HLR 0.1.2

HLR - Hierarchical Linear Regression for Python

HLR - Hierarchical Linear Regression in Python

HLR is a simple Python package for running hierarchical regression. It was created because there wasn't any good options to run hierarchical regressopm without using programs like SPSS.

It is built to work with Pandas dataframes, uses SciPy and statsmodels for all statistics and regression functions, and runs diagnostic tests for testing assumptions while plotting figures with matplotlib and seaborn.

Installation

HLR is meant to be used with Python 3.x and has been tested on Python 3.7-3.9.

Dependencies

• NumPy
• SciPy
• Pandas
• statsmodels
• matplotlib
• seaborn

User installation

To install HLR, run this command in your terminal:

pip install hlr

This is the preferred method to install HLR, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

Usage

Quick start

An example Jupyter Notebook can be found in 'example' subfolder with a sample dataset. You can also just run the code below.

import pandas as pd
import HLR

nba = pd.read_csv('example/NBA_train.csv')

# List of dataframes of predictor variables for each step
X = [nba[['PTS']],
     nba[['PTS', 'ORB']],
     nba[['PTS', 'ORB', 'BLK']]]

# List of predictor variable names for each step
X_names = [['points'],
           ['points', 'offensive_rebounds'], 
           ['points', 'offensive_rebounds', 'blocks']]

# Outcome variable as dataframe
y = nba[['W']]

# Create a HLR model with diagnostic tests, run and save the results
model = HLR.HLR_model(diagnostics=True, showfig=True, save_folder='results', verbose=True)
model_results, reg_models = model.run(X=X, X_names=X_names, y=y)
model.save_results(filename='nba_results', show_results=True)

OUTPUT (without figures):

	Step	Predictors	N (observations)	DF (residuals)	DF (model)	R-squared	F-value	P-value (F)	SSE	SSTO	MSE (model)	MSE (residuals)	MSE (total)	Beta coefs	P-values (beta coefs)	Failed assumptions (check!)	R-squared change	F-value change	P-value (F change)
0	1	[points]	835.0	833.0	1.0	0.089297	81.677748	1.099996e-18	123292.827686	135382.0	12089.172314	148.010597	162.328537	{'Constant': -13.846261266053896, 'points': 0....	{'Constant': 0.023091997486255577, 'points': 1...	[Homoscedasticity, Normality]	NaN	NaN	NaN
1	2	[points, offensive_rebounds]	835.0	832.0	2.0	0.168503	84.302598	4.591961e-34	112569.697267	135382.0	11406.151367	135.300117	162.328537	{'Constant': -14.225561767669713, 'points': 0....	{'Constant': 0.014660145903221372, 'points': 1...	[Normality, Multicollinearity]	0.079206	79.254406	3.372595e-18
2	3	[points, offensive_rebounds, blocks]	835.0	831.0	3.0	0.210012	73.638176	3.065838e-42	106950.174175	135382.0	9477.275275	128.700571	162.328537	{'Constant': -21.997353037483723, 'points': 0....	{'Constant': 0.00015712851466562279, 'points':...	[Normality, Multicollinearity, Outliers/Levera...	0.041509	43.663545	6.962046e-11

Documentation

Visit the documentation for more information. https://hlr-hierarchical-linear-regression.readthedocs.io

Development

HLR was created by Toomas Erik Anijärv using original code by Rory Boyle. The package is maintained by Toomas during his spare time, thereby contributions are more than welcome!

This program is provided with no warranty of any kind and it is still under heavy development. However, this code has been checked and validated against multiple same analyses conducted in SPSS.

To-do

Would be great if someone with more experience with packages would contribute with testing and the whole deployment process. Also, if someone would want to write documentation, that would be amazing.

• Documentation
• More thorough testing

Contributors

Toomas Erik Anijärv Rory Boyle

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

======= History

0.1.0 (2023-02-24)

• First release on PyPI.