Panel Generating Functions {party} | R Documentation

## 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 = "lightgray", ymax = NULL,
      ylines = 3, widths = 1, gap = 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, ...)

### 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
`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.
`...` | 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

Meyer, D., Zeileis, A., and Hornik, K. (2005) The strucplot framework: Visualizing multi-way contingency tables with vcd. Report 22, Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Research Report Series. http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1

### Examples

      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)))

[Package party version 0.8-6 Index]