R: Partial dependence plot

partialPlot {RRF} | 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 'RRF'
    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 `RRF`, 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 `RRF` object must contain the `forest` component; i.e., created with `RRF(..., 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

`RRF`

### Examples

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

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

[Package RRF version 1.9.4 Index]