BAyesian Model-Building Interface (Bambi) in Python

Bambi is a high-level Bayesian model-building interface written in Python. It works with the probabilistic programming frameworks PyMC3 and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.

Dependencies

Bambi is tested on Python 3.6+ and depends on NumPy, Pandas, PyMC3, PyStan, Patsy and ArviZ (see requirements.txt for version information).

Installation

The latest release of Bambi can be installed using pip:

pip install bambi

Alternatively, if you want the bleeding edge version of the package, you can install from GitHub:

pip install git+https://github.com/bambinos/bambi.git

Usage

A simple fixed effects model is shown below as example.

from bambi import Model
import pandas as pd

# Read in a tab-delimited file containing our data
data = pd.read_table('my_data.txt', sep='\t')

# Initialize the model
model = Model(data)

# Fixed effects only model
results = model.fit('DV ~ IV1 + IV2', draws=1000, chains=4)

# Use ArviZ to plot the results
az.plot_trace(results)

# Key summary and diagnostic info on the model parameters
az.summary(results)

# Drop the first 100 draws (burn-in)
results_bi = results.sel(draw=slice(100, None))

For a more in-depth introduction to Bambi see our Quickstart or our set of example notebooks.

Contributing

We welcome contributions from interested individuals or groups! For information about contributing to Bambi, check out our instructions, policies, and guidelines here.

Contributors

See the GitHub contributor page.

Contents

• Getting Started
  • Quickstart
  • User Guide
    • Creating a model
      • Data format
    • Model specification
      • Formula-based specification
      • Incremental specification
      • Notes on common and group specific effects in Bambi
      • Coding of categorical variables
    • Fitting the model
      • Building the model
    • Specifying priors
      • Different ways of specifying priors
      • Mapping priors onto terms
    • Generalized linear mixed models
      • Families
    • Results
    • Plotting
    • Summarizing
    • Accessing back-end objects
• Examples
  • Logistic Regression
    • Load and examine American National Election Studies (ANES) data
    • Build clinton_model
      • Logistic regression
      • Specify and fit model in Bambi
    • Run Inference
    • Spaghetti plot of model predictions
  • Multi-level Regression
    • Load data
    • Model
    • Estimated regression line
  • T-test
    • Generate data
      • Model
      • Analysis
  • Multiple Regression
    • Load and examine Eugene-Springfield community sample data
    • Specify model and examine priors
    • Examine the model results
    • Summarize effects on partial correlation scale
    • Relative importance: Which predictors have the strongest effects (defined in terms of squared partial correlation?
  • Bayesian Workflow (Police Officer’s Dilemma)
    • Load and examine data
    • Fit response rate models
      • Subject specific effects only
      • Stimulus specific effects
    • Fit response models
  • Bayesian Workflow (Strack RRR Analysis Replication)
    • Reading in the data
    • Reshaping the data
    • Fitting the model
    • Inspecting results
    • Expanding the model
    • And the answer is…
  • Logistic Regression and Model Comparison with Bambi and Arviz
    • Adults dataset
    • The model
    • Model 1:
    • Model 2
    • Model 3
    • Model comparison
    • Probability estimation
      • Final remarks
• API Reference
  • bambi.models
  • bambi.priors

Indices

• Index

• Module Index

Getting Started