https://github.com/limix/glimix-core
Fast inference for Generalised Linear Mixed Models.
https://github.com/limix/glimix-core
Last synced: 6 months ago
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Fast inference for Generalised Linear Mixed Models.
- Host: GitHub
- URL: https://github.com/limix/glimix-core
- Owner: limix
- License: mit
- Created: 2016-11-10T17:30:56.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-03-01T04:31:49.000Z (about 2 years ago)
- Last Synced: 2024-10-31T00:24:05.935Z (7 months ago)
- Language: Python
- Homepage:
- Size: 17.1 MB
- Stars: 11
- Watchers: 2
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# glimix-core
[](https://glimix-core.readthedocs.io/en/latest/?badge=latest)
Fast inference over mean and covariance parameters for Generalised Linear Mixed
Models.
It implements the mathematical tricks of
[FaST-LMM](https://github.com/MicrosoftGenomics/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
There are two main ways of installing it.
Via [pip](https://pypi.python.org/pypi/pip):
```bash
pip install glimix-core
```
Or via [conda](http://conda.pydata.org/docs/index.html):
```bash
conda install -c conda-forge glimix-core
```
## Running the tests
After installation, you can test it
```bash
python -c "import glimix_core; glimix_core.test()"
```
as long as you have [pytest](https://docs.pytest.org/en/latest/).
## Usage
Here it is a very simple example to get you started:
```python
>>> 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, False)
>>> 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](http://glimix-core.readthedocs.org/) about the library.
## Authors
* [Danilo Horta](https://github.com/horta)
## License
This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/glimix-core/master/LICENSE.md).