SEPA
Segment Profile Extraction via Pattern Analysis
Implements the Segment Profile Extraction via Pattern
Analysis method for row-mean-centered multivariate data. Core
capabilities include SVD-based row-isometric biplot
construction, bias-corrected and accelerated, and percentile
bootstrap confidence intervals for domain coordinates and
per-person direction cosines, Procrustes alignment of bootstrap
replicates across planes, parallel analysis for dimensionality
selection, and segment profile reconstruction in planes defined
by pairs of singular dimensions. A synthetic Woodcock-Johnson IV
look-alike dataset is provided for examples and testing. The
method is described in Kim and Grochowalski (2019)
SEPA: Subprofile Extraction via Pattern Analysis
SEPA implements the Subprofile Extraction via Pattern Analysis method for ipsatized multivariate data. Given a matrix of domain scores (e.g., WJ-IV broad ability scores), SEPA removes the level-elevation component, fits a rank-K row-isometric SVD biplot, and quantifies each person's alignment with each domain via direction cosines, with bootstrap confidence intervals.
Installation
# Install from CRAN:
install.packages("SEPA")
Quick start
library(SEPA)
# Run the full pipeline on the bundled synthetic dataset
res <- run_sepa(
data = fake_wj,
K = 4,
target_ids = c(724, 944, 1080, 2117),
B_dom = 2000,
B_cos = 2000,
seed = 20251003
)
print(res)
# SEPA result
# n = 5127 p = 7 K = 4
# Domains: LT, ST, CP, AP, VP, CK, FL
# PA significant dims: 1, 2, 3, 4
# Marker domains: LT, ST, VP, CK
# Exemplar cosine table for Plane 1
head(res$cosine_tables[["12"]], 14)
# Biplot
draw_sepa_biplot(
svd_fit = list(U = res$ref_fit$U, d = res$ref_fit$d_all, V = res$ref_fit$V),
id_vec = res$pid,
domain_names = res$domains,
ids_highlight = c(724, 944, 1080, 2117)
)
Key functions
| Function | Description |
|---|---|
run_sepa() |
Full pipeline wrapper |
sepa_stats_all() |
Plane-fit rho + direction cosines |
parallel_analysis_ipsatized() |
Permutation-based dimensionality selection |
boot_cis_all() |
BCa confidence intervals |
percentile_ci_mat() |
Percentile confidence intervals |
draw_sepa_biplot() |
Row-isometric SVD biplot |
simulate_sepa_fake_wj() |
Synthetic WJ-IV data generator |
Data
fake_wj is a 5,127-case synthetic dataset that approximates the WJ-IV marginal distributions. The original norming data are proprietary; this object provides a fully reproducible substitute.
Citation
If you use SEPA in your research, please cite:
Kim, S.-K. (2025). SEPA: Subprofile Extraction via Pattern Analysis. R package version 0.1.0. https://CRAN.R-project.org/package=SEPA
License
MIT © Se-Kang Kim