Thanks for the advise Gavin and Wen! Will look into these options...
Cheers,
Marc

On Thu, Nov 24, 2011 at 9:27 PM, Wen Li <Li.Wen@environment.nsw.gov.au>wrote:

> Hi again, Marc,
>
> You may first test the seasonality of your data use the
> SeasonalMannkendall command in the Kendall Package.
>
> Cheers,
> Li
>
> -----Original Message-----
> From: r-sig-ecology-bounces@r-project.org [mailto:
> r-sig-ecology-bounces@r-project.org] On Behalf Of Wen Li
> Sent: Friday, 25 November 2011 7:11 AM
> To: 'gavin.simpson@ucl.ac.uk'; Marc Taylor
> Cc: r-sig-ecology@r-project.org
> Subject: Re: [R-sig-eco] autocorrelation structure
>
> Hi, Marc
>
> You may try to transfer your data by seasonal differential if you have
> multi-year data.
>
> Cheer,
> Li
>
> -----Original Message-----
> From: r-sig-ecology-bounces@r-project.org [mailto:
> r-sig-ecology-bounces@r-project.org] On Behalf Of Gavin Simpson
> Sent: Friday, 25 November 2011 3:34 AM
> To: Marc Taylor
> Cc: r-sig-ecology@r-project.org
> Subject: Re: [R-sig-eco] autocorrelation structure
>
> On Thu, 2011-11-24 at 17:02 +0100, Marc Taylor wrote:
> > Dear R-sig-ecology list,
> >
> >
> > I am fitting a gamm model(package: mgcv)  in which I have a violation of
> > independence with regard to temporal autocorrelation due to the fact that
> > all of my variables have a cyclical seasonal pattern.
>
> I would try to model this in the main effects rather than correct for it
> in the covariance matrix.
>
> If you have data measured throughout the year, over several years, then
> add a cyclic spline term to your model. For example,
>
> mod <- gamm(resp ~ s(year) + s(month, bs = "cc"), data = foo, ....)
>
> will fit a cyclic cubic spline in month (say coded 1, 2, 3 numerically)
> which means that the end points of the spline are match, up to 2nd
> derivatives.
>
> I have regularly used this for spot samples throughout years, where I
> use the day of the year of the sample as my variable for the cyclic
> term:
>
> mod <- gamm(resp ~ s(year) + s(doy bs = "cc"), data = foo, ....)
>
> You may want to specify the knots for the seasonal term to give coverage
> over the real period of interest. For example, I had data on water
> temperature over most days of the year but the people doing the sampling
> didn't sample over Christmas and New Year, so I specifically set the
> knot endpoints to be day of year 1 and 366 to be able to generate
> predictions over the entire year (of course the fitted spline in the
> period without data was subject to greater uncertainty but that was fine
> for me).
>
> HTH
>
> G
>
> >  I have largely
> > corrected for this by including a AR-2 correlation structure in the
> > following way:
> >
> > >corstr <- corARMA(c(0.4, -0.1), p=2, q=0, form = ~ DATE)
> >
> > I nevertheless continue to have some autocorrelation in the "significant"
> > range at some of the larger time lags (i.e. year lags) as identified by:
> >
> > >E <- resid(gamm.fit$lme, type="normalized")
> > >acf(E)
> >
> >
> > My guess is that an AR-2 structure can't produce a sin-like correlation
> > structure, but when I try a higher AR structure(i.e. p=3), the fitting
> > doesn't converge.
> >
> > Is anyone familiar with such a problem? Does the fitting need to run for
> a
> > longer period of time, or are there other options for me?
> >
> > Many thanks for you help,
> > Marc
> >
> >       [[alternative HTML version deleted]]
> >
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> >
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