mosaicperm 0.1.5

Exact inference via mosaic permutations

A python implementation of the mosaic permutation testing framework.

Installation

To install mosaicperm, just use pip:

pip install mosaicperm

Documentation

Documentation and tutorials are available at https://mosaicperm.readthedocs.io/.

Quickstart

Below, we give a simple example showing how to use mosaicperm to test whether a set of factor exposures explain the correlations among a matrix of outcomes variables.

	import numpy as np
	import mosaicperm as mp

	# synthetic outcomes and exposures
	n_obs, n_subjects, n_factors = 100, 200, 20
	outcomes = np.random.randn(n_obs, n_subjects)
	exposures = np.random.randn(n_obs, n_subjects, n_factors)
	# example of missing data
	outcomes[0:10][:, 0:5] = np.nan
	exposures[0:10][:, 0:5] = np.nan

	# fit mosaic permutation test
	mpt = mp.factor.MosaicFactorTest(
		outcomes=outcomes,
		exposures=exposures,
		test_stat=mp.statistics.mean_maxcorr_stat,
	)
	print(mpt.fit().summary())

	# produce a time series plot of this analysis
	mpt.fit_tseries(
		nrand=100, n_timepoints=20,
	).plot_tseries()

See the documentation for more details.

Reference

If you use mosaicperm in an academic publication, please consider citing our paper:

@article{mosaic2024,
  author = {Spector, Asher and Barber, Rina Foygel and Hastie, Trevor and Kahn, Ronald N. and Candès, Emmanuel},
  title = {The mosaic permutation test: an exact and nonparametric goodness-of-fit test for factor models},
  date = {2024},
  annotation = {2024f},
  eprint = {2404.15017},
  eprintclass = {stat.ME},
  eprinttype = {arXiv},
  url={https://arxiv.org/abs/2404.15017},
}