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()