Keeping Gavin's advice in mind, you may also want to look at ?acf (and see section 14.1 of MASS) and help(ACF, package=nlme) (see section 5.3 of MEMSS). These are useful functions for exploring the 1d empirical autocorrelation structure of model residuals.
hth, Kingsford Jones On Fri, Aug 15, 2008 at 1:18 AM, Gavin Simpson <[EMAIL PROTECTED]> wrote: > On Thu, 2008-08-14 at 16:12 +0100, Abigail McQuatters-Gollop wrote: >> Hi, >> >> I am looking at the effects of two explanatory variables on chlorophyll. >> The data are an annual time-series (so are autocorrelated) and the >> relationships are non-linear. I want to account for autocorrelation in >> my model. >> >> >> >> The model I am trying to use is this: >> >> >> >> Library(mgcv) >> >> >> >> gam1 <-gam(Chl~s(wintersecchi)+s(SST),family=gaussian, >> na.action=na.omit, correlation=corAR1(form =~ Year)) >> > > There is no correlation argument in mgcv::gam you need gamm(). gam() has > a ... argument which I suspect is silently mopping up your correlation > argument so that no error/warning is raised. > > Note that gamm() uses lme from the nlme package (in the Gaussian case) > and works that function very hard (see Wood 2006 GAM book). In my > experience with this function separating trend from the correlation is > quite difficult when also estimating the degree of smoothness and one > has to work hard with the options. > > As an alternative you might also take a look at the paper by Ferguson et > al (2007): > > http://www3.interscience.wiley.com/journal/119392062/abstract?CRETRY=1&SRETRY=0 > > Which has R code using the sm package to do a very similar thing. > > HTH > > G > >> >> the result I get is this: >> >> >> >> Family: gaussian >> >> Link function: identity >> >> >> >> Formula: >> >> CPRChl ~ s(wintersecchi) + s(SST) >> >> >> >> Parametric coefficients: >> >> Estimate Std. Error t value Pr(>|t|) >> >> (Intercept) 3.57000 0.05061 70.54 <2e-16 *** >> >> --- >> >> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 >> >> >> >> Approximate significance of smooth terms: >> >> edf Est.rank F p-value >> >> s(wintersecchi) 2.445 5 4.672 0.00887 ** >> >> s(SST) 2.408 5 4.301 0.01237 * >> >> --- >> >> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 >> >> >> >> R-sq.(adj) = 0.676 Deviance explained = 75.4% >> >> GCV score = 0.074563 Scale est. = 0.053781 n = 21 >> >> >> >> The result look good - significant, with a lot of deviance explained, >> but I am not convinced the model is actually accounting for the >> autocorrelation (based on the formula in the results). How can I tell? >> >> >> >> Many thanks, >> >> >> >> >> >> >> >> Dr Abigail McQuatters-Gollop >> >> Sir Alister Hardy Foundation for Ocean Science (SAHFOS) >> >> The Laboratory >> >> Citadel Hill >> >> Plymouth UK PL1 2PB >> >> tel: +44 1752 633233 >> >> >> >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
