panelfunctions: Panel-Generators for Visualization of Party Trees

In party: A Laboratory for Recursive Partytioning

Panel-Generators for Visualization of Party Trees

Description

The plot method for BinaryTree and mob objects are rather flexible and can be extended by panel functions. Some pre-defined panel-generating functions of class grapcon_generator for the most important cases are documented here.

Usage

node_inner(ctreeobj, digits = 3, abbreviate = FALSE, 
  fill = "white", pval = TRUE, id = TRUE)
node_terminal(ctreeobj, digits = 3, abbreviate = FALSE, 
  fill = c("lightgray", "white"), id = TRUE)
edge_simple(treeobj, digits = 3, abbreviate = FALSE)
node_surv(ctreeobj, ylines = 2, id = TRUE, ...)
node_barplot(ctreeobj, col = "black", fill = NULL, beside = NULL,
  ymax = NULL, ylines = NULL, widths = 1, gap = NULL, 
  reverse = NULL, id = TRUE)
node_boxplot(ctreeobj, col = "black", fill = "lightgray", 
  width = 0.5,  yscale = NULL, ylines = 3, cex = 0.5, id = TRUE)
node_hist(ctreeobj, col = "black", fill = "lightgray", 
  freq = FALSE, horizontal = TRUE, xscale = NULL, ymax = NULL, 
  ylines = 3, id = TRUE, ...)
node_density(ctreeobj, col = "black", rug = TRUE, 
  horizontal = TRUE, xscale = NULL, yscale = NULL, ylines = 3, 
  id = TRUE)
node_scatterplot(mobobj, which = NULL, col = "black", 
  linecol = "red", cex = 0.5, pch = NULL, jitter = FALSE, 
  xscale = NULL, yscale = NULL, ylines = 1.5, id = TRUE, 
  labels = FALSE)
node_bivplot(mobobj, which = NULL, id = TRUE, pop = TRUE,
  pointcol = "black", pointcex = 0.5,
  boxcol = "black", boxwidth = 0.5, boxfill = "lightgray",
  fitmean = TRUE, linecol = "red",
  cdplot = FALSE, fivenum = TRUE, breaks = NULL,
  ylines = NULL, xlab = FALSE, ylab = FALSE, margins = rep(1.5, 4), ...)

Arguments

ctreeobj	an object of class BinaryTree.
treeobj	an object of class BinaryTree or mob.
mobobj	an object of class mob.
digits	integer, used for formating numbers.
abbreviate	logical indicating whether strings should be abbreviated.
col, pointcol	a color for points and lines.
fill	a color to filling rectangles.
pval	logical. Should p values be plotted?
id	logical. Should node IDs be plotted?
ylines	number of lines for spaces in y-direction.
widths	widths in barplots.
width, boxwidth	width in boxplots.
gap	gap between bars in a barplot (node_barplot).
yscale	limits in y-direction
xscale	limits in x-direction
ymax	upper limit in y-direction
beside	logical indicating if barplots should be side by side or stacked.
reverse	logical indicating whether the order of levels should be reversed for barplots.
horizontal	logical indicating if the plots should be horizontal.
freq	logical; if TRUE, the histogram graphic is a representation of frequencies. If FALSE, probabilities are plotted.
rug	logical indicating if a rug representation should be added.
which	numeric or character vector indicating which of the regressor variables should be plotted (default = all).
linecol	color for fitted model lines.
cex, pointcex	character extension of points in scatter plots.
pch	plotting character of points in scatter plots.
jitter	logical. Should the points be jittered in y-direction?
labels	logical. Should axis labels be plotted?
pop	logical. Should the panel viewports be popped?
boxcol	color for box plot borders.
boxfill	fill color for box plots.
fitmean	logical. Should lines for the predicted means from the model be added?
cdplot	logical. Should CD plots (or spinograms) be used for visualizing the dependence of a categorical on a numeric variable?
fivenum	logical. When using spinograms, should the five point summary of the explanatory variable be used for determining the breaks?
breaks	a (list of) numeric vector(s) of breaks for the spinograms. If set to NULL (the default), the breaks are chosen according to the fivenum argument.
xlab, ylab	character with x- and y-axis labels. Can also be logical: if FALSE axis labels are surpressed, if TRUE they are taken from the underlying data. Can be a vector of labels for xlab.
margins	margins of the viewports.
...	additional arguments passed to callies.

Details

The plot methods for BinaryTree and mob objects provide an extensible framework for the visualization of binary regression trees. The user is allowed to specify panel functions for plotting terminal and inner nodes as well as the corresponding edges. The panel functions to be used should depend only on the node being visualzied, however, for setting up an appropriate panel function, information from the whole tree is typically required. Hence, party adopts the framework of grapcon_generator (graphical appearance control) from the vcd package (Meyer, Zeileis and Hornik, 2005) and provides several panel-generating functions. For convenience, the panel-generating functions node_inner and edge_simple return panel functions to draw inner nodes and left and right edges. For drawing terminal nodes, the functions returned by the other panel functions can be used. The panel generating function node_terminal is a terse text-based representation of terminal nodes.

Graphical representations of terminal nodes are available and depend on the kind of model and the measurement scale of the variables modelled.

For univariate regressions (typically fitted by ctree), node_surv returns a functions that plots Kaplan-Meier curves in each terminal node; node_barplot, node_boxplot, node_hist and node_density can be used to plot bar plots, box plots, histograms and estimated densities into the terminal nodes.

For multivariate regressions (typically fitted by mob), node_bivplot returns a panel function that creates bivariate plots of the response against all regressors in the model. Depending on the scale of the variables involved, scatter plots, box plots, spinograms (or CD plots) and spine plots are created. For the latter two spine and cd_plot from the vcd package are re-used.

References

David Meyer, Achim Zeileis, and Kurt Hornik (2006). The Strucplot Framework: Visualizing Multi-Way Contingency Tables with vcd. Journal of Statistical Software, 17(3). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v017.i03")}

Examples

  set.seed(290875)

  airq <- subset(airquality, !is.na(Ozone))
  airct <- ctree(Ozone ~ ., data = airq)

  ## default: boxplots
  plot(airct)
  
  ## change colors
  plot(airct, tp_args = list(col = "blue", fill = hsv(2/3, 0.5, 1)))
  ## equivalent to
  plot(airct, terminal_panel = node_boxplot(airct, col = "blue", 
                                            fill = hsv(2/3, 0.5, 1)))

  ### very simple; the mean is given in each terminal node
  plot(airct, type = "simple")

  ### density estimates
  plot(airct, terminal_panel = node_density)
    
  ### histograms 
  plot(airct, terminal_panel = node_hist(airct, ymax = 0.06, 
                                         xscale = c(0, 250)))

party documentation built on Sept. 11, 2024, 5:18 p.m.