Gabriel Jobert

2023-11-05

This repository contains the R Markdown file and associated resources for a simulation study. The paper focuses on employing a Monte Carlo simulation approach to evaluate the impact of missing data on the performance and stability of various statistical methods.

The primary objective of this study is to provide insights into how missing data can affect the outcome and reliability of different statistical learning techniques.

• R Programming: The analysis and simulations are conducted using R.
• Libraries:
  • MASS: For support with statistical functions.
  • caret: For data splitting, pre-processing, feature selection, etc.
  • cluster & mclust: For clustering analysis.
  • infotheo: For information theory-based methods.
  • RANN: For approximate nearest neighbor techniques.
  • ggplot2: For creating elegant data visualisations.
  • dplyr: For data manipulation.

• Simulation Paper.Rmd: The main R Markdown file containing the study, code, and documentation.
• HTML version : The paper in plain word from the RMD file in html version (prefer reading this version than the pdf one).
• PDF version : Paper in plain word in PDF version.

1. Ensure you have R and the necessary libraries installed.
2. Clone this repository.
3. Open the SImulation Paper.Rmd file in an R environment like RStudio.
4. Run the code chunks sequentially to reproduce the analysis and view the results.

This README is a brief overview of the 'Simulation Paper on Statistical Learning'. For more detailed information, please refer to the Rmd file itself.