party and constparty 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.node_inner(obj, id = TRUE, abbreviate = FALSE, fill = "white",
gp = gpar())
node_terminal(obj, digits = 3, abbreviate = FALSE,
fill = c("lightgray", "white"), id = TRUE, gp = gpar())
edge_simple(obj, digits = 3, abbreviate = FALSE)
node_boxplot(obj, col = "black", fill = "lightgray", width = 0.5,
yscale = NULL, ylines = 3, cex = 0.5, id = TRUE, gp = gpar())
node_barplot(obj, col = "black", fill = NULL, beside = NULL,
ymax = NULL, ylines = NULL, widths = 1, gap = NULL,
reverse = NULL, id = TRUE, gp = gpar())party.node_barplot).plot methods for party and constparty 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 visualized, however, for setting up
an appropriate panel function, information from the whole tree is typically
required. Hence, grapcon_generator
(graphical appearance control) from the 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 modeled.
For univariate regressions (typically fitted by ),
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