[R-sig-ME] LME and nonlinearity?
Kevin E. Thorpe
kevin.thorpe at utoronto.ca
Fri Apr 10 01:03:11 CEST 2009
John Maindonald wrote:
> 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)
>> Is that possible, to use a poly() function in the lme? (this model
>> seems to be the best, based on AIC).
I also used ns from the splines package recently to handle
non-linearity. It seemed to work like a charm.
>> 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.
>> Which solution is the best?
Kevin E. Thorpe
Biostatistician/Trialist, Knowledge Translation Program
Assistant Professor, Dalla Lana School of Public Health
University of Toronto
email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.6057
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