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)) 

 

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

 


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