Usage edits

Author: teanijarv

README.md

@@ -7,7 +7,7 @@ HLR is a simple Python package for running hierarchical regression. It was creat
 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.
+HLR is meant to be used with Python 3.x and has been tested on Python 3.7-3.9.
 
 #### Dependencies
 - [NumPy](https://numpy.org/)
@@ -33,7 +33,7 @@ An example Jupyter Notebook can be found in 'example' subfolder with a sample da
 
 ```python
 import pandas as pd
-import hlr
+import HLR
 
 nba = pd.read_csv('example/NBA_train.csv')
 
@@ -51,9 +51,9 @@ X_names = [['points'],
 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)
+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) |
@@ -71,6 +71,10 @@ HLR was created by [Toomas Erik Anijärv](https://www.toomaserikanijarv.com) usi
 
 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
+- Documentation
+- More thorough testing
+
 #### Contributors
 [Toomas Erik Anijärv](https://github.com/teanijarv)
 [Rory Boyle](https://github.com/rorytboyle)

docs/usage.rst

@@ -4,4 +4,25 @@ Usage
 
 To use HLR - Hierarchical Linear Regression in a project::
 
+    import pandas as pd
+    import os
     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']]
+
+    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)

example/usage.ipynb

@@ -325,9 +325,9 @@
     "# Outcome variable as dataframe\n",
     "y = nba[['W']]\n",
     "\n",
-    "hlr_model = HLR.HLR_model(diagnostics=True, showfig=True, save_folder='results', verbose=True)\n",
-    "model_results, reg_models = hlr_model.run(X=X, X_names=X_names, y=y)\n",
-    "hlr_model.save_results(filename='test', show_results=True)"
+    "model = HLR.HLR_model(diagnostics=True, showfig=True, save_folder='results', verbose=True)\n",
+    "model_results, reg_models = model.run(X=X, X_names=X_names, y=y)\n",
+    "model.save_results(filename='nba_results', show_results=True)"
    ]
   }
  ],

setup.py

@@ -50,6 +50,6 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/teanijarv/HLR',
-    version='0.1.0',
+    version='0.1.1',
     zip_safe=False,
 )