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Message: 1
Date: Fri, 11 Dec 2009 11:43:40 -
From: Anna Renwick anna.renw...@bto.org
Subject: [R-sig-eco] low predicted vales in GAMs
To: r-sig-ecology@r-project.org
Message-ID: bfd6df2c5ca142c58c272652fa017...@btodomain.bto.org
Content-Type: text/plain
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 0.92
cv017.405e-02 5.437e-0313.620 2e-16 ***
cv032.258e-02 5.145e-03 4.389 1.20e-05 ***
cv042.878e-02 4.839e-03 5.949 3.18e-09 ***
cv053.634e-02 5.326e-03 6.823 1.17e-11 ***
cv072.370e-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 0.99
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 0.99
---
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. = 39.056n = 2038
Count = bird counts/square
Is this really an integer?
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.
Why would you use a weighting factor in a Poisson/quasi-Poisson GLM/GAM?
See also the weights text for the help file for glm. Not sure what it
would be doing.
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.
you seem to have a very large overdispersion. But that is another
problem. I think your number of squares should actually be used in the
offset (the log obviously).
Alain
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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Dr. Alain F. Zuur
First author of:
1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7
2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9
3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3
Other books: http://www.highstat.com/books.htm
Statistical consultancy, courses, data analysis and software
Highland Statistics Ltd.
6 Laverock road
UK - AB41 6FN Newburgh
Tel: 0044 1358 788177
Email: highs...@highstat.com
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