sarlib 0.0.2

Statistical Analysis and Regression Library

SARLIB: Statistical Analysis and Regression Library

sarlib - Statistical Analysis and Regression Library

This library provides tools for statistical analysis, regression modeling, sample size analysis, and visualization. It includes OLS and SAR models, as well as utilities for data preprocessing and plotting.

Installation

sarlib is a standalone Python module. To use it, ensure you have the following dependencies installed:

• numpy
• matplotlib
• statsmodels
• scikit-learn
• scipy

You can install these with pip:

pip install numpy matplotlib statsmodels scikit-learn scipy

Usage

Import the module in your Python script:

import sarlib

Or copy the code into your project and import the classes/functions as needed.

Main Components

• fix_data(x, y): Standardizes and cleans input data.

• show_scatter(x, y, ...): Visualizes predictors vs. response.

• OLS: Ordinary Least Squares regression with permutation-based significance and power analysis.

• SAR: Statistical Analysis Regression with PAC-Bayes, Vapnik, and IGP bounds.

• SampleSizeAnalysis: Analyzes the effect of sample size on model performance and statistics.

Example Workflow

1. Prepare your data as numpy arrays:

   import numpy as np x = np.random.randn(100, 3) # predictors y = np.random.randn(100) # response

2. Visualize data:

   show_scatter(x, y)

3. Fit OLS model:

   model_ols = OLS(n_realiz=100, alpha=0.05) stats_ols = model_ols.fit(x, y, verbose=True)

4. Fit SAR model:

   model_sar = SAR(n_realiz=100, norm='epsins', alpha=0.05) stats_sar = model_sar.fit(x, y, verbose=True)

5. Analyze sample size effect:

   analysis = SampleSizeAnalysis(model_sar, x, y, steps=7) analysis.plot_loss() analysis.plot_pvalue() analysis.plot_coef()

Function/Class Documentation

All functions and classes are documented with docstrings. Please refer to the code for parameter details and usage.

License & Author

Author: Sipba Group, UGR, https://sipba.ugr.es/ License: GPL Version 3