def test_t_score_with_covars_and_normalized_design_nocovar(random_state=0): rng = check_random_state(random_state) ### Normalized data n_samples = 50 # generate data var1 = np.ones((n_samples, 1)) / np.sqrt(n_samples) var2 = rng.randn(n_samples, 1) var2 = var2 / np.sqrt(np.sum(var2 ** 2, 0)) # normalize # compute t-scores with nilearn routine t_val_own = _t_score_with_covars_and_normalized_design(var1, var2) # compute t-scores with linalg or statsmodels t_val_alt = get_tvalue_with_alternative_library(var1, var2) assert_array_almost_equal(t_val_own, t_val_alt)
def test_t_score_with_covars_and_normalized_design_withcovar(random_state=0): """ """ rng = check_random_state(random_state) ### Normalized data n_samples = 50 # generate data var1 = np.ones((n_samples, 1)) / np.sqrt(n_samples) # normalized var2 = rng.randn(n_samples, 1) var2 = var2 / np.sqrt(np.sum(var2 ** 2, 0)) # normalize covars = np.eye(n_samples, 3) # covars is orthogonal covars[3] = -1 # covars is orthogonal to var1 covars = orthonormalize_matrix(covars) # nilearn t-score own_score = _t_score_with_covars_and_normalized_design(var1, var2, covars) # compute t-scores with linalg or statmodels ref_score = get_tvalue_with_alternative_library(var1, var2, covars) assert_array_almost_equal(own_score, ref_score)