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DiProPerm for high dimensional hypothesis testing.

Project description

DiProPerm

author: Iain Carmichael_

Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.

Overview

This package implements Direction-Projection-Permutation for High Dimensional Hypothesis Tests (DiPoPerm). For details see Wei et al, 2016 (paper link, arxiv link). DiProPerm "rigorously assesses whether a binary linear classifier is detecting statistically significant differences between two high-dimensional distributions."

Wei, S., Lee, C., Wichers, L., & Marron, J. S. (2016). Direction-projection-permutation for high-dimensional hypothesis tests. Journal of Computational and Graphical Statistics, 25(2), 549-569.

Installation

The diproperm package can be installed via pip or github. This package is currently only tested in python 3.6.

::

pip install diproperm

::

git clone https://github.com/idc9/diproperm.git
python setup.py install

Example

.. code:: python

from sklearn.datasets import make_blobs
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline

from diproperm.DiProPerm import DiProPerm

# toy binary class dataset (two isotropic Gaussians)
X, y = make_blobs(n_samples=100, n_features=2, centers=2, cluster_std=2)

# DiProPerm with mean difference classifier, mean difference summary
# statistic, and 1000 permutation samples.
dpp = DiProPerm(B=1000, stat='md', clf='md')
dpp.fit(X, y)

dpp.test_stats_['md']

.. code:: python

{'Z': 11.704865481794599,
 'cutoff_val': 1.2678333596648679,
 'obs': 4.542253375623943,
 'pval': 0.0,
 'rejected': True}

.. code:: python

dpp.hist('md')

.. image:: doc/figures/dpp_hist.png

For more example code see these example notebooks_.

Help and Support

Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.

Documentation ^^^^^^^^^^^^^

The source code is located on github: https://github.com/idc9/diproperm

Testing ^^^^^^^

Testing is done using nose.

Contributing ^^^^^^^^^^^^

We welcome contributions to make this a stronger package: data examples, bug fixes, spelling errors, new features, etc.

.. _Iain Carmichael: https://idc9.github.io/ .. _paper link: https://www.tandfonline.com/doi/abs/10.1080/10618600.2015.1027773 .. _arxiv link: https://arxiv.org/pdf/1304.0796.pdf .. _these example notebooks: https://github.com/idc9/diproperm/tree/master/doc

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