return fig from plotting functions

.gitignore

@@ -1,2 +1,4 @@
 /example
 *__pycache__
+*.DS_Store
+*.ipynb

AUTHORS.rst

@@ -6,10 +6,10 @@ Development Lead
 ----------------
 
 * Toomas Erik Anijärv <[email protected]>
-* Rory Boyle <[email protected]>
 
 Contributors
 ------------
 
 * Jules Mitchell
+* Rory Boyle
 * Cate Scanlon

HISTORY.rst

@@ -37,4 +37,9 @@ History
 0.2.2 (2024-03-3)
 ------------------
 
-* Updated documentation
+* Updated documentation
+
+0.2.3 (2024-03-7)
+------------------
+
+* Added that the plotting functions return matplotlib figure object

HLR/__init__.py

@@ -2,7 +2,7 @@
 
 __author__ = """Toomas Erik Anijärv"""
 __email__ = '[email protected]'
-__version__ = '0.2.0'
+__version__ = '0.2.3'
 
 from .diagnostic_tests import (test_durbin_watson, 
                                test_pearsons_r,

HLR/diagnostic_tests.py

@@ -1,11 +1,4 @@
-"""Functions for diagnostic tests to test for assumptions.
-
-Authors
--------
-Toomas Erik Anijärv [email protected] github.com/teanijarv
-Rory Boyle [email protected] github.com/rorytboyle
-"""
-
+"""Functions for diagnostic tests to test for assumptions."""
 import scipy.stats
 import numpy as np
 from statsmodels.stats.stattools import durbin_watson

HLR/plots.py

@@ -1,3 +1,4 @@
+"""Functions for plotting."""
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
@@ -45,5 +46,4 @@ def create_subplot_partial_regression(model, fig_size, level):
     sm.graphics.plot_partregress_grid(model, fig=fig)
     fig.suptitle(f'Partial Regression Plots (Model Level {level})', y=1)
 
-    plt.tight_layout()
-    plt.show()
+    return fig

HLR/regression.py

@@ -1,10 +1,4 @@
-"""Functions for running hierarchical linear regression.
-
-Authors
--------
-Toomas Erik Anijärv [email protected] github.com/teanijarv
-Rory Boyle [email protected] github.com/rorytboyle
-"""
+"""HLR - Hierarchical Linear Regression."""
 import pandas as pd
 import statsmodels.api as sm
 import scipy.stats
@@ -215,7 +209,11 @@ def diagnostics(self, verbose=True):
         return diagnostics_results
 
     def plot_studentized_residuals_vs_fitted(self):
-        """Plots studentized residuals against fitted values for all model levels."""
+        """Plots studentized residuals against fitted values for all model levels.
+        
+        Returns:
+            matplotlib.figure.Figure: The matplotlib figure object.
+        """
         model_results = self.fit_models()
         num_levels = len(model_results)
 
@@ -229,9 +227,15 @@ def plot_studentized_residuals_vs_fitted(self):
 
         plt.tight_layout()
         plt.show()
+
+        return fig
 
     def plot_qq_residuals(self):
-        """Plots Normal QQ Plots for all model levels."""
+        """Plots Normal QQ Plots for all model levels.
+        
+        Returns:
+            matplotlib.figure.Figure: The matplotlib figure object.
+        """
         model_results = self.fit_models()
         fig, axs = plt.subplots(len(model_results), 1, figsize=(8, 4 * len(model_results)))
         if len(model_results) == 1:
@@ -243,8 +247,14 @@ def plot_qq_residuals(self):
         plt.tight_layout()
         plt.show()
 
+        return fig
+
     def plot_influence(self):
-        """Plots Influence Plots for all model levels."""
+        """Plots Influence Plots for all model levels.
+        
+        Returns:
+            matplotlib.figure.Figure: The matplotlib figure object.
+        """
         model_results = self.fit_models()
         fig, axs = plt.subplots(len(model_results), 1, figsize=(8, 4 * len(model_results)))
         if len(model_results) == 1:
@@ -256,8 +266,14 @@ def plot_influence(self):
         plt.tight_layout()
         plt.show()
 
+        return fig
+
     def plot_std_residuals(self):
-        """Plots Box Plots of Standardized Residuals for all model levels."""
+        """Plots Box Plots of Standardized Residuals for all model levels.
+        
+        Returns:
+            matplotlib.figure.Figure: The matplotlib figure object.
+        """
         model_results = self.fit_models()
         fig, axs = plt.subplots(len(model_results), 1, figsize=(8, 4 * len(model_results)))
         if len(model_results) == 1:
@@ -270,9 +286,15 @@ def plot_std_residuals(self):
 
         plt.tight_layout()
         plt.show()
+
+        return fig
 
     def plot_histogram_std_residuals(self):
-        """Plots Histogram of Standardized Residuals for all model levels."""
+        """Plots Histogram of Standardized Residuals for all model levels.
+        
+        Returns:
+            matplotlib.figure.Figure: The matplotlib figure object.
+        """
         model_results = self.fit_models()
         fig, axs = plt.subplots(len(model_results), 1, figsize=(8, 4 * len(model_results)))
         if len(model_results) == 1:
@@ -286,11 +308,21 @@ def plot_histogram_std_residuals(self):
         plt.tight_layout()
         plt.show()
 
