Alexis Cerezo
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Dear all,
I'm running an analysis in PRESENCE with bird presence-absence data, in three habitats (tropical forest and two agroforestry habitats, pepper/cinnamon and cardamom) and 4 years of data. I ran the following models:
NUM Model
1 psi(.),p(.)
2 psi(year),p(.)
3 psi(habitat),p(.)
4 psi(habitat+year),p(.)
5 psi(year),p(habitat)
6 psi(habitat),p(habitat)
7 psi(habitat+year),p(habitat)
8 psi(habitatxyear),p(.)
9 psi(habitatxyear),p(habitat),
with the following B's:
for psi:
B1-ordinate (represents tropical forest)
B2-pepper/cinnamon effect
B3-cardamom effect
B4-year
B5-year x pepper/cinnamon effect
B6-year cardamom effect
for p:
B1-ordinate (represents forest)
B2-pepper cinnamon effect
B3-cardamom effect
The results file at first looked ok, including the Bi estimates for each model, but when I looked at the model likelihoods, for some models they were HUGE!! my question is thus, are these values correct, or is there a problem?
Here is an example from the PRESENCE output:
Model AIC Model Likelihood
psi(.),p(.) 438.24 1.00
psi(year),p(.) 439.90 0.44
psi(habitat),p(.) 416.35 56669.99
psi(habitat+year),p(.) 417.89 26238.94
psi(year),p(habitat) 420.83 6033.00
psi(habitat),p(habitat) 411.40 673336.17
psi(habitatxyear),p(.) 414.67 131268.48
psi(habitat+year),p(habitat) 412.67 356824.73
psi(habitatxyear),p(habitat) 409.76 1528809.67
(see likelihoods for models 3-9)
Any help will be greatly appreciated, cheers.
Alexis