset knowledge by exploration in cases like this one. And, then, variable selection. With a similar "shape" and assumptions you may start by analyzing paired correlations for redundant variables and single t-tests for uninformative variables. Then try to detect important variables by exploring the main (or first) linear combinations of a (mathematically equivalent to MANOVA, so same assumptions apply) discriminant analysis (DCA) | |
| Oct 28, 2013 at 22:37 | comment | added | FairMiles | I do not agree AT ALL. MANOVA seems appropriate only for the configuration of the problem, but: (1) with 107 variables (and just 98 observations), statistical power will be non-existent, (2) are you prone to assume multivariate normality in a 107-dimensional space?, (3) Strong multicollinearity is expected among 107 variables (even if just random ones), (4) you will have no further knowledge of the variables "causing" group discrimination. Considering, focus in p-values is simply not-right. | |
| Oct 17, 2013 at 16:29 | comment | added | GK89 | Thanks Ladislav, I ran the approach and I also confirmed it with the plots and it worked well. Thanks again | |
| Oct 17, 2013 at 16:29 | vote | accept | GK89 | ||
| Oct 17, 2013 at 8:38 | history | edited | Ladislav Naďo | CC BY-SA 3.0 |
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| Oct 17, 2013 at 8:31 | history | answered | Ladislav Naďo | CC BY-SA 3.0 |