fscores_longitudinal_grmtree: Compute Latent Factor Scores for Longitudinal GRM Tree

In grmtree: Recursive Partitioning for Graded Response Models

Compute Latent Factor Scores for Longitudinal GRM Tree

Description

Computes latent factor scores (theta) for both T1 and T2 within each terminal node of a longitudinal GRM tree. Unlike the cross-sectional fscores_grmtree which returns a single theta per person, this function returns two scores per person: \theta_{T1} and \theta_{T2}.

Usage

fscores_longitudinal_grmtree(object, method = "EAP", ...)

Arguments

object	A longitudinal_grmtree object.
method	Scoring method: "EAP" (default), "MAP", "ML", or "WLE".
...	Additional arguments (currently unused).

Value

A named list (one element per terminal node). Each element is a data.frame with columns:

Theta_T1

Estimated latent trait at Time 1

Theta_T2

Estimated latent trait at Time 2

See Also

longitudinal_grmtree for fitting the tree, generate_node_scores_dataset to generate node assignment and factor scores

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 the factor scores
  scores <- fscores_longitudinal_grmtree(ltree)
  # Scores for node 2
  head(scores[["2"]])

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