Metadata-Version: 1.1
Name: lmdiag
Version: 0.1.2
Summary: Diagnostic Plots for Lineare Regression Models. Similar to plot.lm in R.
Home-page: http://github.com/dynobo/lmdiag
Author: dynobo
Author-email: dynobo@mailbox.org
License: MIT
Description: lmdiag
        =======
        
        **Python Library providing Diagnostic Plots for Lineare Regression Models.** (Like `plot.lm <https://www.rdocumentation.org/packages/stats/versions/3.5.0/topics/plot.lm>`_ in R.)
        
        I built this, because I missed the diagnostics plots of R for a university project. There are some substitutions in Python for individual charts, but they are spread over different libraries and sometimes don't show the exact same. My implementation tries to copycat the R-plots, but I didn't reimplement the R-code: The charts are just based on available documentation.
        
        Installation
        ------------
        
        Available in PyPi: https://pypi.org/project/lmdiag/
        
        - Using pip: ``pip install lmdiag``
        - Using pipenv: ``pipenv install lmdiag``
        
        Usage
        -----------
        
        The plots need a `fitted Linear Regression Model <https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.fit.html>`_ created by statsmodels as input.
        
        Example
        ........
        (See also the more extensive `Example Notebook <https://github.com/dynobo/lmdiag/blob/master/example.ipynb>`_)
        
        ::
        
                import numpy as np
                import matplotlib.pyplot as plt
                import statsmodels.api as sm
                import lmdiag
        
                %matplotlib inline  # In Jupyter
        
                # Generate sample model
                np.random.seed(20)
                predictor = np.random.normal(size=30, loc=20, scale=3)
                response = 5 + 5 * predictor + np.random.normal(size=30)
                X = sm.add_constant(predictor)
                lm = sm.OLS(response, X).fit()
        
                # Plot chart matrix (and enlarge figure)
                plt.figure(figsize=(10,7))
                lmdiag.plot(lm);
        
        
        .. image:: https://raw.githubusercontent.com/dynobo/lmdiag/master/example.png
        
        
        Methods
        ........
        
        - Draw matrix of all plots:
        
          ``lmdiag.plot(lm)``
        
        - Draw individual plots:
        
          ``lmdiag.resid_fit(lm)``
        
          ``lmdiag.q_q(lm)``
        
          ``lmdiag.scale_loc(lm)``
        
          ``lmdiag.resid_lev(lm)``
        
        - Print useful descriptions for interpretations:
        
          ``lmdiag.info()`` (for all plots)
        
          ``lmdiag.info('<method name>')`` (for individual plot)
        
        Development
        ------------
        
        Disclaimer
        ..........
        
        This is my very first public python library. Don't expect everything to work smoothly. I'm happy to receive useful feedback or pull requests.
        
        Certification
        ..............
        .. image:: https://raw.githubusercontent.com/dynobo/lmdiag/master/badge.png
        
        Packaging and Upload to PyPi
        ............................
        
        - ``python setup.py sdist``
        - ``python setup.py bdist_wheel``
        - ``twine upload dist/*``
        
Keywords: lm lineare regression diagnostics plot chart r matplotlib
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Information Analysis
