Metadata-Version: 2.1
Name: HLR
Version: 0.1.0
Summary: HLR - Hierarchical Linear Regression for Python
Home-page: https://github.com/teanijarv/HLR
Author: Toomas Erik Anijärv
Author-email: toomaserikanijarv@gmail.com
License: GNU General Public License v3
Keywords: HLR
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS.rst

# HLR - Hierarchical Linear Regression in Python

[![image](https://img.shields.io/pypi/v/HLR.svg)](https://pypi.python.org/pypi/HLR) [![image](https://img.shields.io/travis/teanijarv/HLR.svg)](https://travis-ci.com/teanijarv/HLR) [![Documentation Status](https://readthedocs.org/projects/hlr-hierarchical-linear-regression/badge/?version=latest)](https://hlr-hierarchical-linear-regression.readthedocs.io/en/latest/?version=latest)

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 3.6-3.9.

#### Dependencies
- [NumPy](https://numpy.org/)
- [SciPy](https://www.scipy.org/)
- [Pandas](https://pandas.pydata.org/)
- [statsmodels](https://www.statsmodels.org/)
- [matplotlib](https://matplotlib.org/)
- [seaborn](https://seaborn.pydata.org/)

#### 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](https://pip.pypa.io/) installed, this [Python installation guide](http://docs.python-guide.org/en/latest/starting/installation/) 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.

```python
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
hlr_model = hlr.HLR(diagnostics=True, showfig=True, save_folder='results', verbose=True)
model_results, reg_models = hlr_model.run(X=X, X_names=X_names, y=y)
hlr_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](https://www.toomaserikanijarv.com) using original code by [Rory Boyle](https://github.com/rorytboyle). 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.

#### Contributors
[Toomas Erik Anijärv](https://github.com/teanijarv)
[Rory Boyle](https://github.com/rorytboyle)

#### Credits
This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.

=======
History
=======

0.1.0 (2023-02-24)
------------------

* First release on PyPI.
