Dear All

 

I have come across a problem with the GAM models I am running. Basically the
predicted values are consistently only about 0.4 of the actual values. 

 

A bit more detail:

MODEL:

m4<-gam(count~s(east,north,k=10)+ez+cv01+cv03+cv04+cv05+cv07+mtemp+mtotalrai
n+ez:mtemp+ez:mtotalrain+

            offset(log(fit.vec)),

            weights=wt,

            data=spat6,

            family=quasipoisson,

            start=rep(0,26)

)

MODEL SUMMARY:

 

Family: quasipoisson 

Link function: log 

 

Formula:

count ~ s(east, north, k = 10) + ez + cv01 + cv03 + cv04 + cv05 + 

    cv07 + mtemp + mtotalrain + ez:mtemp + ez:mtotalrain +
offset(log(fit.vec))

 

Parametric coefficients:

                 Estimate Std. Error   t value Pr(>|t|)    

(Intercept)    -5.296e+00  1.846e+00    -2.869 0.004166 ** 

ezM             1.651e+00  2.102e+00     0.785 0.432397    

ezP             7.358e+00  2.047e+00     3.595 0.000332 ***

ezU            -1.061e+02  1.064e+07 -9.97e-06 0.999992    

cv01            7.405e-02  5.437e-03    13.620  < 2e-16 ***

cv03            2.258e-02  5.145e-03     4.389 1.20e-05 ***

cv04            2.878e-02  4.839e-03     5.949 3.18e-09 ***

cv05            3.634e-02  5.326e-03     6.823 1.17e-11 ***

cv07            2.370e-02  5.712e-03     4.149 3.48e-05 ***

mtemp          -1.838e-01  1.750e-01    -1.050 0.293900    

mtotalrain      1.872e-02  5.072e-03     3.692 0.000229 ***

ezM:mtemp       6.181e-02  2.204e-01     0.280 0.779197    

ezP:mtemp      -7.028e-01  2.050e-01    -3.429 0.000619 ***

ezU:mtemp       8.697e-01  1.371e+06  6.34e-07 0.999999    

ezM:mtotalrain -3.393e-02  5.799e-03    -5.851 5.68e-09 ***

ezP:mtotalrain -1.901e-02  5.379e-03    -3.535 0.000417 ***

ezU:mtotalrain  3.510e-02  4.074e+04  8.62e-07 0.999999    

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

 

Approximate significance of smooth terms:

                edf Ref.df     F p-value    

s(east,north) 8.736  8.736 28.88  <2e-16 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

 

R-sq.(adj) =  0.324   Deviance explained = -5.12e+03%

GCV score = 39.556  Scale est. = 39.056    n = 2038

 

 

Count = bird counts/square

ez=environmental zone

cv = habitat types

mtemp = mean annual temperature

mtotalrain= mean total rain/year

 

Sample size is approximately 2000.

 

The offset fit.vec is bird detectability and the weighting is based on the
number of squares in each area surveyed. I belief that the strange deviance
explained is due to the weighting we have added into the model.

 

I would have assumed that the predicted values divided by the real counts
should be around 1, however they are much lower and hence the model is
consistently predicting lower counts than were observed. I was wondering if
there is anything obvious which I am missing when carrying out these models.

 

Many thanks,

Anna

 

Dr Anna R. Renwick
Research Ecologist
British Trust for Ornithology, 
The Nunnery, 
Thetford, 
Norfolk, 
IP24 2PU, 
UK
Tel: +44 (0)1842 750050; Fax: +44 (0)1842 750030 

 


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