Example

(See also the more extensive Example Notebook)

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);

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)

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

Packaging and Upload to PyPi

• pipenv run rstcheck README.rst (check syntax)

• rm -rf ./dist (delete old builds)

• python setup.py sdist

• python setup.py bdist_wheel

• twine upload dist/*

• Then new release on github…