version bump prep

- changes made by Jules reviewed - added pingouin dependency
teanijarv · Apr 6, 2023 · dce4961 · dce4961
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diff --git a/HISTORY.rst b/HISTORY.rst
@@ -13,4 +13,12 @@ History
 * Fixed pairwise correlations threshold for multicollinearity assumption testing (0.3 -> 0.7)
 * Fixed partial regression plots fixed figure size
 * Added titles to diagnostic plots
-* Fixed the VIF to match with SPSS output by adding the constant to X
+* Fixed the VIF to match with SPSS output by adding the constant to X
+
+0.1.5 (2023-04-6)
+------------------
+
+* Added standardised beta coefficients to model output
+* Added partial and semi-partial correlations (unique variance) to model output
+* Fixed F-change degrees of freedom calculation
+* Fixed F-change p-value calculation
diff --git a/HLR/hierarchical_regression.py b/HLR/hierarchical_regression.py
@@ -72,7 +72,8 @@ def linear_reg(X, X_names, y):
     beta_conversion = [b * c for b,c in zip(coeffs_values, conversion)]
     std_coeffs = dict(zip(coeffs_keys, beta_conversion)) # 25/03/23 dictionary to store beta_coefficients
 
-    # 05/04/2023 - Calculate the partial and semi-partial (part in SPSS) correlations for each X value with y, while holding other X values as covariates
+    # 05/04/2023 - Calculate the partial and semi-partial (defined 'part' in SPSS) correlations
+    # for each X value with y, while holding other X values as covariates
     # Initialize an array to store partial and semi-partial correlation values
     x_cols = [X.columns.tolist()]
     semi_partial_correlations = np.zeros(len(x_cols[0]))
@@ -225,6 +226,10 @@ def hierarchical_regression(X, X_names, y, diagnostics=True, save_folder='result
         MSE (total) = total mean square error
         Beta coefs = coefficient values for intercept and predictors
         P-values (beta coefs) = p-values for intercept and predictors
+        Std Beta coefs = standardised beta coefficients for intercept and predictors
+        Partial correlation = partial correlation for each X value with y
+        Semi-partial correlation = semi-partial correlation for each X value with y
+        Unique Variance % = squared semi-partial correlation for unique variance of predictors
         R-squared change = r-squared change for model (Step 2 r-sq - Step 1 r-sq)
         F-value change = f change for model (Step 2 f - Step 1 f)
         P-value (F change) = p-value of f-change of model

diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # HLR - Hierarchical Linear Regression in Python
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7683809.svg)](https://doi.org/10.5281/zenodo.7683809) [![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)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7683809.svg)](https://doi.org/10.5281/zenodo.7683809) [![image](https://img.shields.io/pypi/v/HLR.svg)](https://pypi.python.org/pypi/HLR) [![Build Status](https://app.travis-ci.com/teanijarv/HLR.svg?branch=main)](https://app.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 regression without using programs like SPSS.
 
@@ -41,6 +41,7 @@ HLR is meant to be used with Python 3.x and has been tested on Python 3.7-3.9.
 - [SciPy](https://www.scipy.org/)
 - [Pandas](https://pandas.pydata.org/)
 - [statsmodels](https://www.statsmodels.org/)
+- [pingouin](https://pingouin-stats.org/)
 - [matplotlib](https://matplotlib.org/)
 - [seaborn](https://seaborn.pydata.org/)
 
@@ -158,6 +159,7 @@ Would be great if someone with more experience with packages would contribute wi
 #### Contributors
 [Toomas Erik Anijärv](https://github.com/teanijarv)
 [Rory Boyle](https://github.com/rorytboyle)
+[Jules Mitchell](https://github.com/JulesMitchell)
 
 #### 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.
diff --git a/requirements.txt b/requirements.txt
@@ -3,4 +3,5 @@ scipy==1.7.3
 pandas==1.3.5
 matplotlib==3.5.2
 seaborn==0.11.2
-statsmodels==0.13.2
+statsmodels==0.13.2
+pingouin==0.5.3
diff --git a/setup.py b/setup.py
@@ -15,7 +15,8 @@
                 'pandas>=1.3.5',
                 'matplotlib>=3.5.2',
                 'seaborn>=0.11',
-                'statsmodels>=0.13']
+                'statsmodels>=0.13',
+                'pingouin>=0.5.3']
 
 test_requirements = ['pytest>=3']
 
@@ -50,6 +51,6 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/teanijarv/HLR',
-    version='0.1.4',
+    version='0.1.5',
     zip_safe=False,
 )