[R-sig-ME] LME and nonlinearity?

[John Maindonald]

Additional to the comments below: Think/check also whether  
transformation of one or more of the variables (log transformation?)  
makes the relationship more nearly linear.

On 10/04/2009, at 7:13 AM, Balázs Lestár wrote:

> Dear All,
>
> I have a mixed model (LME), but one of my explanatory variables is  
> not linearly related to the dependent variable.
>
> 1.)     Somebody told me, to make a 2 or 3 level factor from the  
> continuous variable. (I wouldn't prefer this)

In general, this makes poor use of the information in the data.  You  
lose power.

> 2.)     I saw in some statistical books that in these cases, I have  
> to use in the model the quadratic term of the variable. (but the AIC  
> is much greater than with the  factorized variable)
>
> OR
>
> Is that possible, to use a poly() function in the lme? (this model  
> seems to be the best, based on AIC).

Yes.

> I'm a bit confused, 'cause the LME supposes linear relation between  
> variables. Isn't it right?

Linear models are linear in the parameters.  They can model highly  
nonlinear effects.

> 3.)        Do I need a non-linear model?

Only if you need a model that is non-linear in the parameters.   
Without checking out your data and model, one cannot say.


John Maindonald             email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.




> Which solution is the best?
>
>
> Regards,
> Balazs
>
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