Plotting Partial Dependence Functions (deprecated)

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,
  ...
)

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

# 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,
  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 graphics::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 graphics::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 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: Vector of colors to be passed on to lattice::levelplot()'s col.region argument. Defaults to grDevices::hcl.colors(100) (which is the same viridis color palette used in the past).
number: Integer specifying the number of conditional intervals to use for the continuous panel variables. See graphics::co.intervals() and lattice::equal.count() for further details.
overlap: The fraction of overlap of the conditioning variables. See graphics::co.intervals() and lattice::equal.count() for further details.

Details

Deprecated: plotPartial() is deprecated and will be removed in a future release; please use plot(..., lattice = TRUE) instead, which produces the same displays through a single interface (see plot.partial() for details).

Examples

if (FALSE) { # \dontrun{
#
# Regression example (requires randomForest package to run)
#

# Load required packages
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") %>%
  plotPartial(rug = TRUE, train = boston)

# Partial dependence of cmedv on lstat and rm
boston.rf %>%
  partial(pred.var = c("lstat", "rm"), chull = TRUE, progress = TRUE) %>%
  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 = 0.1)
p2 <- plotPartial(age.ice, center = TRUE, alpha = 0.1)
grid.arrange(p1, p2, ncol = 2)
} # }