cor_example: Example Correlation Matrix with Block Structure

In corrselect: Correlation-Based and Model-Based Predictor Pruning

Example Correlation Matrix with Block Structure

Description

A 20x20 correlation matrix with known block structure designed for demonstrating threshold selection, algorithm comparison, and visualization examples in vignettes.

Usage

cor_example

Format

A 20x20 numeric correlation matrix with row and column names V1-V20. The matrix has four distinct correlation blocks (realized pairwise ranges, after the positive-definiteness correction described in data-raw/create_cor_example.R – these can differ slightly from the runif() bounds used to generate each block):

Block 1 (V1-V5)

High correlation: mean = 0.81, range = (0.74, 0.88)

Block 2 (V6-V10)

Moderate correlation: mean = 0.57, range = (0.50, 0.63)

Block 3 (V11-V15)

Low correlation: mean = 0.28, range = (0.20, 0.36)

Block 4 (V16-V20)

Minimal correlation: mean = 0.06, range = (0.01, 0.12)

Between-block correlations are low (range = (0.0, 0.3)). The matrix is guaranteed to be positive definite.

Details

This dataset provides a controlled correlation structure useful for:

• Threshold sensitivity analysis (comparing results at tau = 0.5, 0.7, 0.9)

• Algorithm comparison (exact vs greedy modes)

• Visualization examples (heatmaps, correlation distributions)

• Reproducible benchmarks across vignettes

Expected behavior with different thresholds, verified via MatSelect(cor_example, threshold = tau) on the full matrix:

• tau = 0.5: Blocks 1 and 2 both require pruning (largest subset keeps 12 of 20 variables)

• tau = 0.7: Block 1 requires pruning; Block 2 is retained intact (largest subset keeps 16 of 20 variables)

• tau = 0.9: No block requires pruning (largest subset keeps all 20 variables)

Source

Generated with data-raw/create_cor_example.R using seed 20250125.

Examples

data(cor_example)

# Matrix dimensions
dim(cor_example)

# Visualize structure
if (requireNamespace("corrplot", quietly = TRUE)) {
  corrplot::corrplot(cor_example, method = "color", type = "upper",
                     tl.col = "black", tl.cex = 0.7)
}

# Distribution of correlations
hist(cor_example[upper.tri(cor_example)],
     breaks = 30,
     main = "Distribution of Correlations in cor_example",
     xlab = "Correlation",
     col = "steelblue")

# Use with MatSelect
library(corrselect)
results <- MatSelect(cor_example, threshold = 0.7, method = "els")
show(results)

corrselect documentation built on July 19, 2026, 1:06 a.m.