forbes2023: Assessing Mental Health symptom correlation matrix (Forbes...

In ackwards: Bass-Ackwards Hierarchical Structural Analysis

Assessing Mental Health symptom correlation matrix (Forbes 2023 applied example)

Description

The 155 x 155 Spearman correlation matrix among 155 mental-health symptom variables that forms the applied example in Forbes (2023). It is a real, deep hierarchy: ackwards(forbes2023, k_max = 10) unfolds a general factor of psychopathology at the top down to 10 fine-grained components, the worked example that motivates the Forbes extension (pairs = "all", prune()).

Usage

forbes2023

Format

A 155 x 155 numeric matrix of Spearman correlations: symmetric, unit diagonal, correlations in roughly 0.01–0.94. Row and column names are the 155 symptom-variable labels (e.g. Impulsivity, Blurting).

Details

Where sim16 and bfi25 are teaching foils, forbes2023 is a fidelity/reproduction dataset: a large, messy, published case bundled so the package can reproduce the exact applied example analyzed in Forbes's paper.

The correlations come from the Assessing Mental Health (AMH) study – the Australian general-population sample (N = 3,175) of Forbes et al. (2021) – spanning symptoms of 18 DSM disorders. Being a correlation matrix, it carries no per-variable sample size; supply n_obs = 3175 to ackwards() if you want EFA/ESEM fit statistics scaled to the original sample.

This matrix reproduces Forbes's published results exactly: the package regression test test-forbes-fidelity.R runs ackwards() on this exported forbes2023 and matches her reference implementation's between-level correlations to 1.3e-14 across all 45 level-pairs at k_max = 10.

To regenerate this dataset, run source("data-raw/forbes2023.R") from the package root (downloads the source CSV from OSF).

Source

Forbes's OSF project for the 2023 paper (file corSpearman_AMH.csv), https://osf.io/pcwm8/, redistributed here under its Creative Commons Attribution 4.0 International (CC-BY 4.0) license (see the package's LICENSE.note). The underlying data are from the Assessing Mental Health study (Forbes et al., 2021).

References

Forbes, M. K. (2023). Improving hierarchical models of individual differences: An extension of Goldberg's bass-ackward method. Psychological Methods. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000546")}

Forbes, M. K., Sunderland, M., Rapee, R. M., Batterham, P. J., Calear, A. L., Carragher, N., Ruggero, C., Zimmerman, M., Baillie, A. J., Lynch, S. J., Mewton, L., Slade, T., & Krueger, R. F. (2021). A detailed hierarchical model of psychopathology: From individual symptoms up to the general factor of psychopathology. Clinical Psychological Science, 9(2), 139–168. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/2167702620954799")}

See Also

vignette("ackwards-forbes2023") for a full reproduction of the Forbes (2023) applied example on this dataset, and vignette("ackwards-forbes") for the method extension itself.

Examples

dim(forbes2023)
forbes2023[1:3, 1:3]

# The Forbes (2023) applied example: a 10-level hierarchy from 155 symptoms.
x <- ackwards(forbes2023, k_max = 10, pairs = "all")
x

ackwards documentation built on July 25, 2026, 1:08 a.m.