I'm fitting a Poisson gam model, say

model<-gam(a65tm~as.factor(day.week
)+as.factor(week)+offset(log(pop65))+s(time,k=10,bs="cr",fx=FALSE,by=NA,m=1),sp=c(
0.001),data=dati1,family=poisson)



Currently I've difficulties in obtaining right predictions by using
gam.predict function with MGCV package in R version 2.2.1 (see below my
syntax).

previ<-predict.gam(model,dati2,type="response")

I expect to get the predicted values in the same scale of the response
variable but they are far what I expect. (i.e 1.3 when my response variable
is 40), while when I use predict.gam with gam package I have right
prediction (in the response scale).

Does anybody know if I missed anything important? Is there anyway to make it
work correctly or reliably?

Moreover, if it's possible, could anybody explain me why using gam function
with GAM package I obtain different predictions from those obtained using
gam function with MGCV package?

Any help will be highly appreciated.

All the best

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