How to use the nimare.stats.two_way function in NiMARE

pFgA_prior = pAgF * self.prior / pAgF_prior

        # One-way chi-square test for consistency of activation
        pAgF_chi2_vals = one_way(np.squeeze(n_selected_active_voxels),
                                 n_selected)
        pAgF_p_vals = special.chdtrc(1, pAgF_chi2_vals)
        pAgF_sign = np.sign(n_selected_active_voxels -
                            np.mean(n_selected_active_voxels))
        pAgF_z = p_to_z(pAgF_p_vals, tail='two') * pAgF_sign

        # Two-way chi-square for specificity of activation
        cells = np.squeeze(
            np.array([[n_selected_active_voxels, n_unselected_active_voxels],
                      [n_selected - n_selected_active_voxels,
                       n_unselected - n_unselected_active_voxels]]).T)
        pFgA_chi2_vals = two_way(cells)
        pFgA_p_vals = special.chdtrc(1, pFgA_chi2_vals)
        pFgA_p_vals[pFgA_p_vals < 1e-240] = 1e-240
        pFgA_sign = np.sign(pAgF - pAgU).ravel()
        pFgA_z = p_to_z(pFgA_p_vals, tail='two') * pFgA_sign
        images = {
            'pA': pA,
            'pAgF': pAgF,
            'pFgA': pFgA,
            ('pAgF_given_pF=%0.2f' % self.prior): pAgF_prior,
            ('pFgA_given_pF=%0.2f' % self.prior): pFgA_prior,
            'consistency_z': pAgF_z,
            'specificity_z': pFgA_z,
            'consistency_chi2': pAgF_chi2_vals,
            'specificity_chi2': pFgA_chi2_vals,
            'consistency_p': pAgF_p_vals,
            'specificity_p': pFgA_p_vals,

p_selected_g_term = n_selected_term / n_term_foci  # probForward
    l_selected_g_term = p_selected_g_term / p_selected  # likelihoodForward
    p_selected_g_noterm = n_selected_noterm / n_noterm_foci

    p_term_g_selected = p_selected_g_term * p_term / p_selected  # probReverse
    p_term_g_selected = p_term_g_selected / np.sum(p_term_g_selected)  # Normalize

    # Significance testing
    # Forward inference significance is determined with a binomial distribution
    p_fi = 1 - binom.cdf(k=n_selected_term, n=n_term_foci, p=p_selected)
    sign_fi = np.sign(n_selected_term - np.mean(n_selected_term)).ravel()  # pylint: disable=no-member

    # Two-way chi-square test for specificity of activation
    cells = np.array([[n_selected_term, n_selected_noterm],  # pylint: disable=no-member
                      [n_unselected_term, n_unselected_noterm]]).T
    chi2_ri = two_way(cells)
    p_ri = special.chdtrc(1, chi2_ri)
    sign_ri = np.sign(p_selected_g_term - p_selected_g_noterm).ravel()  # pylint: disable=no-member

    # Ignore rare features
    p_fi[n_selected_term < 5] = 1.
    p_ri[n_selected_term < 5] = 1.

    # Multiple comparisons correction across features. Separately done for FI and RI.
    if correction is not None:
        _, p_corr_fi, _, _ = multipletests(p_fi, alpha=u, method=correction,
                                           returnsorted=False)
        _, p_corr_ri, _, _ = multipletests(p_ri, alpha=u, method=correction,
                                           returnsorted=False)
    else:
        p_corr_fi = p_fi
        p_corr_ri = p_ri

# Conditional probabilities
        # pAgF = n_selected_active_voxels * 1.0 / n_selected
        # pAgU = n_unselected_active_voxels * 1.0 / n_unselected

        # One-way chi-square test for consistency of activation
        pAgF_chi2_vals = one_way(np.squeeze(n_selected_active_voxels),
                                 n_selected)
        iter_pAgF_chi2 = np.max(pAgF_chi2_vals)

        # Two-way chi-square for specificity of activation
        cells = np.squeeze(
            np.array([[n_selected_active_voxels, n_unselected_active_voxels],
                      [n_selected - n_selected_active_voxels,
                       n_unselected - n_unselected_active_voxels]]).T)
        pFgA_chi2_vals = two_way(cells)
        iter_pFgA_chi2 = np.max(pFgA_chi2_vals)
        return iter_pAgF_chi2, iter_pFgA_chi2

# Recompute conditions with empirically derived prior (or inputted one)
    if prior is None:
        # if this is used, p_term_g_selected_prior = p_selected (regardless of term)
        prior = p_term

    # Significance testing
    # One-way chi-square test for consistency of term frequency across terms
    chi2_fi = one_way(n_selected_term, n_term)
    p_fi = special.chdtrc(1, chi2_fi)
    sign_fi = np.sign(n_selected_term - np.mean(n_selected_term)).ravel()  # pylint: disable=no-member

    # Two-way chi-square test for specificity of activation
    cells = np.array([[n_selected_term, n_selected_noterm],  # pylint: disable=no-member
                      [n_unselected_term, n_unselected_noterm]]).T
    chi2_ri = two_way(cells)
    p_ri = special.chdtrc(1, chi2_ri)
    sign_ri = np.sign(p_selected_g_term - p_selected_g_noterm).ravel()  # pylint: disable=no-member

    # Multiple comparisons correction across terms. Separately done for FI and RI.
    if correction is not None:
        _, p_corr_fi, _, _ = multipletests(p_fi, alpha=u, method=correction,
                                           returnsorted=False)
        _, p_corr_ri, _, _ = multipletests(p_ri, alpha=u, method=correction,
                                           returnsorted=False)
    else:
        p_corr_fi = p_fi
        p_corr_ri = p_ri

    # Compute z-values
    z_corr_fi = p_to_z(p_corr_fi, 'two') * sign_fi
    z_corr_ri = p_to_z(p_corr_ri, 'two') * sign_ri