summary.gbm(object,
cBars=object$cCols,
n.trees=object$n.trees,
plotit=TRUE,
order=TRUE,
...)gbm object created from an initial call to
gbm.order=TRUE the only the
variables with the cBars largest relative influence will appear in the
barplot. If order=FALSE then the first cBars variables will
appear in thn.trees trees will be used.distribution="gaussian" this returns exactly a set of
Type III sum of squares for each variable normalized to sum to 100. For other
loss functions this returns the reduction attributeable to each varaible in sum
of squared error in predicting the gradient on each iteration. It describes the
relative influence of each variable in reducing the loss function. See the
references below for exact details on the computation.gbm