Timeline for How to get an R-squared for a loess fit?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Sep 16, 2014 at 10:41 | comment | added | russellpierce | Would it be more conceptually sound to calculate the $ r^2$ from a GAM? | |
| Mar 28, 2012 at 11:26 | vote | accept | Yuriy Petrovskiy | ||
| Mar 22, 2012 at 14:06 | comment | added | whuber♦ | Not necessarily "more accurate." Indeed, using Loess to achieve accuracy in a predictive model would be foolhardy. I think referring to Loess as a "model" conveys a possible misunderstanding about how it works and how it is intended to be used: it is really a graphical, exploratory tool to help see patterns and trends. Because it is really just a moving-window smoother, it acts as a fairly complicated spatial neighborhood model in which the fitted value at a point depends on which neighboring points exist in the dataset and on the values there. | |
| Mar 22, 2012 at 6:11 | comment | added | Yuriy Petrovskiy | @whuber: So it will be better (more accurate) to use polynominal (or other) model with $r^2$ supported when to use loess model if I need to get how good resulting model describes source data? | |
| Mar 22, 2012 at 3:24 | comment | added | whuber♦ | Your last line is correct: computing a pseudo-$R^2$ is contrary to the spirit of Loess, which is to explore, identify patterns, and smooth data. Computing a measure like this misses the point and, IMHO, is an abuse of the tool. Instead, if you want to assess the fit, continue in the spirit of EDA and analyze the residuals (the "rough" in Tukey's language). Although you might wind up looking at m-letter statistics, IQRs, etc., which could be construed as serving in a role a bit like $R^2$, the analysis proceeds in an entirely different spirit. | |
| Mar 21, 2012 at 21:42 | history | answered | chl | CC BY-SA 3.0 |