# plotPartial: Plotting Partial Dependence Functions In pdp: Partial Dependence Plots

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```plotPartial(x, ...) ## S3 method for class 'ice' plotPartial(x, center = FALSE, plot.pdp = TRUE, pdp.col = "red2", pdp.lwd = 2, pdp.lty = 1, rug = FALSE, train = NULL, ...) ## S3 method for class 'cice' plotPartial(x, plot.pdp = TRUE, pdp.col = "red2", pdp.lwd = 2, pdp.lty = 1, rug = FALSE, train = NULL, ...) ## S3 method for class 'partial' plotPartial(x, center = FALSE, plot.pdp = TRUE, pdp.col = "red2", pdp.lwd = 2, pdp.lty = 1, smooth = FALSE, rug = FALSE, chull = FALSE, levelplot = TRUE, contour = FALSE, number = 4, overlap = 0.1, train = NULL, col.regions = viridis::viridis, ...) ```

## Arguments

 `x` 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`. `smooth` Logical indicating whether or not to overlay a LOESS smooth. Default is `FALSE`. `chull` Logical indicating wether 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`. `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. `col.regions` Color vector to be used if `levelplot` is `TRUE`. Defaults to the wonderful Matplotlib 'viridis' color map provided by the `viridis` package. See `viridis` for details.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31``` ```## 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) ```

pdp documentation built on July 20, 2017, 9:01 a.m.