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))
