Anke,
mgcv:predict.gam certainly didn't produce `something like a
negative log-likelihood of occurrence', but is it possible that one of
your maps is on the probability scale and the other on the linear
predictor scale?
If you used predict.glm(model1,type="response"), but
predict.gam(model2,type="link"), then you'd get the sort of difference
that you are maybe describing. ?predict.gam and ?predict.glm give more
details.
If that doesn't resolve the issue, then a few more details about the
models actually being fitted are probably needed.
best,
Simon
Quoting Anke Konrad <akon...@nmsu.edu>:
Hi all,
I am currently trying to compare different plant occurrence prediction
maps generated in R and exported into GRASS. One of these maps was
generated from a glm fitted to some data, and subsequently applying
this glm model to a wider region using predict.glm. The outcome here
was a probability of occurrence. The second map I generated using a gam
(mgcv), however, this map seems to have assigned something like a
negative log-likelihood of occurrence to each raster cell in the
region. Since I would like to compare the two, I would like to figure
out a way of having the same kind of output from the "predict"
functions (either probability OR negative log-likelihood). Does anyone
know of a way of changing the output options? And if not, does anyone
have any suggestions of how I could deal with this issue?
Thank you!
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