latentpar_longitudinal_grmtree: Extract Latent Trait Parameters from Longitudinal GRM Tree

In grmtree: Recursive Partitioning for Graded Response Models

Extract Latent Trait Parameters from Longitudinal GRM Tree

Description

Extracts the latent trait distribution parameters from each terminal node of a longitudinal GRM tree: the T2 latent mean shift (\mu_{T2}), the T2 latent variance (\sigma^2_{T2}), and the T1-T2 correlation.

Usage

latentpar_longitudinal_grmtree(object, node = NULL, ...)

Arguments

object	A longitudinal_grmtree object.
node	Optional vector of node IDs. If NULL, all terminal nodes.
...	Additional arguments (currently unused).

Value

A data.frame with columns:

Node

Terminal node ID

n

Sample size in the node

mu_T2

Latent mean at T2 (positive = improvement)

sigma2_T2

Latent variance at T2

cor_T1_T2

Correlation between T1 and T2 latent traits

See Also

longitudinal_grmtree for Phase 1 (tree fitting), fscores_longitudinal_grmtree, for computing factor scores for longitudinal GRMTree

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)
  )

  # Print latent trait summary
  latent <- latentpar_longitudinal_grmtree(ltree)
  print(latent)

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