neurosynth/analysis/stats.py
def one_way(data, n):
    """ One-way chi-square test of independence.
    Takes a 1D array as input and compares activation at each voxel to
    proportion expected under a uniform distribution throughout the array. Note
    that if you're testing activation with this, make sure that only valid
    voxels (e.g., in-mask gray matter voxels) are included in the array, or
    results won't make any sense!
    """
    term = data.astype('float64')
    no_term = n - term
    t_exp = np.mean(term, 0)
    t_exp = np.array([t_exp, ] * data.shape[0])
    nt_exp = n - t_exp
    t_mss = (term - t_exp) ** 2 / t_exp
    nt_mss = (no_term - nt_exp) ** 2 / nt_exp
    chi2 = t_mss + nt_mss
    return special.chdtrc(1, chi2)
def one_way(data, n):
    """ One-way chi-square test of independence.
    Takes a 1D array as input and compares activation at each voxel to
    proportion expected under a uniform distribution throughout the array. Note
    that if you're testing activation with this, make sure that only valid
    voxels (e.g., in-mask gray matter voxels) are included in the array, or
    results won't make any sense!
    """
    term = data.astype('float64')
    no_term = n - term
    t_exp = np.mean(term, 0)
    t_exp = np.array([t_exp, ] * data.shape[0])
    nt_exp = n - t_exp
    t_mss = (term - t_exp) ** 2 / t_exp
    nt_mss = (no_term - nt_exp) ** 2 / nt_exp
    chi2 = t_mss + nt_mss
    return special.chdtrc(1, chi2)
One-way chi-square test of independence.
    Takes a 1D array as input and compares activation at each voxel to
    proportion expected under a uniform distribution throughout the array. Note
    that if you're testing activation with this, make sure that only valid
    voxels (e.g., in-mask gray matter voxels) are included in the array, or
    results won't make any sense!