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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