Partial dependence plot

Partial dependence plot gives a graphical depiction of the marginal effect of a variable on the class probability (classification) or response (regression).

# S3 method for randomForest
partialPlot(x, pred.data, x.var, which.class,
      w, plot = TRUE, add = FALSE,
      n.pt = min(length(unique(pred.data[, xname])), 51),
      rug = TRUE, xlab=deparse(substitute(x.var)), ylab="",
      main=paste("Partial Dependence on", deparse(substitute(x.var))),
      ...)

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	name of the variable for which partial dependence is to be examined.
which.class	For classification data, the class to focus on (default the first class).
w	weights to be used in averaging; if not supplied, mean is not weighted
plot	whether the plot should be shown on the graphic device.
add	whether to add to existing plot (TRUE).
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.
xlab	label for the x-axis.
ylab	label for the y-axis.
main	main title for the plot.
...	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.

Details

The function being plotted is defined as: $$ \tilde{f}(x) = \frac{1}{n} \sum_{i=1}^n f(x, x_{iC}), $$ where \(x\) is the variable for which partial dependence is sought, and \(x_{iC}\) is the other variables in the data. The summand is the predicted regression function for regression, and logits (i.e., log of fraction of votes) for which.class for classification: $$ f(x) = \log p_k(x) - \frac{1}{K} \sum_{j=1}^K \log p_j(x),$$ where \(K\) is the number of classes, \(k\) is which.class, and \(p_j\) is the proportion of votes for class \(j\).

Note

The randomForest object must contain the forest component; i.e., created with randomForest(..., keep.forest=TRUE).

This function runs quite slow for large data sets.

References

Friedman, J. (2001). Greedy function approximation: the gradient boosting machine, Ann. of Stat.

See also

randomForest

Examples

data(iris)
set.seed(543)
iris.rf <- randomForest(Species~., iris)
partialPlot(iris.rf, iris, Petal.Width, "versicolor")

## Looping over variables ranked by importance:
data(airquality)
airquality <- na.omit(airquality)
set.seed(131)
ozone.rf <- randomForest(Ozone ~ ., airquality, importance=TRUE)
imp <- importance(ozone.rf)
impvar <- rownames(imp)[order(imp[, 1], decreasing=TRUE)]
op <- par(mfrow=c(2, 3))
for (i in seq_along(impvar)) {
    partialPlot(ozone.rf, airquality, impvar[i], xlab=impvar[i],
                main=paste("Partial Dependence on", impvar[i]),
                ylim=c(30, 70))
}
#> Error in eval(x.var): object 'impvar' not found
par(op)