randomForest: partialPlot – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

partialPlot

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,
      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`.

Details

The function being plotted is defined as:

\tilde{f}(x) = \frac{1}{n} ∑_{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} ∑_{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.

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))
}
par(op)

randomForest

Breiman and Cutler's Random Forests for Classification and Regression

v4.6-14

GPL (>= 2)

Authors

Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.

Initial release

2018-03-22