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Plot a greedy concentration-index tree with labeled split and node panels

Source: R/tree_default_name.R
plot.ci_tree.Rd

ci_tree() returns objects with class ci_tree, so calling plot() on a fitted tree dispatches to this method. The method keeps the underlying partykit layout while supplying compact edge labels, labeled internal nodes, and optional terminal-node summaries by default.

Usage

# S3 method for class 'ci_tree'
plot(
  x,
  main = NULL,
  terminal_panel = NULL,
  tp_args = list(),
  inner_panel = NULL,
  ip_args = list(),
  edge_panel = NULL,
  ep_args = list(),
  data = NULL,
  terminal_stats = list(n = function(data) NROW(data)),
  var_labels = NULL,
  stat_labels = NULL,
  stat_formatters = list(),
  show_p = TRUE,
  edge_fill = "white",
  inner_fill = "white",
  terminal_fill = "lightgray",
  ...
)

Arguments

x

A fitted ci_tree object created by ci_tree().

main

Optional main title passed to the underlying plot method.

terminal_panel, inner_panel, edge_panel

Optional panel functions passed through to partykit plotting.

tp_args, ip_args, ep_args

Lists of extra arguments for the panel generators.

data

Optional data used to compute terminal-node summaries. Defaults to the model frame stored on x.

terminal_stats

Named list of summary functions for terminal nodes. Use NULL to fall back to the default partykit terminal panel.

var_labels

Optional named character vector mapping variable names to prettier labels in split and inner-node annotations.

stat_labels

Optional named character vector for terminal summary labels.

stat_formatters

Optional named list of formatting functions for terminal summaries.

show_p

Logical; include split-test p-values in internal nodes.

edge_fill, inner_fill, terminal_fill

Fill colors for the custom panels.

...

Additional arguments passed to partykit::plot.party().

Value

Invisibly returns the plotted ci_tree.

Examples

data(kenya, package = "ineqTrees")
kenya_plot_vars <- c("wealth", "deadu5_num", "rural", "ed", "reg", "unskilled")
kenya_plot_data <- kenya[
  stats::complete.cases(kenya[, kenya_plot_vars]),
  kenya_plot_vars
]
set.seed(20260512)
kenya_plot_data <- kenya_plot_data[
  sample.int(nrow(kenya_plot_data), 800L),
  ,
  drop = FALSE
]
kenya_plot_fit <- ci_tree(
  cbind(wealth, deadu5_num) ~ rural + ed + reg + unskilled,
  data = kenya_plot_data,
  rank_name = "wealth",
  outcome_name = "deadu5_num",
  control = ci_tree_control(
    minsplit = 100,
    minbucket = 50,
    minprob = 0.05,
    maxdepth = 3
  )
)
kenya_var_labels <- c(
  rural = "Residence",
  ed = "Mother education",
  reg = "Province",
  unskilled = "Mother occupation"
)
kenya_stat_funs <- list(
  n = nrow,
  mortality = function(df) mean(df$deadu5_num),
  mean_wealth = function(df) mean(df$wealth)
)

plot(
  kenya_plot_fit,
  var_labels = kenya_var_labels,
  plural_overrides = c(Province = "provinces")
)


plot(
  kenya_plot_fit,
  data = kenya_plot_data,
  var_labels = kenya_var_labels,
  plural_overrides = c(Province = "provinces"),
  terminal_stats = kenya_stat_funs,
  stat_labels = c(
    n = "n",
    mortality = "% death",
    mean_wealth = "mean wealth"
  ),
  stat_formatters = list(
    mortality = function(x) sprintf("%.1f%%", 100 * x),
    mean_wealth = function(x) sprintf("%.2f", x)
  )
)