DataGen_rare_group_usage"

In MUGS: Multisource Graph Synthesis with EHR Data

knitr::opts_chunk$set(
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Introduction

The DataGen_rare_group function generates synthetic data for rare group analysis, simulating structured datasets for testing and validating algorithms. This vignette demonstrates how to use DataGen_rare_group with example inputs.

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Load the Required Library

Ensure the MUGS package is loaded before running the example:

library(MUGS)

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Generate Synthetic Data

Run the DataGen_rare_group function to generate the synthetic dataset:

# Generate data
seed =1
p = 5
n1 = 100
n2 = 100
n.common = 50
n.group = 30
sigma.eps.1 = 1
sigma.eps.2 = 3
ratio.delta = 0.05
network.k = 5
rho.beta = 0.5
rho.U0 = 0.4
rho.delta = 0.7
sigma.rare = 10
n.rare = 20
group.size = 5

DataGen.out <- DataGen_rare_group(seed, p, n1, n2, n.common, n.group, sigma.eps.1, sigma.eps.2, ratio.delta, network.k, rho.beta, rho.U0, rho.delta, sigma.rare, n.rare, group.size)

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Examine the Output

Explore the structure and key components of the generated dataset:

# View structure of the output
str(DataGen.out)

# Print the first few rows and columns of the S.1 matrix
cat("\nFirst 5 rows and columns of S.1:\n")
print(DataGen.out$S.1[1:5, 1:5])

# Print the first few rows and columns of the S.2 matrix
cat("\nFirst 5 rows and columns of S.2:\n")
print(DataGen.out$S.2[1:5, 1:5])

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Notes

1. Custom Parameters: Modify parameters like p, n1, n2, n.group, and others to test different scenarios.
2. Reproducibility: The seed parameter ensures reproducibility of results.
3. Applications: Use the generated data for testing rare group detection algorithms or performance benchmarking.

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Summary

This vignette demonstrated how to use the DataGen_rare_group function to simulate structured data for rare group analysis. Adjust input parameters to suit specific use cases or experimental setups. For further details, refer to the package documentation.

MUGS documentation built on June 8, 2025, 12:35 p.m.