[R-sig-ME] time-series analysis: how to deal with 'nuisance' factors influencing a trend?

[Giancarlo Sadoti]

Thank you David. 

I meant to say "three years is inadequate for the inclusion of an autoregressive or moving-average structure".  Thank you for confirming this. 

My central question relates to the apparent and confounding influence of another characteristic of each site (in this case the mean # of individuals) on the "trend" fixed effect (in this case the change in per-site # of individuals across the three years).  How is the best way to 'control' for this influence in a mixed model in order to get to the 'true' trend?

Thanks for any additional insights.

Giancarlo



--- On Wed, 12/15/10, David Duffy <davidD at qimr.edu.au> wrote:

> From: David Duffy <davidD at qimr.edu.au>
> Subject: Re: [R-sig-ME] time-series analysis: how to deal with 'nuisance'factors influencing a trend?
> To: "Giancarlo Sadoti" <gcsadoti at yahoo.com>
> Cc: r-sig-mixed-models at r-project.org
> Date: Wednesday, December 15, 2010, 11:44 PM
> On Wed, 15 Dec 2010, Giancarlo Sadoti
> wrote:
> 
> > I'm examining longitudinal data of counts [COUNT] of a
> species across three years [YEAR] and across 40 sites
> [SITE].  My primary interest is in the coefficient of
> YEAR to determine if there is a trend across the three
> years.  Counts fit a poisson distribution.  I
> can't cite this, but it is my understanding that three years
> is inadequate for the inclusion of a temporal correlation
> structure, so I am not including it.
> 
> As I understand it, 3 years is not enough data to
> _differentiate_ between an autoregressive model with
> innovations, and a "general" correlation model.  You
> can definitely include YEAR random effects and either model
> will fit equally well, and may or may not be a significant
> improvement on your original model.
> 
> Cheers, David Duffy.
> 
> -- | David Duffy (MBBS PhD)       
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