Description Usage Arguments Details Value Author(s) References See Also
Computes the relative influence of each variable in the gbm object.
1 2 3 4 5 6 7 8 9 |
object |
a |
cBars |
the number of bars to plot. If |
n.trees |
the number of trees used to generate the plot. Only the first
|
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.
|
normalize |
if |
... |
other arguments passed to the plot function. |
For distribution="gaussian"
this returns exactly the reduction
of squared error attributable to each variable. For other loss functions this
returns the reduction attributable to each variable 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.
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.
Greg Ridgeway gregridgeway@gmail.com
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
L. Breiman (2001).Random Forests.
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