GLAMbox

GLAMbox is a Python toolbox for investigating the association between gaze allocation and decision behaviour, and applying the Gaze-weighted Linear Accumulator Model (Thomas, Molter et al., 2019, full text available online).

See the GLAMbox paper for detailed background, model description and example applications.

Installation

GLAMbox is written for Python 3.7 and requires a working Python environment running on your computer. We recommend to install the Anaconda Distribution (available for all major platforms). With the Python environment fully set up, the GLAMbox module can be installed from the command line using pip:

pip install glambox

This command also installs all of GLAMbox’s dependencies, which are listed in the requirements.txt file in the Github repository.

Tip

Using conda you can install glambox into a new environment, separate from your base environment:

1. Create a new environment named glambox: conda create -n glambox python=3.7
2. Activate it: conda activate glambox
3. Install the glambox module and its dependencies: pip install glambox

Quickstart

Fitting the GLAM to a dataset can be done in just a few lines of code:

import glambox as gb
import pandas as pd

# load dataset (format must be GLAMbox compatible, of course)
data = pd.read_csv('data.csv')

# create the GLAM model object
model = gb.GLAM(data)

# build the PyMC3 model
model.make_model(kind='individual')

# perform MCMC sampling
model.fit()

# inspect parameter estimates
print(model.estimates)

# predict data using MAP estimates, save predictions
model.predict()
model.prediction.to_csv('prediction.csv')

A more detailed overview of the available functions can be found in the Basic Usage section and the API Reference.

Application Examples

This documentation includes the three usage examples outlined in the GLAMbox paper with full code. The original Jupyter notebook files for these examples can be found in the examples folder in the Github repository. When downloaded and run with Jupyter (also included in the Anaconda Python distribution), the notebooks can be ran interactively.

Example 1: Individual gaze biases

In this example, we demonstrate individual model fitting, model comparisons between model variants, and out-of-sample prediction.

Example 2: Hierarchical parameter estimation

In the second example, we demonstrate how to setup a hierarchical model with multiple groups, and compare parameter estimates between groups.

Example 3: Parameter Recovery

In the last example, we demonstrate how to perform a basic parameter recovery analyis for a given dataset, using GLAMbox.

Contents

• Home
• Basic usage
  • Data format, the GLAM class
  • Inference
  • Accessing parameter estimates
  • Comparing parameters between groups or conditions
  • Comparing model variants
  • Predicting choices and response times
  • References
• API Reference
  • The GLAM class
  • The analysis and plots submodules
• License

Examples

• Example 1: Individual gaze biases
  • 1. Simulating data
  • 2. Exploring the behavioural data
  • 3. Likelihood-based model comparison
  • 4. Out-of-sample prediction
  • 5. References:
• Example 2: Hierarchical parameter estimation in cases with limited data
  • 1. Simulating Data
  • 2. Exploring behavioural differences
  • 3. Building the hierarchical model
  • 4. Parameter estimation
  • 5. Evaluating parameter estimates, interpreting results
  • 6. References
• Example 3: Parameter Recovery
  • 1. Simulating “observed data”
  • 2. Parameter estimation
  • 3. Simulation of synthetic data
  • 4. Parameter re-estimation
  • 5. Comparison of generating and recovered estimates
  • 6. Conclusion
  • 7. References

Indices and tables

• Index
• Search Page