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.

`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]