From: RenE J.V. Bertin (rjvbertin@hotmail.com)
Date: Fri 28 Jun 2002 - 01:57:42 EST
Message-id: <LAW2-F126eO925B76WE00001d58@hotmail.com>
Hello,
This is an "intuition" question from a "stats-user!=expert". I hope this is
the right place to post it!
I understand that lm() is quite sensitive to data with severe outliers. I
imagine therefore that aov(), which uses lm(), is too. I have tried to hack
aov() such that it calls rlm() which is much more robust against outliers.
Even though the calling conventions for lm() and rlm() are quite similar,
this just doesn't work:
Error in terms.default(formula, data = data) :
no terms component
1) Is such a modification feasible, and does it make sense to use rlm() (or
similar) in an anova?
2) How to do this, or is there already such a function?
Thanks in advance,
RenE J.V. Bertin
College de France/LPPA
11, place Marcelin Berthelot
75005 Paris, France
_________________________________________________________________
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request@stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
This archive was generated by hypermail 2.1.3 : Wed 16 Oct 2002 - 11:57:34 EST