Read Excel file with fallback options

Description

Read Excel file with fallback options

Usage

.read_xlsx(path)

Example Eating Disorder Diagnostic Data

Description

Individual-level binary diagnostic data for eating disorder patients, including nine psychiatric diagnoses and pre/post treatment measures.

Usage

ANR2

Format

A data frame with 1261 rows and 13 variables:

MDD

Major Depressive Disorder (0 = absent, 1 = present)

DYS

Dysthymia (0 = absent, 1 = present)

DEP

Depression (0 = absent, 1 = present)

PTSD

Post-Traumatic Stress Disorder (0 = absent, 1 = present)

OCD

Obsessive-Compulsive Disorder (0 = absent, 1 = present)

GAD

Generalized Anxiety Disorder (0 = absent, 1 = present)

ANX

Anxiety (0 = absent, 1 = present)

SOPH

Social Phobia (0 = absent, 1 = present)

ADHD

Attention-Deficit/Hyperactivity Disorder (0 = absent, 1 = present)

pre_bmi

Pre-treatment Body Mass Index (numeric)

post_bmi

Post-treatment Body Mass Index (numeric)

pre_EDI

Pre-treatment Eating Disorder Inventory score (numeric)

post_EDI

Post-treatment Eating Disorder Inventory score (numeric)

Source

Baylor College of Medicine eating disorder treatment program data. Data have been de-identified and anonymized for research purposes.

Examples

data(ANR2)

# Examine structure
str(ANR2)

# View diagnostic variables
vars <- c("MDD", "DYS", "DEP", "PTSD", "OCD", "GAD", "ANX", "SOPH", "ADHD")
head(ANR2[, vars])

# Check prevalence of diagnoses
colMeans(ANR2[, vars], na.rm = TRUE)

Compute Aggregated Latent Space Index (ALSI)

Description

Calculates ALSI as a variance-weighted Euclidean norm of row principal coordinates within a retained K-dimensional MCA subspace.

Usage

alsi(Fmat, eig, K)

Arguments

Value

S3 object of class alsi containing:

Examples

# Create example data
set.seed(123)
Fmat <- matrix(rnorm(100 * 4), nrow = 100, ncol = 4)
eig <- c(0.5, 0.3, 0.15, 0.05)

# Compute ALSI
a <- alsi(Fmat, eig, K = 3)
print(a)
hist(a$alpha, main = "Distribution of ALSI")

Example workflow using the ALSI package

Description

Example workflow using the ALSI package

Usage

alsi_workflow(
  path,
  vars,
  B_pa = 2000,
  B_boot = 2000,
  q = 0.95,
  seed = 20260123
)

Arguments

Value

List containing all analysis objects

Examples

# Complete workflow
results <- alsi_workflow(
  path = "ANR2.xlsx",
  vars = c("MDD", "DYS", "DEP", "PTSD", "OCD", "GAD", "ANX", "SOPH", "ADHD"),
  B_pa = 2000,
  B_boot = 2000
)

# Access components
results$pa       # Parallel analysis
results$boot     # Bootstrap stability
results$alsi     # ALSI values

Create disjunctive (indicator) matrix from binary data

Description

Create disjunctive (indicator) matrix from binary data

Usage

make_disjunctive(X01)

Align MCA solution via Procrustes rotation with sign anchoring

Description

Align MCA solution via Procrustes rotation with sign anchoring

Usage

mca_align(G, Gref)

Arguments

Value

List with aligned coordinates and rotation matrix

Bootstrap-Based Subspace Stability Assessment

Description

Evaluates reproducibility of retained MCA dimensions via bootstrap resampling. Quantifies stability using Procrustes principal angles (subspace-level) and Tucker's congruence coefficients (dimension-level).

Usage

mca_bootstrap(data, vars, K, B = 2000, seed = 20260123, verbose = TRUE)

Arguments

Value

S3 object of class mca_bootstrap containing:

Examples

data(ANR2)
boot <- mca_bootstrap(ANR2, vars = names(ANR2), K = 3, B = 100)
print(boot)
plot(boot)

Perform Multiple Correspondence Analysis on binary indicator matrix

Description

Perform Multiple Correspondence Analysis on binary indicator matrix

Usage

mca_indicator(Xbin01)

Arguments

Value

List containing MCA results with eigenvalues, coordinates, and masses

Parallel Analysis for MCA Dimensionality Assessment

Description

Compares observed MCA eigenvalues against reference distributions from permuted data to identify statistically meaningful dimensions.

Usage

mca_pa(
  data,
  vars,
  B = 2000,
  q = 0.95,
  seed = 20260123,
  max_dims = 20,
  verbose = TRUE
)

Arguments

Value

S3 object of class mca_pa containing:

Examples

# Using included ANR2 dataset
data(ANR2)
pa <- mca_pa(ANR2, vars = names(ANR2), B = 100)
print(pa$K_star)

Plot Category Projections in MCA Space

Description

Visualizes category coordinates in a 2D MCA subspace and optionally displays projections onto the aggregated ALSI direction.

Usage

plot_category_projections(
  fit,
  K,
  alpha_vec = NULL,
  dim_pair = c(1, 2),
  cex = 0.8,
  top_n = 15
)

Arguments

Value

No return value, called for side effects. The function creates a scatter plot of category coordinates in the specified 2D subspace, with category labels displayed. If alpha_vec is provided, it also prints the top categories ranked by their absolute projection onto the ALSI direction to the console.

Plot Subspace Stability Diagnostics

Description

Creates diagnostic plots showing distributions of principal angles and Tucker congruence coefficients across bootstrap resamples.

Usage

plot_subspace_stability(boot_obj)

Arguments

Value

No return value, called for side effects. The function creates a two-panel figure with: (1) boxplots of principal angles (left panel), showing the distribution of subspace similarity across bootstrap resamples for each dimension; and (2) boxplots of Tucker congruence coefficients (right panel), showing dimension-level replicability with reference lines at phi = 0.85 (good) and phi = 0.95 (excellent).

Summarize matrix columns (median and quantiles)

Description

Summarize matrix columns (median and quantiles)

Usage

summarise_matrix(X, probs = c(0.05, 0.95))

Convert various formats to binary 0/1

Description

Convert various formats to binary 0/1

Usage

to01(x)