Dear all,
the help file for predict.gbm states that "The predictions from gbm do not
include the offset term. The user may add the value of the offset to the
predicted value if desired." I am just not sure how exactly, especially for
a Poisson model, where I believe the offset is multiplicative ?
For example:
library(MASS)
fit1 <- glm(Claims ~ District + Group + Age + offset(log(Holders)),
data = Insurance, family = poisson)
head(predict(fit1, data = Insurance, type = "response"))
#glm.predict includes the offset:
head(predict(fit1, newdata = Insurance, type = "response"))
# 1 2 3 4 5 6
# 31.86358 35.27587 28.18080 158.87829 53.97772 84.16012
library(gbm)
fit2 <- gbm(Claims ~ District + Group + Age + offset(log(Holders)),
data = Insurance, distribution ="poisson", n.trees = 600)
head(predict(fit2, newdata = Insurance, type = "response", n.trees=600))
#[1] 0.1378249 0.1378249 0.1314991 0.1284441 0.1389563 0.1389563
#Warning message:
#In predict.gbm(fit2, newdata = Insurance, type = "response", n.trees = 600)
:
# predict.gbm does not add the offset to the predicted values.
Would the answer be simple multiplication such as:
head(predict(fit2, newdata = Insurance, type = "response",
n.trees=600)*Insurance[,"Holders"])
[1] 27.15151 36.38577 32.34878 215.78607 39.46359 74.48058
Any help would be immensely useful.
Thx,
Markus
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