Fast inference over mean and covariance parameters for Generalised Linear Mixed Models
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.
There are two main ways of installing it. Via pip:
pip install glimix-core
Or via conda:
conda install -c conda-forge glimix-core
Running the tests
After installation, you can test it
python -c "import glimix_core; glimix_core.test()"
as long as you have pytest.
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
We also provide an extensive documentation about the library.
This project is licensed under the MIT License.
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