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I have started looking into robust methods of data analysis. In particular, robust regression methods for ANCOVA type models using the "lmRob" function from the "robust" library in R. I'm having a hard time finding what assumptions I need to satisfy in order to use this. I've read that they can be useful in situations where there are outliers, heterogeneity of variance and for non-normal distributions, i.e., http://psych.colorado.edu/~willcutt/res_meth/Wilcox_1998.pdf (more recently http://www.springerlink.com/content/x761787633512440/). However, I'm unsure of how much departure robust measures will allow for and what other traps might be awaiting me.
References and/or examples will be much appreciated.

Data description:
sample size ~ 1000, differs substantially between groups
1st factor has 3 levels; 2nd factor has 3 levels
3 covariates, 1 of which is related to both factors, 1 related to only one factor

The model I'm especially interested in examining is something like this:

lmr1 <- lmRob(dv ~ cov1 + cov2 + cov3 + fac1 * fac2, data)
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That the residuals from the good part of the data are approximately symmetric, other than that you're safe! PS: you might wanna link to a more recent article, there as been gigantic leaps in computational tractability in this filed in the last 10 years (specially in higher dimensions).... –  user603 Oct 28 '11 at 7:06
    
Thanks. Will look for a more recent paper to update the question. –  Matt Albrecht Oct 31 '11 at 4:32
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