Partial dependence plot gives a graphical depiction of the marginal
effect of a variable on the response, currently only implemented for
random forests.
Usage
## S3 method for class 'randomForest':
partial.plot(x, pred.data, x.var, add=FALSE, n.pt=min(nrow(pred.data), 51),
rug=TRUE, ...)
Arguments
x
an object of class randomForest, which contains a
forest component.
pred.data
a data frame used for contructing the plot, usually
the training data used to contruct the random forest.
x.var
(unquoted) name of the variable for which partial
dependence is to be examined.
add
whether to add to existing plot (TRUE) or create a
new plot (FALSE).
n.pt
if x.var is continuous, the number of points on the
grid for evaluating partial dependence.
rug
whether to draw hash marks at the bottom of the plot
indicating the deciles of x.var.
...
other graphical parameters to be passed on to plot
or lines.
Value
A list with two components: x and y, which are the values
used in the plot.
References
Freidman, J. (2001). Greedy function approximation: the gradient
boosting machine, Ann. of Stat.