autoplot.partial function

Plotting Partial Dependence Functions

Plots partial dependence functions (i.e., marginal effects) using ggplot2 graphics.

## S3 method for class 'partial'
autoplot(
  object,
  center = FALSE,
  plot.pdp = TRUE,
  pdp.color = "red",
  pdp.size = 1,
  pdp.linetype = 1,
  rug = FALSE,
  smooth = FALSE,
  smooth.method = "auto",
  smooth.formula = y ~ x,
  smooth.span = 0.75,
  smooth.method.args = list(),
  contour = FALSE,
  contour.color = "white",
  train = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  legend.title = "yhat",
  ...
)

## S3 method for class 'ice'
autoplot(
  object,
  center = FALSE,
  plot.pdp = TRUE,
  pdp.color = "red",
  pdp.size = 1,
  pdp.linetype = 1,
  rug = FALSE,
  train = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  ...
)

## S3 method for class 'cice'
autoplot(
  object,
  plot.pdp = TRUE,
  pdp.color = "red",
  pdp.size = 1,
  pdp.linetype = 1,
  rug = FALSE,
  train = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  ...
)

Arguments

• object: An object that inherits from the "partial" class.
• 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.color: Character string specifying the color to use for the partial dependence function when plot.pdp = TRUE. Default is "red".
• pdp.size: Positive number specifying the line width to use for the partial dependence function when plot.pdp = TRUE. Default is 1.
• pdp.linetype: Positive number specifying the line type to use for the partial dependence function when plot.pdp = TRUE. Default is 1.
• rug: Logical indicating whether or not to include rug marks on the predictor axes. Default is FALSE.
• smooth: Logical indicating whether or not to overlay a LOESS smooth. Default is FALSE.
• smooth.method: Character string specifying the smoothing method (function) to use (e.g., "auto", "lm", "glm", "gam", "loess", or "rlm"). Default is "auto". See geom_smooth for details.
• smooth.formula: Formula to use in smoothing function (e.g., y ~ x, y ~ poly(x, 2), or y ~ log(x)).
• smooth.span: Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines. Default is 0.75.
• smooth.method.args: List containing additional arguments to be passed on to the modeling function defined by smooth.method.
• contour: Logical indicating whether or not to add contour lines to the level plot.
• contour.color: Character string specifying the color to use for the contour lines when contour = TRUE. Default is "white".
• train: Data frame containing the original training data. Only required if rug = TRUE or chull = TRUE.
• xlab: Character string specifying the text for the x-axis label.
• ylab: Character string specifying the text for the y-axis label.
• main: Character string specifying the text for the main title of the plot.
• legend.title: Character string specifying the text for the legend title. Default is "yhat".
• ...: Additional (optional) arguments to be passed onto geom_line, geom_point, or scale_fill_viridis_c.

Returns

A "ggplot" object.

Examples

## Not run:

#
# Regression example (requires randomForest package to run)
#

# Load required packages
library(ggplot2)  # for autoplot() generic
library(gridExtra)  # for `grid.arrange()`
library(magrittr)  # for forward pipe operator `%>%`
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") %>%
  autoplot(rug = TRUE, train = boston) + theme_bw()

# Partial dependence of cmedv on lstat and rm
boston.rf %>%
  partial(pred.var = c("lstat", "rm"), chull = TRUE, progress = TRUE) %>%
  autoplot(contour = TRUE, legend.title = "cmedv",
           option = "B", direction = -1) + theme_bw()

# ICE curves and c-ICE curves
age.ice <- partial(boston.rf, pred.var = "lstat", ice = TRUE)
grid.arrange(
  autoplot(age.ice, alpha = 0.1),                 # ICE curves
  autoplot(age.ice, center = TRUE, alpha = 0.1),  # c-ICE curves
  ncol = 2
)
## End(Not run)