[R-sig-eco] Time series and GLS

[LisaB]

Thanks for the very helpful advice.  I've been doing a lot of plotting and it
looks like portions of my time series are linearly related with year, but
there are clear deviations from this so I've also been looking at using gam
with smoothers, but still have a ways to go before settling on a model that
adequately describes the data.  After describing trends over time for nest
success, I'll eventually want to relate nest success with food availability
(also a time series variable).  Both nest success and food availability
generally decline over time in a similar way.  Since year and food
availability are correlated and I probably should not use them in the same
model to describe nest success, but I still need to control for year
-correct?  Is it valid to specify the general model as: nest success~food
availability+error even though both the response and explanatory variables
are negatively correlated with time?  I ask because in the "Mixed models and
extensions in R" book, chapter 14 describes a similar problem in which they
leave out year in a model where the explanatory variable exhibited a trend
over time.  However, in other sources I read that this could be a major
problem.  Sorry for the rather long-winded question.  I'm new to time series
analysis.  Thanks to anyone who can provide some insight.   




LisaB wrote:
> 
> Hello -
> 
> I need to analyze some time series data in an ANOVA framework, but am
> unsure of how to go about it.  I have data on nest success (response) over
> a 22 year period for two populations.  For each year I have one value of
> nest success per population.  I am interested in determining 1) whether
> there are differences in nest success over time between these two
> populations and 2) what are the trends for each population over time.  My
> thought is to use GLS and model temporal autocorrelation if the acf
> function indicates this is an issue, but since population is a categorical
> variable I'm unsure if this is appropriate.  Any advice would be much
> appreciated. Thank you. Lisa
> 
> 
> 
>    
> 

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