def test_power_ztost_prop():
power = smprop.power_ztost_prop(0.1, 0.9, 10, p_alt=0.6, alpha=0.05,
discrete=True, dist='binom')[0]
assert_almost_equal(power, 0.8204, decimal=4) # PASS example
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='binom')[0]
res_power = np.array([ 0., 0., 0., 0.0889, 0.2356, 0.4770, 0.5530,
0.6154, 0.7365, 0.7708])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# with critval_continuity correction
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='binom', variance_prop=None,
continuity=2, critval_continuity=1)[0]
res_power = np.array([0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457,
0.6154, 0.6674, 0.7708])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='binom', variance_prop=0.5,
critval_continuity=1)[0]
res_power = np.array([0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457,
0.6154, 0.6674, 0.7112])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
def test_power_ztost_prop():
power = smprop.power_ztost_prop(0.1,
0.9,
10,
p_alt=0.6,
alpha=0.05,
discrete=True,
dist='binom')[0]
assert_almost_equal(power, 0.8204, decimal=4) # PASS example
with warnings.catch_warnings(): # python >= 2.6
warnings.simplefilter("ignore", HypothesisTestWarning)
power = smprop.power_ztost_prop(0.4,
0.6,
np.arange(20, 210, 20),
p_alt=0.5,
alpha=0.05,
discrete=False,
dist='binom')[0]
res_power = np.array([
0., 0., 0., 0.0889, 0.2356, 0.4770, 0.5530, 0.6154, 0.7365, 0.7708
])
# TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# with critval_continuity correction
power = smprop.power_ztost_prop(0.4,
0.6,
np.arange(20, 210, 20),
p_alt=0.5,
alpha=0.05,
discrete=False,
dist='binom',
variance_prop=None,
continuity=2,
critval_continuity=1)[0]
res_power = np.array([
0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457, 0.6154, 0.6674, 0.7708
])
# TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
power = smprop.power_ztost_prop(0.4,
0.6,
np.arange(20, 210, 20),
p_alt=0.5,
alpha=0.05,
discrete=False,
dist='binom',
variance_prop=0.5,
critval_continuity=1)[0]
res_power = np.array([
0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457, 0.6154, 0.6674, 0.7112
])
# TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
def test_power_ztost_prop_norm():
# regression test for normal distribution
# from a rough comparison, the results and variations look reasonable
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='norm', variance_prop=0.5,
continuity=0, critval_continuity=0)[0]
res_power = np.array([0., 0., 0., 0.11450013, 0.27752006, 0.41495922,
0.52944621, 0.62382638, 0.70092914, 0.76341806])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='norm', variance_prop=0.5,
continuity=1, critval_continuity=0)[0]
res_power = np.array([0., 0., 0.02667562, 0.20189793, 0.35099606,
0.47608598, 0.57981118, 0.66496683, 0.73427591,
0.79026127])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=True,
dist='norm', variance_prop=0.5,
continuity=1, critval_continuity=0)[0]
res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313,
0.55312718, 0.61549537, 0.66743625, 0.77066806])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=True,
dist='norm', variance_prop=0.5,
continuity=1, critval_continuity=1)[0]
res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313,
0.44588687, 0.61549537, 0.66743625, 0.71115563])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=True,
dist='norm', variance_prop=None,
continuity=0, critval_continuity=0)[0]
res_power = np.array([0., 0., 0., 0., 0.15851942, 0.41611758,
0.5010377 , 0.5708047 , 0.70328247, 0.74210096])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
def test_power_ztost_prop_norm():
# regression test for normal distribution
# from a rough comparison, the results and variations look reasonable
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='norm', variance_prop=0.5,
continuity=0, critval_continuity=0)[0]
res_power = np.array([0., 0., 0., 0.11450013, 0.27752006, 0.41495922,
0.52944621, 0.62382638, 0.70092914, 0.76341806])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=False,
dist='norm', variance_prop=0.5,
continuity=1, critval_continuity=0)[0]
res_power = np.array([0., 0., 0.02667562, 0.20189793, 0.35099606,
0.47608598, 0.57981118, 0.66496683, 0.73427591,
0.79026127])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=True,
dist='norm', variance_prop=0.5,
continuity=1, critval_continuity=0)[0]
res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313,
0.55312718, 0.61549537, 0.66743625, 0.77066806])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=True,
dist='norm', variance_prop=0.5,
continuity=1, critval_continuity=1)[0]
res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313,
0.44588687, 0.61549537, 0.66743625, 0.71115563])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
# regression test for normal distribution
power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
p_alt=0.5, alpha=0.05, discrete=True,
dist='norm', variance_prop=None,
continuity=0, critval_continuity=0)[0]
res_power = np.array([0., 0., 0., 0., 0.15851942, 0.41611758,
0.5010377 , 0.5708047 , 0.70328247, 0.74210096])
# TODO: I currently don't impose power>=0, i.e np.maximum(power, 0)
assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)
2 brown medium 53 2 brown dark 54 2 brown black 13'''
dta0 = np.array(ss.split()).reshape(-1,4)
dta = np.array(map(tuple, dta0.tolist()), dtype=[('Region', int), ('Eyes', 'S6'), ('Hair', 'S6'), ('Count', int)])
xfair = np.repeat([1,0], [228, 762-228])
# comparing to SAS last output at
# http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm
# confidence interval for tost
ci01 = smw.confint_ztest(xfair, alpha=0.1)
assert_almost_equal(ci01, [0.2719, 0.3265], 4)
res = smw.ztost(xfair, 0.18, 0.38)
assert_almost_equal(res[1][0], 7.1865, 4)
assert_almost_equal(res[2][0], -4.8701, 4)
nn = np.arange(200, 351)
pow_z = sms.power_ztost_prop(0.5, 0.72, nn, 0.6, alpha=0.05)
pow_bin = sms.power_ztost_prop(0.5, 0.72, nn, 0.6, alpha=0.05, dist='binom')
import matplotlib.pyplot as plt
plt.plot(nn, pow_z[0], label='normal')
plt.plot(nn, pow_bin[0], label='binomial')
plt.legend(loc='lower right')
plt.title('Proportion Equivalence Test: Power as function of sample size')
plt.xlabel('Number of Observations')
plt.ylabel('Power')
plt.show()
dta0 = np.array(ss.split()).reshape(-1, 4)
dta = np.array(lmap(tuple, dta0.tolist()),
dtype=[('Region', int), ('Eyes', 'S6'), ('Hair', 'S6'),
('Count', int)])
xfair = np.repeat([1, 0], [228, 762 - 228])
# comparing to SAS last output at
# http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm
# confidence interval for tost
ci01 = smw.confint_ztest(xfair, alpha=0.1)
assert_almost_equal(ci01, [0.2719, 0.3265], 4)
res = smw.ztost(xfair, 0.18, 0.38)
assert_almost_equal(res[1][0], 7.1865, 4)
assert_almost_equal(res[2][0], -4.8701, 4)
nn = np.arange(200, 351)
pow_z = sms.power_ztost_prop(0.5, 0.72, nn, 0.6, alpha=0.05)
pow_bin = sms.power_ztost_prop(0.5, 0.72, nn, 0.6, alpha=0.05, dist='binom')
import matplotlib.pyplot as plt
plt.plot(nn, pow_z[0], label='normal')
plt.plot(nn, pow_bin[0], label='binomial')
plt.legend(loc='lower right')
plt.title('Proportion Equivalence Test: Power as function of sample size')
plt.xlabel('Number of Observations')
plt.ylabel('Power')
plt.show()
