On 12 May 2013 at 13:56, Peter Teuben wrote: | Patrick | I agree, this is a useful option! | | can you say a little more here how you define robustness. The one I | know takes the quartiles Q1 and Q3 (where Q2 would | be the median), then define D=Q3-Q1 and only uses points between | Q1-1.5*D and Q3+1.5*D to define things like a robust mean and variance. | Why 1.5 I don't know, I guess you could keep that a variable and tinker | with it. | For OLS you can imagine applying this in an iterative way to the Y | values, since formally the errors in X are neglibable compared to those | in Y. I'm saying iterative, since in theory the 2nd iteration could have | rejected points that should have | been part or the "core points". For non-linear fitting this could be a | lot more tricky.
There is an entire "task view" (ie edited survey of available packages) available for R concerning robust methods (for model fitting and more): http://cran.r-project.org/web/views/Robust.html So there is not just one generally accepted best option. That said, having something is clearly better than nothing. But let's properly define the method and delineat its scope/ Dirk -- Dirk Eddelbuettel | [email protected] | http://dirk.eddelbuettel.com
