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Autocorrelation in a GAMM for nightly time series
3 messages · Andrew Digby, Gavin Simpson
On Thu, 2013-06-06 at 16:29 +1200, Andrew Digby wrote:
From: Ben Bolker <bbolker at gmail.com> Subject: Re: [R-sig-ME] Autocorrelation in a GAMM for nightly time series Date: 5 June 2013 13:35:51 NZST To: r-sig-mixed-models at r-project.org Andrew Digby <andrewdigby at ...> writes:
I'm analysing call counts of a nocturnal species in response to temporal and environmental effects. Over a period of 3 years I divide each night into 10 equal-length blocks, and count the calls of each sex in each block.
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Thanks very much for that, Ben. The amount of help you provide through these forums is truly impressive!
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So thanks to your help the model is improved, although the autocorrelation problem remains. I have a couple of remaining questions regarding this, which I'd really appreciate advice with:
Can I just check that you are using the normalised residuals here? The default for `resid()` will give you deviance? residuals and won't take the covariance matrix into account. The normalised residuals will do that: resid(mod$lme, type = "normalised") See ?resid.lme with the nlme package loaded. This often catches peoples out (me included) at first. G
1) ACF confidence intervals: because I have a large dataset (~40,000 counts in ~11,000 bins), the 95% confidence intervals of the acf [acf(resid(gamm.model, type='n')] are tiny: about +/- 0.01. So while the peaks in the ACF exceed these, they're still small correlations: ~0.1. Because my data have lots of time gaps in them, since they're just lots of nights merged together, can I rely on these confidence intervals and ACF calculation? Am I overstating the importance of the ACF? When I construct a variogram (as per Zuur et al's 2009 book), it's pretty flat, suggesting that there are no major independence problems. 2) This model seems to need a complex correlation structure: an AR correlation between time bins in the same night (TimeofNight | NightofYear), AND correlation (AR?) between the same time bin on different nights (1 | TimeofNight). Several sources warn against using over-complicated correlation structures. But if the ACF shows residual autocorrelation with more basic structures, isn't a more realistic structure needed; or am I putting too much emphasis on the ACF CIs (question 1)? Again, many thanks. Andrew [[alternative HTML version deleted]]
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Gavin Simpson, PhD [t] +1 306 337 8863 Adjunct Professor, Department of Biology [f] +1 306 337 2410 Institute of Environmental Change & Society [e] gavin.simpson at uregina.ca 523 Research and Innovation Centre [tw] @ucfagls University of Regina Regina, SK S4S 0A2, Canada %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
On 7/06/2013, at 02:42 , Gavin Simpson <gavin.simpson at ucl.ac.uk> wrote:
On Thu, 2013-06-06 at 16:29 +1200, Andrew Digby wrote:
From: Ben Bolker <bbolker at gmail.com> Subject: Re: [R-sig-ME] Autocorrelation in a GAMM for nightly time series Date: 5 June 2013 13:35:51 NZST To: r-sig-mixed-models at r-project.org Andrew Digby <andrewdigby at ...> writes:
I'm analysing call counts of a nocturnal species in response to temporal and environmental effects. Over a period of 3 years I divide each night into 10 equal-length blocks, and count the calls of each sex in each block.
[snip]
<snip/>
Thanks very much for that, Ben. The amount of help you provide through these forums is truly impressive!
<snip />
So thanks to your help the model is improved, although the autocorrelation problem remains. I have a couple of remaining questions regarding this, which I'd really appreciate advice with:
Can I just check that you are using the normalised residuals here? The default for `resid()` will give you deviance? residuals and won't take the covariance matrix into account. The normalised residuals will do that: resid(mod$lme, type = "normalised") See ?resid.lme with the nlme package loaded. This often catches peoples out (me included) at first. G
Yes, unfortunately I am using type='normalized'. Thanks for the tip though! Andrew