Timeline for Manually calculated $R^2$ doesn't match up with randomForest() $R^2$ for testing new data
Current License: CC BY-SA 2.5
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 16, 2020 at 13:34 | comment | added | igorkf |
If you do help(randomForest), it says: "rss: (regression only) “pseudo R-squared”: 1 - mse / Var(y)."
|
|
| Jul 16, 2019 at 22:07 | comment | added | Eric | If the R-squared value is negative from the instrumental variable regression results, is there a way to supress this negative value and translate into a positive value for the sake of reporting? Refer to this link please: stata.com/support/faqs/statistics/two-stage-least-squares | |
| Feb 18, 2011 at 21:25 | comment | added | Stephen Turner | If you have a reference other than the Seber/Lee textbook (not accessible to me) I would love to see a good explanation of how variation explained (i.e. 1-SSerr/SStot) differs from the squared correlation coefficient, or variance explained. Thanks again for the tip. | |
| Feb 18, 2011 at 20:13 | vote | accept | Stephen Turner | ||
| Feb 18, 2011 at 13:26 | comment | added | cardinal | @mpiktas, @chl, I'll try to expand on this a little more later today. Basically, there's a close (but, perhaps, slightly hidden) connection to hypothesis testing in the background. Even in a linear regression setting, if the constant vector is not in the column space of the design matrix, then the "correlation" definition will fail. | |
| Feb 18, 2011 at 9:14 | comment | added | chl | (+1) Very elegant response, indeed. | |
| Feb 18, 2011 at 8:22 | comment | added | mpiktas | +1, great answer. I always wondered why the original formula is used for $R^2$ instead of square of correlation. For linear regression it is the same, but when applied to other contexts it is always confusing. | |
| Feb 18, 2011 at 4:21 | history | edited | cardinal | CC BY-SA 2.5 |
added 2 characters in body
|
| Feb 18, 2011 at 3:31 | history | answered | cardinal | CC BY-SA 2.5 |