update docs

teanijarv · Mar 3, 2024 · 8d14bf4 · 8d14bf4
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diff --git a/.DS_Store b/.DS_Store
diff --git a/docs/images/plot_histogram_std_residuals.png b/docs/images/plot_histogram_std_residuals.png
diff --git a/docs/images/plot_influence.png b/docs/images/plot_influence.png
diff --git a/docs/images/plot_partial_regression.png b/docs/images/plot_partial_regression.png
diff --git a/docs/images/plot_qq_residuals.png b/docs/images/plot_qq_residuals.png
diff --git a/docs/images/plot_std_residuals.png b/docs/images/plot_std_residuals.png
diff --git a/docs/images/plot_studentized_residuals_vs_fitted.png b/docs/images/plot_studentized_residuals_vs_fitted.png
diff --git a/docs/usage.rst b/docs/usage.rst
@@ -4,8 +4,8 @@ Usage
 
 Quick start guide to use hierarchical linear regression using HLR package.
 
-Initialising the HLR object
----------------------------
+Fetch example data
+------------------
 
 Let's first fetch some data and initiate the HLR object. We'll use the `penguins` dataset from `seaborn` for our example.
 
@@ -19,8 +19,8 @@ Let's first fetch some data and initiate the HLR object. We'll use the `penguins
      df.dropna(inplace=True)
      df = df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']]
 
-Initialising the HLR object and generating summary report
----------------------------------------------------------
+Initialize HLR & generate summary report
+----------------------------------------
 
 .. code-block:: python
 
@@ -40,19 +40,108 @@ Initialising the HLR object and generating summary report
      hlr = HierarchicalLinearRegression(df, ivs_dict, dv)
      hlr.summary()
 
+Output:
++--------------+-------------------------------------------------------+-------------------+----------------+------------+------------+-----------+----------------+-----------------+-----------------+-------------+-----------------+---------------------------+---------------------+--------------------------------+----------------------------------+----------------------------------+------------------+----------------+--------------------------+
+| Model Level  | Predictors                                            | N (observations)  | DF (residuals) | DF (model) | R-squared  | F-value   | P-value (F)    | SSR             | SSTO            | MSE (model) | MSE (residuals) | MSE (total)               | Beta coefs         | P-values (beta coefs)          | Std Beta coefs                  | Partial correlations           | Semi-partial correlations       | Unique variance % | R-squared change | F-value change          | P-value (F-value change) |
++==============+=======================================================+===================+================+============+============+===========+================+=================+=================+=============+=================+===========================+=====================+================================+==================================+==================================+==================+==================+==========================+
+| 1            | [bill_length_mm]                                      | 333.0             | 331.0          | 1.0        | 0.347453   | 176.242854| 1.538614e-32   | 1.404671e+08   | 2.152597e+08   | ...         | 648372.487699   | {'const': 388.84515876027484, 'bill_length_mm': ...} | {'const': 0.1806158931602473, 'bill_length_mm': ...} | {'bill_length_mm': 0.5894511101769488} | {'bill_length_mm': 0.589451110176949} | {'bill_length_mm': 0.589451110176949} | {'bill_length_mm': 34.74526112888376} | NaN              | NaN              | NaN                        |
++--------------+-------------------------------------------------------+-------------------+----------------+------------+------------+-----------+----------------+-----------------+-----------------+-------------+-----------------+---------------------------+---------------------+--------------------------------+----------------------------------+----------------------------------+----------------------------------+------------------+------------------+--------------------------+
+| 2            | [bill_length_mm, bill_depth_mm]                       | 333.0             | 330.0          | 2.0        | 0.467465   | 144.838513| 7.038981e-46   | 1.146334e+08   | 2.152597e+08   | ...         | 648372.487699   | {'const': 3413.451851285957, 'bill_length_mm': ...} | {'const': 8.498889663375682e-14, 'bill_length_mm': ...} | {'bill_length_mm': 0.5080941489911919, 'bill_depth_mm': ...} | {'bill_length_mm': 0.5610736136532921, 'bill_depth_mm': ...} | {'bill_length_mm': 0.4946369788440619, 'bill_depth_mm': ...} | {'bill_length_mm': 24.46657408399809, 'bill_depth_mm': ...} | 0.120012         | 74.368625        | 2.760228e-16               |
++--------------+-------------------------------------------------------+-------------------+----------------+------------+------------+-----------+----------------+-----------------+-----------------+-------------+-----------------+---------------------------+---------------------+--------------------------------+----------------------------------+----------------------------------+----------------------------------+------------------+------------------+--------------------------+
+| 3            | [bill_length_mm, bill_depth_mm, flipper_length_mm]    | 333.0             | 329.0          | 3.0        | 0.763937   | 354.897950| 9.260836e-103  | 5.081491e+07   | 2.152597e+08   | ...         | 648372.487699   | {'const': -6445.4760430301985, 'bill_length_mm': ...} | {'const': 1.5260941153089323e-25, 'bill_length_mm': ...} | {'bill_length_mm': 0.022363660870482777, 'bill_depth_mm': ..., 'flipper_length_mm': ...} | {'bill_length_mm': 0.03381286549878069, 'bill_depth_mm': ..., 'flipper_length_mm': ...} | {'bill_length_mm': 0.01643783598133535, 'bill_depth_mm': ..., 'flipper_length_mm': ...} | {'bill_length_mm': 0.027020245174928313, 'bill_depth_mm': ..., 'flipper_length_mm': ...} | 0.296472         | 413.191418       | 4.446424e-60               |
++--------------+-------------------------------------------------------+-------------------+----------------+------------+------------+-----------+----------------+-----------------+-----------------+-------------+-----------------+---------------------------+---------------------+--------------------------------+----------------------------------+----------------------------------+----------------------------------+------------------+------------------+--------------------------+
+
 Run diagnostics for testing assumptions
 ---------------------------------------
 
