[RsR] robust regression and fixed effects models

[Martin Maechler]

>>>>> michael westphal via R-SIG-Robust <r-sig-robust using r-project.org>
>>>>>     on Wed, 10 Jun 2015 14:17:57 +0000 writes:

    > Hello: 
    > I am using R 3.0.2.   

so you really should upgrade {unless you meant 3.2.0}... at
least in a few days when  R 3.2.1 is released.
     

    > I have panel data on countries' renewable energy net generation (and installed capacity) over time.  I am regressing these dependent variables on various socioeconomic variables, as well as binary policy variables.  I have have done basic OLS, but I wanted to explore both fixed effects models, as there are likely significant country effects and robust regression, as Q-Q plots indicate that there are some strong outliers.  This might be a question of apples and oranges, but how do I compare the goodness of fit of the fixed effects models with the robust regression models?  
    > Any help would be appreciated. 

Package  robustbase  which has function  lmrob()  with many good
and modern options for robust regression
*also* has a 'Suggests: fit.models' in its own description file,

because the package 'fit.models' with its function fit.models()
tries to take fits of basically the same model and
produce "comparison output" from that.

It's quite useful in situations like yours,
and I plan to add an example of its use to the 'robustbase'
package documentation.



    > [[alternative HTML version deleted]]

  ((Because you used "HTML" aka "rich text" / "formatted text"
    instead of simple plain text, your message ends up looking so
    messy as above ...))



Martin Maechler,
ETH Zurich