lmdiag 0.4.1

Diagnostic Plots for Lineare Regression Models. Similar to plot.lm in R.

lmdiag

Python Library providing Diagnostic Plots for Linear Regression Models. (Like 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

pip install lmdiag

Usage

lmdiag generates plots for fitted linear regression models from statsmodels, linearmodels and scikit-learn.

You can find some usage examples in this jupyter notebook.

Example

import numpy as np
import statsmodels.api as sm
import lmdiag

# Fit model with random sample data
np.random.seed(20)
X = np.random.normal(size=30, loc=20, scale=3)
y = 5 + 5 * X + np.random.normal(size=30)
X = sm.add_constant(predictor)  # intercept required by statsmodels
lm = sm.OLS(y, X).fit()

# Plot lmdiag facet chart
lmdiag.style.use(style="black_and_red")  # Mimic R's plot.lm style
fig = lmdiag.plot(lm)
fig.show()

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 description to aid plot interpretation:

  lmdiag.help() (for all plots)

  lmdiag.help('<method name>') (for individual plot)

Increase performance

Plotting models fitted on large datasets might be slow. There are some things you can try to speed it up:

1. Tune LOWESS-parameters

The red smoothing lines are calculated using the "Locally Weighted Scatterplot Smoothing" algorithm, which can be quite expensive. Try a lower value for lowess_it and a higher value for lowess_delta to gain speed at the cost of accuracy:

lmdiag.plot(lm, lowess_it=1, lowess_delta=0.02)
# Defaults are: lowess_it=2, lowess_delta=0.005

(For details about those parameters, see statsmodels docs.)

2. Change matplotlib backend

Try a different matplotlib backend. Especially static backends like AGG or Cairo should be faster, e.g.:

import matplotlib
matplotlib.use('agg')

Setup development environment

python -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'
pre-commit install

Certification