def test_betai(self): np.random.seed(12345) for i in range(10): a = np.random.rand() * 5. b = np.random.rand() * 200. assert_equal(stats.betai(a, b, 0.), 0.) assert_equal(stats.betai(a, b, 1.), 1.) assert_equal(stats.mstats.betai(a, b, 0.), 0.) assert_equal(stats.mstats.betai(a, b, 1.), 1.) x = np.random.rand() assert_almost_equal(stats.betai(a, b, x), stats.mstats.betai(a, b, x), decimal=13)
def check_sample_mean(sm, v, n, popmean): # from stats.stats.ttest_1samp(a, popmean): # Calculates the t-obtained for the independent samples T-test on ONE group # of scores a, given a population mean. # # Returns: t-value, two-tailed prob df = n-1 svar = ((n-1)*v) / float(df) # looks redundant t = (sm-popmean) / np.sqrt(svar*(1.0/n)) prob = stats.betai(0.5*df, 0.5, df/(df+t*t)) # return t,prob npt.assert_(prob > 0.01, 'mean fail, t,prob = %f, %f, m, sm=%f,%f' % (t, prob, popmean, sm))
def check_sample_mean(sm, v, n, popmean): # from stats.stats.ttest_1samp(a, popmean): # Calculates the t-obtained for the independent samples T-test on ONE group # of scores a, given a population mean. # # Returns: t-value, two-tailed prob df = n - 1 svar = ((n - 1) * v) / float(df) # looks redundant t = (sm - popmean) / np.sqrt(svar * (1.0 / n)) prob = stats.betai(0.5 * df, 0.5, df / (df + t * t)) # return t,prob npt.assertTrue( prob > 0.01, 'mean fail, t,prob = %f, %f, m, sm=%f,%f' % (t, prob, popmean, sm))