From orsolyaaa at freemail.hu  Sun Mar 22 13:02:05 2009
From: orsolyaaa at freemail.hu (vincze orsolya)
Date: Sun, 22 Mar 2009 13:02:05 +0100 (CET)
Subject: [Rd] multicollinearity
Message-ID: <freemail.20090222130205.14908@fm23.freemail.hu>

Dear R users,

I'm analysing some data, and I'm using an lme function.
 I have a problem with choosing the  right order for three of my explanatory variables, which shows collinearity. Is there any rules to make the decision?(r.squared?) Or it's better if I choose the order,  that I think gives me more information about the data?
 
Say x1 is the variable with the highest r.squared, x3 is with the lowest.
If i use
      m1=lme(y~x1+x2+x3,...)
 x2, and x3 is not significant,

 but if i use 
       m2=lme(y~x2+x3+x1, ...) 
all of the 3 variable is significant.

 I would prefer the the m2, because it gives me more ionformation about the dat, but in this case I have to leave in the model x2 and x3, which causes the increase in AIC.

What's the solution?
Can anybody help me?

Cheers




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[Rd] multicollinearity
vincze orsolya