partialPlot {randomForest}R Documentation

Partial dependence plot

Description

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

Usage

## S3 method for class 'randomForest':
partialPlot(x, pred.data, x.var, which.class,
      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).
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.
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.

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.

Value

A list with two components: x and y, which are the values used in the plot.

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.

Author(s)

Andy Liaw andy_liaw@merck.com

References

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

See Also

randomForest

Examples

data(airquality)
airquality <- na.omit(airquality)
set.seed(131)
ozone.rf <- randomForest(Ozone ~ ., airquality)
partialPlot(ozone.rf, airquality, Temp)

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

[Package randomForest version 4.5-1 Index]