 .. code-block:: python
+
      diagnostics_dict = hlr.diagnostics(verbose=True)
 
+Output:
+.. code-block:: text
+
+     Model Level 1 Diagnostics:
+     Independence of residuals (Durbin-Watson test):
+     DW stat: 0.8450671190941991
+     Passed: False
+     Linearity (Pearson r):
+     bill_length_mm: {'Pearson r': 0.5894511101769488, 'p-value': 1.5386135144860176e-32, 'Passed': True}
+     Linearity (Rainbow test):
+     Rainbow Stat: 0.845825915500362
+     p-value: 0.8589217163587981
+     Passed: True
+     Homoscedasticity (Breusch-Pagan test):
+     Lagrange Stat: 76.51043993569607
+     p-value: 2.1905189444330245e-18
+     Passed: False
+     Homoscedasticity (Goldfeld-Quandt test):
+     F-Stat: 3.298385120028286
+     p-value: 5.1841847326260096e-14
+     Passed: False
+     Multicollinearity (pairwise correlations):
+     Correlations: {}
+     Passed: True
+     Multicollinearity (Variance Inflation Factors):
+     VIFs: {}
+     Passed: True
+     Outliers (extreme standardized residuals):
+     Indices: []
+     Passed: True
+     Outliers (high Cooks distance):
+     Indices: []
+     Passed: True
+     Normality (mean of residuals):
+     Mean: -2.403469482162693e-13
+     Passed: True
+     Normality (Shapiro-Wilk test):
+     SW Stat: 0.9912192354166119
+     p-value: 0.04492289320888261
+     Passed: False
+
+     Model Level 2 Diagnostics:
+     ...
+
 Plotting options for all model levels
 -------------------------------------
 
 .. code-block:: python
+
      hlr.plot_studentized_residuals_vs_fitted()
      hlr.plot_qq_residuals()
      hlr.plot_influence()
      hlr.plot_std_residuals()
      hlr.plot_histogram_std_residuals()
-     hlr.plot_partial_regression()
+     hlr.plot_partial_regression()
+
+Output:
+.. image:: /images/plot_studentized_residuals_vs_fitted.png
+   :alt: plot_studentized_residuals_vs_fitted
+   :align: center
+   :width: 50%
+
+.. image:: /images/plot_qq_residuals.png
+   :alt: plot_qq_residuals
+   :align: center
+   :width: 50%
+
+.. image:: /images/plot_influence.png
+   :alt: plot_influence
+   :align: center
+   :width: 50%
+
+.. image:: /images/plot_std_residuals.png
+   :alt: plot_std_residuals
+   :align: center
+   :width: 50%
+
+.. image:: /images/plot_histogram_std_residuals.png
+   :alt: plot_histogram_std_residuals
+   :align: center
+   :width: 50%
+
+(only Model Level 1 displayed, but actual output would plot all levels)
+.. image:: /images/plot_partial_regression.png
+   :alt: plot_partial_regression
+   :align: center
+   :width: 50%