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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