+        return fig
+
     def plot_partial_regression(self):
-        """Plots Partial Regression Plots for all model levels."""
+        """Plots Partial Regression Plots for all model levels.
+        
+        Returns:
+            list: A list of matplotlib.figure.Figure objects, one for each model level.
+        """
         model_results = self.fit_models()
         num_ivs = max(len(ivs) for ivs in self.models.values())
         fig_size = (15, min(10, 5 * num_ivs))
 
+        fig_list = []
         for level, model in model_results.items():
-            plots.create_subplot_partial_regression(model, fig_size, level)
+            fig = plots.create_subplot_partial_regression(model, fig_size, level)
+            fig_list.append(fig)
+
+        return fig_list

README.md

@@ -57,12 +57,12 @@ hreg.summary()
 hreg.diagnostics(verbose=True)
 
 # Different plots (see docs for more)
-hreg.plot_studentized_residuals_vs_fitted()
-hreg.plot_qq_residuals()
-hreg.plot_influence()
-hreg.plot_std_residuals()
-hreg.plot_histogram_std_residuals()
-hreg.plot_partial_regression()
+fig1 = hreg.plot_studentized_residuals_vs_fitted()
+fig2 = hreg.plot_qq_residuals()
+fig3 = hreg.plot_influence()
+fig4 = hreg.plot_std_residuals()
+fig5 = hreg.plot_histogram_std_residuals()
+fig_list = hreg.plot_partial_regression()
 ```
 Output:
 |   | Model Level |                           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) |
@@ -129,30 +129,29 @@ Please use Zenodo DOI for citing the package in your work.
 
 #### Example
 
-Anijärv, T. E., Mitchell, J. and Boyle, R. (2024) ‘teanijarv/HLR: v0.2.2’. Zenodo. https://doi.org/10.5281/zenodo.7683808
+Anijärv, T. E., Mitchell, J. and Boyle, R. (2024) ‘teanijarv/HLR: v0.2.3’. Zenodo. https://doi.org/10.5281/zenodo.7683808
 ```
 @software{toomas_erik_anijarv_2024_7683808,
   author       = {Toomas Erik Anijärv, Jules Mitchell, Rory Boyle},
-  title        = {teanijarv/HLR: v0.2.2},
+  title        = {teanijarv/HLR: v0.2.3},
   month        = mar,
   year         = 2024,
   publisher    = {Zenodo},
-  version      = {v0.2.2},
+  version      = {v0.2.3},
   doi          = {10.5281/zenodo.7683808},
   url          = {https://doi.org/10.5281/zenodo.7683808}
 }
 ```
 
 ## 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!
+The HLR package was created and is maintained by [Toomas Erik Anijärv](https://www.toomaserikanijarv.com). It is updated during spare time, thereby contributions are more than welcome!
 
 This program is provided with no warranty of any kind and it is still under 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.
-- dict valus within df hard to read
+- dict values within df hard to read
 - add t stats for coefficients
-- give option for output only some columns not all
 - add regression type option (eg, for logistic regression)
 
 #### Contributors

docs/usage.rst

@@ -102,7 +102,7 @@ Plotting options for all model levels
 
 .. code-block:: python
 
-     hlr.plot_studentized_residuals_vs_fitted()
+     fig = hlr.plot_studentized_residuals_vs_fitted()
 
 Output:
 
@@ -113,7 +113,7 @@ Output:
 
 .. code-block:: python
 
-     hlr.plot_qq_residuals()
+     fig = hlr.plot_qq_residuals()
 
 Output:
 
@@ -124,7 +124,7 @@ Output:
 
 .. code-block:: python
 
-     hlr.plot_influence()
+     fig = hlr.plot_influence()
 
 Output:
 
@@ -135,7 +135,7 @@ Output:
 
 .. code-block:: python
 
-     hlr.plot_std_residuals()
+     fig = hlr.plot_std_residuals()
 
 Output:
 
@@ -146,7 +146,7 @@ Output:
 
 .. code-block:: python
 
-     hlr.plot_histogram_std_residuals()
+     fig = hlr.plot_histogram_std_residuals()
 
 Output:
 
@@ -157,7 +157,7 @@ Output:
 
 .. code-block:: python
 
-     hlr.plot_partial_regression()
+     fig_list = hlr.plot_partial_regression()
 
 Output:
 
@@ -166,4 +166,4 @@ Output:
    :align: center
    :width: 50%
 
-(only Model Level 1 displayed, but actual output would plot all levels)
+(the fig_list contains a fig for each Model Level; only Model Level 1 displayed (i.e., fig_list[0]))

setup.py

@@ -44,6 +44,6 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/teanijarv/HLR',
-    version='0.2.2',
+    version='0.2.3',
     zip_safe=False,
 )