R: Plotting Partial Dependence Functions

plotPartial {pdp} | R Documentation

## Plotting Partial Dependence Functions

### Description

Plots partial dependence functions (i.e., marginal effects) using `lattice` graphics.

### Usage

    plotPartial(object, ...)

    ## S3 method for class 'ice'
    plotPartial(object, center = FALSE, plot.pdp = TRUE,
      pdp.col = "red2", pdp.lwd = 2, pdp.lty = 1, rug = FALSE,
      train = NULL, alpha = 1, ...)

    ## S3 method for class 'cice'
    plotPartial(object, plot.pdp = TRUE, pdp.col = "red2",
      pdp.lwd = 2, pdp.lty = 1, rug = FALSE, train = NULL, alpha = 1,
      ...)

    ## S3 method for class 'partial'
    plotPartial(object, center = FALSE, plot.pdp = TRUE,
      pdp.col = "red2", pdp.lwd = 2, pdp.lty = 1, smooth = FALSE,
      rug = FALSE, chull = FALSE, levelplot = TRUE, contour = FALSE,
      contour.color = "white", col.regions = NULL, palette = c("viridis",
      "magma", "inferno", "plasma", "cividis"), alpha = 1, number = 4,
      overlap = 0.1, train = NULL, ...)

### Arguments

`object` | An object that inherits from the `"partial"` class.
`...` | Additional optional arguments to be passed onto `dotplot`, `levelplot`, `xyplot`, or `wireframe`.
`center` | Logical indicating whether or not to produce centered ICE curves (c-ICE curves). Only useful when `object` represents a set of ICE curves; see `partial` for details. Default is `FALSE`.
`plot.pdp` | Logical indicating whether or not to plot the partial dependence function on top of the ICE curves. Default is `TRUE`.
`pdp.col` | Character string specifying the color to use for the partial dependence function when `plot.pdp = TRUE`. Default is `"red"`.
`pdp.lwd` | Integer specifying the line width to use for the partial dependence function when `plot.pdp = TRUE`. Default is `1`. See `par` for more details.
`pdp.lty` | Integer or character string specifying the line type to use for the partial dependence function when `plot.pdp = TRUE`. Default is `1`. See `par` for more details.
`rug` | Logical indicating whether or not to include rug marks on the predictor axes. Default is `FALSE`.
`train` | Data frame containing the original training data. Only required if `rug = TRUE` or `chull = TRUE`.
`alpha` | Numeric value in `[0, 1]` specifying the opacity alpha ( most useful when plotting ICE/c-ICE curves). Default is 1 (i.e., no transparency).
`smooth` | Logical indicating whether or not to overlay a LOESS smooth. Default is `FALSE`.
`chull` | Logical indicating whether or not to restrict the first two variables in `pred.var` to lie within the convex hull of their training values; this affects `pred.grid`. Default is `FALSE`.
`levelplot` | Logical indicating whether or not to use a false color level plot (`TRUE`) or a 3-D surface (`FALSE`). Default is `TRUE`.
`contour` | Logical indicating whether or not to add contour lines to the level plot. Only used when `levelplot = TRUE`. Default is `FALSE`.
`contour.color` | Character string specifying the color to use for the contour lines when `contour = TRUE`. Default is `"white"`.
`col.regions` | Color vector to be used for trivariate displays. If `levelplot` is `TRUE`, defaults to the wonderful Matplotlib 'viridis' color map provided by the `viridis` package. See `viridis` for details.
`palette` | Character string indicating the colormap option to use. Five options are available: "viridis" (the default), "magma", "inferno", "plasma", and "cividis".
`number` | Integer specifying the number of conditional intervals to use for the continuous panel variables. See `co.intervals` and `equal.count` for further details.
`overlap` | The fraction of overlap of the conditioning variables. See `co.intervals` and `equal.count` for further details.

### Examples

    ## Not run:
    #
    # Regression example (requires randomForest package to run)
    #

    # Load required packages
    library(ggplot2)  # required to use autoplot
    library(randomForest)

    # Fit a random forest to the Boston housing data
    data (boston)  # load the boston housing data
    set.seed(101)  # for reproducibility
    boston.rf <- randomForest(cmedv ~ ., data = boston)

    # Partial dependence of cmedv on lstat
    boston.rf %>%
      partial(pred.var = "lstat") %>%
      plotPartial(rug = TRUE, train = boston)

    # Partial dependence of cmedv on lstat and rm
    boston.rf %>%
      partial(pred.var = c("lstat", "rm"), chull = TRUE, progress = "text") %>%
      plotPartial(contour = TRUE, legend.title = "rm")

    # ICE curves and c-ICE curves
    age.ice <- partial(boston.rf, pred.var = "lstat", ice = TRUE)
    p1 <- plotPartial(age.ice, alpha = 0.5)
    p2 <- plotPartial(age.ice, center = TRUE, alpha = 0.5)
    grid.arrange(p1, p2, ncol = 2)

    ## End(Not run)

[Package pdp version 0.7.0 Index]