本文整理汇总了Python中statsmodels.stats.tests.test_weightstats.Holder.sig_level方法的典型用法代码示例。如果您正苦于以下问题：Python Holder.sig_level方法的具体用法？Python Holder.sig_level怎么用？Python Holder.sig_level使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类statsmodels.stats.tests.test_weightstats.Holder的用法示例。
在下文中一共展示了Holder.sig_level方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setup_class
# 需要导入模块: from statsmodels.stats.tests.test_weightstats import Holder [as 别名]
# 或者: from statsmodels.stats.tests.test_weightstats.Holder import sig_level [as 别名]
def setup_class(cls):
#> example from above
# results copied not directly from R
res2 = Holder()
res2.n = 80
res2.d = 0.3
res2.sig_level = 0.05
res2.power = 0.475100870572638
res2.alternative = 'two.sided'
res2.note = 'NULL'
res2.method = 'two sample power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1}
cls.kwds_extra = {}
cls.cls = smp.NormalIndPower示例2: __init__
# 需要导入模块: from statsmodels.stats.tests.test_weightstats import Holder [as 别名]
# 或者: from statsmodels.stats.tests.test_weightstats.Holder import sig_level [as 别名]
def __init__(self):
res2 = Holder()
#> p = pwr.t.test(d=0.1,n=20,sig.level=0.05,type="two.sample",alternative="two.sided")
#> cat_items(p, "res2.")
res2.n = 20
res2.d = 0.1
res2.sig_level = 0.05
res2.power = 0.06095912465411235
res2.alternative = 'two.sided'
res2.note = 'n is number in *each* group'
res2.method = 'Two-sample t test power calculation'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power': res2.power, 'ratio': 1}
self.kwds_extra = {}
self.cls = smp.TTestIndPower示例3: __init__
# 需要导入模块: from statsmodels.stats.tests.test_weightstats import Holder [as 别名]
# 或者: from statsmodels.stats.tests.test_weightstats.Holder import sig_level [as 别名]
def __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': np.sqrt(res2.f2), 'df_num': res2.v,
'df_denom': res2.u, 'alpha': res2.sig_level,
'power': res2.power}
# keyword for which we don't look for root:
# solving for n_bins doesn't work, will not be used in regular usage
self.kwds_extra = {}
self.cls = smp.FTestPower
# precision for test_power
self.decimal = 5示例4: test_ftest_power
# 需要导入模块: from statsmodels.stats.tests.test_weightstats import Holder [as 别名]
# 或者: from statsmodels.stats.tests.test_weightstats.Holder import sig_level [as 别名]
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)