[R] fit and predict binomial gbm with two offset terms

[David March Morla]

Dear all,

I am using the ‘dismo’ package to conduct boosted regression trees (BRT) 
for both binary and count data. The dismo package uses ‘gbm’ package for 
the implementation of BRT. I would like to incorporate two offset terms 
in the model, as well as being able to make predictions.
For the count data I am using a Poisson model. Based on a previous post 
(https://stat.ethz.ch/pipermail/r-help/2010-September/253647.html), I 
implemented the following code:

library(gbm)
library(dismo)

#define offset
offset=(log(data$off1)+ log(data$off2)) #equivalent to 
log(data$off1*data$off2)

#fit poisson
m.pois<-gbm.step(data=data, gbm.x=7:8, gbm.y=4, offset=offset, 
family="poisson", tree.complexity=1, learning.rate=0.001, 
bag.fraction=0.7, n.folds=10)

#predict poisson
link <-predict.gbm(m.pois, data, n.trees=n.trees, type="link")
link.offset<- link + offset
pred <- exp(link.offset)

My questions is how to implement the same for a binomial model? I have 
tried to look in different forums and documentation without success. The 
only clue that I have is the following document: 
https://r-forge.r-project.org/scm/viewvc.php/*checkout*/pkg/inst/doc/gbm.pdf?revision=18&root=gbm&pathrev=22 


Any advice and/or additional references on this issue would be more than 
welcome.

Thank you in advance,

David March

-- 
David March Morlà
Spatial Ecologist
Email: david at imedea.uib-csic.es

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