plot_rs_tree: Plot Response Shift Summary Tree

In grmtree: Recursive Partitioning for Graded Response Models

Plot Response Shift Summary Tree

Description

Displays the Longitudinal GRMTree structure with RS characterization results annotated in each terminal node panel. Each panel shows the omnibus test result, latent trait parameters, and an item-level RS heatmap color-coded by RS type.

Usage

plot_rs_tree(
  tree,
  rs,
  item_labels = NULL,
  tnex = 2.5,
  drop_terminal = TRUE,
  ...
)

Arguments

tree	A longitudinal_grmtree object.
rs	An rs_characterization object from rs_characterize.
item_labels	Optional character vector of short item labels. If NULL, uses "Item 1", "Item 2", etc.
tnex	Numeric scaling factor for terminal node panels (default: 2.5).
drop_terminal	Logical (default: TRUE).
...	Additional arguments passed to plot.modelparty.

Details

Each terminal node panel contains:

• Omnibus RS result: chi-squared, adjusted p-value, and detection status

• Latent parameters: mean shift at T2 and test-retest correlation (mu_T2 and cor(T1,T2)

• Item-level RS bar: colored cells for each item indicating RS type (blue = recalibration, orange = reprioritization, purple = both, gray = none)

Value

Invisibly returns the tree object.

Examples

library(grmtree)

# Load the synthetic longitudinal data
data("grmtree_long_data", package = "grmtree")

  # Prepare the wide-format response matrix
  items_t1 <- c("MOS_Listen", "MOS_Info", "MOS_Advice_Crisis", "MOS_Confide",
                "MOS_Advice_Want", "MOS_Fears", "MOS_Personal", "MOS_Understand")
  ld <- prepare_longitudinal_data(
    data = grmtree_long_data,
    items_t1 = items_t1,
    items_t2 = paste0(items_t1, "_year1"),
    covariates = c("sex", "age", "residency", "job",
                   "education", "comorbidity_count", "ever_smoker")
  )

  # Phase 1: fit the longitudinal GRM tree
  ltree <- longitudinal_grmtree(
    resp_wide ~ sex + age + residency + job +
      education + comorbidity_count + ever_smoker,
    data = ld, n_items = 8,
    control = grmtree.control(minbucket = 200)
  )

  # Phase 2: characterize response shift within each subgroup
  rs <- rs_characterize(ltree, p_adjust = "fdr",
    global_p_adjust = "bonferroni")

  # Plot the rs tree
  plot_rs_tree(ltree, rs,
  item_labels = c("Listen", "Info", "Crisis",
  "Confide", "Advice", "Fears",
  "Personal", "Understand"))

grmtree documentation built on Sept. 2, 2026, 1:07 a.m.