Computes the relative influence of each variable in the gbm object.
Usage
## S3 method for class 'gbm'
summary(object,
cBars=length(object$var.names),
n.trees=object$n.trees,
plotit=TRUE,
order=TRUE,
method=relative.influence,
normalize=TRUE,
...)
Arguments
object
a gbm object created from an initial call to
gbm.
cBars
the number of bars to plot. If 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 the plot. In either case, the function will return the relative
influence of all of the variables.
n.trees
the number of trees used to generate the plot. Only the first
n.trees trees will be used.
plotit
an indicator as to whether the plot is generated.
order
an indicator as to whether the plotted and/or returned relative
influences are sorted.
method
The function used to compute the relative influence.
relative.influence is the default and is the same as that
described in Friedman (2001). The other current (and experimental) choice is
permutation.test.gbm. This method randomly permutes each predictor
variable at a time and computes the associated reduction in predictive
performance. This is similar to the variable importance measures Breiman uses
for random forests, but gbm currently computes using the entire training
dataset (not the out-of-bag observations).
normalize
if FALSE then summary.gbm returns the
unnormalized influence.
...
other arguments passed to the plot function.
Details
For distribution="gaussian" this returns exactly the reduction
of squared error attributable to each variable. 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.
Value
Returns a data frame where the first component is the variable name and the
second is the computed relative influence, normalized to sum to 100.