st: R: How much of variation in dep var is explained by various sets of variables?

["Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>]

Dear Adrian,
As far as I know, in case of linearity (as OLS should imply) this issue can
be addressed via ANCOVA (please, see Briggs A, Sculpher M, Claxton K.
Decision Modelling for Health Economic Evaluation. Oxford: Oxpord University
press, 2006: 130-132).

Kind Regards,
Carlo 

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di kokootchke
Inviato: domenica 4 aprile 2010 2.35
A: statalist
Oggetto: st: How much of variation in dep var is explained by various sets
of variables?

Dear all, 

I would like to know if it's possible to determine how much of the variation
in the dependent variable is explained by different sets of variables. For
instance, suppose I have:

(1) y = a*x1 + b*x2 + c*x3 + d*z1 + d*z2 + d*z3 + d*z4
(2) y = e*x1 + f*x2 + g*x3
(3) y = h*z1 + i*z2 + j*z3 + k*z4

If I run these regressions by OLS, I obtain, say, R-sq = 0.30, 0.20, 0.15,
respectively. Is it possible to determine what percentage of the variation
in y in (1) is explained by the x's and what percentage is explained by the
z's?

I read some of the threads on this issue and I found some notes on partial
correlation and the -pcorr- command. I read Richard Williams's notes and it
seems like you can determine the proportion of the variation in y focusing
on one variable at a time... but I don't know if it's possible to do it by
sets of variables.

Thank you very much for your help.

Best,
Adrian

 		 	   		  
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