[R-sig-ME] Variable selection for a time-series.
alex.bokov at gmail.com
Thu Jun 2 00:13:44 CEST 2011
If I'm fitting a time-series model, for example...
lme(y ~ time + I(time^2) + ... + I(time^n) + x1 + x2 + x3,
random=~time|id, data=mydata, method='LM')
...where x1,x2,x3 are covariates that may or may not be important to
the model, and 'id' is a factor for grouping all the responses
collected from the same subject.
1) Can I rely on stepAIC() to add relevant interactions involving time
and toss out irrelevant time-terms, just like any other covariates?
2) Should I first do variable selection with stepAIC() and then
manually add terms to the random formula, or the other way around?
3) If I add autocorrelation to the model, for example corExp(), should
I give it the same formula as I used for the random effects?
4) After adding autocorrelation, is it necessary to again test the
effect of each existing term to make sure they are still needed by the
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