def test_ftest_power():
#equivalence ftest, ttest
for alpha in [0.01, 0.05, 0.1, 0.20, 0.50]:
res0 = smp.ttest_power(0.01, 200, alpha)
res1 = smp.ftest_power(0.01, 199, 1, alpha=alpha, ncc=0)
assert_almost_equal(res1, res0, decimal=6)
#example from Gplus documentation F-test ANOVA
#Total sample size:200
#Effect size "f":0.25
#Beta/alpha ratio:1
#Result:
#Alpha:0.1592
#Power (1-beta):0.8408
#Critical F:1.4762
#Lambda: 12.50000
res1 = smp.ftest_anova_power(0.25, 200, 0.1592, k_groups=10)
res0 = 0.8408
assert_almost_equal(res1, res0, decimal=4)
# TODO: no class yet
# examples agains R::pwr
res2 = Holder()
#> rf = pwr.f2.test(u=5, v=199, f2=0.1**2, sig.level=0.01)
#> cat_items(rf, "res2.")
res2.u = 5
res2.v = 199
res2.f2 = 0.01
res2.sig_level = 0.01
res2.power = 0.0494137732920332
res2.method = 'Multiple regression power calculation'
res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u,
alpha=res2.sig_level, ncc=1)
assert_almost_equal(res1, res2.power, decimal=5)
res2 = Holder()
#> rf = pwr.f2.test(u=5, v=199, f2=0.3**2, sig.level=0.01)
#> cat_items(rf, "res2.")
res2.u = 5
res2.v = 199
res2.f2 = 0.09
res2.sig_level = 0.01
res2.power = 0.7967191006290872
res2.method = 'Multiple regression power calculation'
res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u,
alpha=res2.sig_level, ncc=1)
assert_almost_equal(res1, res2.power, decimal=5)
res2 = Holder()
#> rf = pwr.f2.test(u=5, v=19, f2=0.3**2, sig.level=0.1)
#> cat_items(rf, "res2.")
res2.u = 5
res2.v = 19
res2.f2 = 0.09
res2.sig_level = 0.1
res2.power = 0.235454222377575
res2.method = 'Multiple regression power calculation'
res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u,
alpha=res2.sig_level, ncc=1)
assert_almost_equal(res1, res2.power, decimal=5)
def test_ftest_power():
#equivalence ftest, ttest
for alpha in [0.01, 0.05, 0.1, 0.20, 0.50]:
res0 = smp.ttest_power(0.01, 200, alpha)
res1 = smp.ftest_power(0.01, 199, 1, alpha=alpha, ncc=0)
assert_almost_equal(res1, res0, decimal=6)
#example from Gplus documentation F-test ANOVA
#Total sample size:200
#Effect size "f":0.25
#Beta/alpha ratio:1
#Result:
#Alpha:0.1592
#Power (1-beta):0.8408
#Critical F:1.4762
#Lambda: 12.50000
res1 = smp.ftest_anova_power(0.25, 200, 0.1592, k_groups=10)
res0 = 0.8408
assert_almost_equal(res1, res0, decimal=4)
# TODO: no class yet
# examples agains R::pwr
res2 = Holder()
#> rf = pwr.f2.test(u=5, v=199, f2=0.1**2, sig.level=0.01)
#> cat_items(rf, "res2.")
res2.u = 5
res2.v = 199
res2.f2 = 0.01
res2.sig_level = 0.01
res2.power = 0.0494137732920332
res2.method = 'Multiple regression power calculation'
res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u,
alpha=res2.sig_level, ncc=1)
assert_almost_equal(res1, res2.power, decimal=5)
res2 = Holder()
#> rf = pwr.f2.test(u=5, v=199, f2=0.3**2, sig.level=0.01)
#> cat_items(rf, "res2.")
res2.u = 5
res2.v = 199
res2.f2 = 0.09
res2.sig_level = 0.01
res2.power = 0.7967191006290872
res2.method = 'Multiple regression power calculation'
res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u,
alpha=res2.sig_level, ncc=1)
assert_almost_equal(res1, res2.power, decimal=5)
res2 = Holder()
#> rf = pwr.f2.test(u=5, v=19, f2=0.3**2, sig.level=0.1)
#> cat_items(rf, "res2.")
res2.u = 5
res2.v = 19
res2.f2 = 0.09
res2.sig_level = 0.1
res2.power = 0.235454222377575
res2.method = 'Multiple regression power calculation'
res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u,
alpha=res2.sig_level, ncc=1)
assert_almost_equal(res1, res2.power, decimal=5)
def test_ols_noncentrality(self):
k = self.k_groups
res_ols = OLS(self.y, self.ex).fit()
nobs_t = res_ols.model.nobs
# constraint
c_equal = -np.eye(k)[1:]
c_equal[:, 0] = 1
v = np.zeros(c_equal.shape[0])
# noncentrality at estimated parameters
wt = res_ols.wald_test(c_equal, scalar=True)
df_num, df_denom = wt.df_num, wt.df_denom
cov_p = res_ols.cov_params()
nc_wt = wald_test_noncent_generic(res_ols.params,
c_equal,
v,
cov_p,
diff=None,
joint=True)
assert_allclose(nc_wt, wt.statistic * wt.df_num, rtol=1e-13)
nc_wt2 = wald_test_noncent(res_ols.params,
c_equal,
v,
res_ols,
diff=None,
joint=True)
assert_allclose(nc_wt2, nc_wt, rtol=1e-13)
es_ols = nc_wt / nobs_t
es_oneway = smo.effectsize_oneway(res_ols.params,
res_ols.scale,
self.nobs,
use_var="equal")
assert_allclose(es_ols, es_oneway, rtol=1e-13)
alpha = 0.05
pow_ols = smpwr.ftest_power(np.sqrt(es_ols),
df_denom,
df_num,
alpha,
ncc=1)
pow_oneway = smpwr.ftest_anova_power(np.sqrt(es_oneway),
nobs_t,
alpha,
k_groups=k,
df=None)
assert_allclose(pow_ols, pow_oneway, rtol=1e-13)
# noncentrality at other params
params_alt = res_ols.params * 0.75
# compute constraint value so we can get noncentrality from wald_test
v_off = _offset_constraint(c_equal, res_ols.params, params_alt)
wt_off = res_ols.wald_test((c_equal, v + v_off), scalar=True)
nc_wt_off = wald_test_noncent_generic(params_alt,
c_equal,
v,
cov_p,
diff=None,
joint=True)
assert_allclose(nc_wt_off,
wt_off.statistic * wt_off.df_num,
rtol=1e-13)
# check vectorized version, joint=False
nc_wt_vec = wald_test_noncent_generic(params_alt,
c_equal,
v,
cov_p,
diff=None,
joint=False)
for i in range(c_equal.shape[0]):
nc_wt_i = wald_test_noncent_generic(
params_alt,
c_equal[i:i + 1], # noqa
v[i:i + 1],
cov_p,
diff=None, # noqa
joint=False)
assert_allclose(nc_wt_vec[i], nc_wt_i, rtol=1e-13)
