Skip to main content
Join the official Python Developers Survey 2018 and win valuable prizes: Start the survey!

Fast inference over mean and covariance parameters for Generalised Linear Mixed Models

Project description

glimix-core

Travis AppVeyor Documentation

Fast inference over mean and covariance parameters for Generalised Linear Mixed Models.

It implements the mathematical tricks of FaST-LMM for the special case of Linear Mixed Models with a linear covariance matrix and provides an interface to perform inference over millions of covariates in seconds. The Generalised Linear Mixed Model inference is implemented via Expectation Propagation and also makes use of several mathematical tricks to handle large data sets with thousands of samples and millions of covariates.

Install

We recommend installing it via conda:

conda install -c conda-forge glimix-core

Alternatively, glimix-core can also be installed using pip:

pip install glimix-core

The second installation option requires from the user to install liknorm beforehand.

Running the tests

After installation, you can test it

python -c "import glimix_core; glimix_core.test()"

as long as you have pytest.

Usage

Here it is a very simple example to get you started:

>>> from numpy import array, ones
>>> from numpy_sugar.linalg import economic_qs_linear
>>> from glimix_core.lmm import LMM
>>>
>>> X = array([[1, 2], [3, -1], [1.1, 0.5], [0.5, -0.4]], float)
>>> QS = economic_qs_linear(X)
>>> X = ones((4, 1))
>>> y = array([-1, 2, 0.3, 0.5])
>>> lmm = LMM(y, X, QS)
>>> lmm.fit(verbose=False)
>>> lmm.lml()
-2.2726234086180557

An extensive documentation of the library can be found at http://glimix-core.readthedocs.org/.

Authors

License

This project is licensed under the MIT License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
glimix-core-2.0.2.tar.gz (48.8 kB) Copy SHA256 hash SHA256 Source None Oct 6, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page