QAIDR

Quality Assessment for Interval-Based Dimensionality Reduction

QAIDR provides tools for evaluating how well interval-based dimensionality reduction (DR) methods preserve the structure of high-dimensional interval data, using a co-ranking matrix framework.

Features

• 4 interval distance metrics: Interval Euclidean, Hausdorff, Ichino-Yaguchi, L2-Wasserstein
• 6 co-ranking indices: Quality (Q) and Behavior (B) variants of Trustworthiness & Continuity, MRRE, and LCMC
• 6 DR method wrappers: C-PCA, V-PCA, MR-PCA, SPCA, IMDS, Int-UMAP
• Permutation tests for statistical significance
• Visualization: 2D projection plots and K-neighbourhood profile plots

Installation

Install the development version from GitHub:

# install.packages("pak")
pak::pak("hanmingwu1103/QAIDR")

Quick Start

library(QAIDR)

# Load and standardize the built-in Cars dataset
data(cars_mm)
x <- standardize(cars_mm)

# Compute interval distances
D <- idist(x, metric = "Wasserstein")

# Run all 6 DR methods (requires symbolicDA, RSDA)
proj <- run_idr(x)

# Assess quality across all method-metric combinations
result <- assess_quality(x, proj, K = 5, perm_test = TRUE, n_perm = 1000)
print(result)

# Visualize
plot_projections(proj, labels = cars_mm$labels)

profiles <- k_profiles(x, proj)
plot_k_profiles(profiles, metric = "Wasserstein")

Documentation

• Package website
• vignette("introduction") -- Basic workflow
• vignette("real-data-analysis") -- Reproduces the real data analysis
• vignette("simulation-study") -- Reproduces the simulation study

Citation

Paulo Canas Rodrigues and Han-Ming Wu (2025), Interval-metric co-ranking: quality assessment of dimensionality reduction for interval-valued data, Technical report.

License

MIT