def setup_class(cls):
res2 = Holder()
#> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="one.sample",alternative="greater")
#> cat_items(p, prefix='tt_power1_1g.')
res2.n = 30
res2.d = 1
res2.sig_level = 0.05
res2.power = 0.999892010204909
res2.alternative = 'greater'
res2.note = 'NULL'
res2.method = 'One-sample t test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power': res2.power}
cls.kwds_extra = {'alternative': 'larger'}
cls.cls = smp.TTestPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t2n.test(d=0.1,n1=20, n2=30,sig.level=0.05,alternative="two.sided")
#> cat_items(p, "res2.")
res2.n1 = 20
res2.n2 = 30
res2.d = 0.1
res2.sig_level = 0.05
res2.power = 0.0633081832564667
res2.alternative = 'two.sided'
res2.method = 't test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs1': res2.n1,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1.5}
cls.kwds_extra = {'alternative': 'two-sided'}
cls.cls = smp.TTestIndPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t2n.test(d=0.1,n1=20, n2=30,sig.level=0.05,alternative="greater")
#> cat_items(p, "res2.")
res2.n1 = 20
res2.n2 = 30
res2.d = 0.1
res2.sig_level = 0.05
res2.power = 0.09623589080917805
res2.alternative = 'greater'
res2.method = 't test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs1': res2.n1,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1.5}
cls.kwds_extra = {'alternative': 'larger'}
cls.cls = smp.TTestIndPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="two.sample",alternative="greater")
#> cat_items(p, prefix='tt_power2_1g.')
res2.n = 30
res2.d = 1
res2.sig_level = 0.05
res2.power = 0.985459690251624
res2.alternative = 'greater'
res2.note = 'n is number in *each* group'
res2.method = 'Two-sample t test 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 = {'alternative': 'larger'}
cls.cls = smp.TTestIndPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t.test(d=0.01,n=30,sig.level=0.05,type="two.sample",alternative="greater")
#> cat_items(p, "res2.")
res2.n = 30
res2.d = 0.01
res2.sig_level = 0.05
res2.power = 0.0540740302835667
res2.alternative = 'greater'
res2.note = 'n is number in *each* group'
res2.method = 'Two-sample t test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
cls.kwds_extra = {'alternative': 'larger'}
cls.cls = smp.TTestIndPower
def setup_class(cls):
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'
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.TTestIndPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t.test(d=-0.2,n=20,sig.level=0.05,type="one.sample",alternative="less")
#> cat_items(p, "res2.")
res2.n = 20
res2.d = -0.2
res2.sig_level = 0.05
res2.power = 0.21707518167191
res2.alternative = 'less'
res2.note = '''NULL'''
res2.method = 'One-sample t test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power': res2.power}
cls.kwds_extra = {'alternative': 'smaller'}
cls.cls = smp.TTestPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t.test(d=0.05,n=20,sig.level=0.05,type="one.sample",alternative="greater")
#> cat_items(p, "res2.")
res2.n = 20
res2.d = 0.05
res2.sig_level = 0.05
res2.power = 0.0764888785042198
res2.alternative = 'greater'
res2.note = '''NULL'''
res2.method = 'One-sample t test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power': res2.power}
cls.kwds_extra = {'alternative': 'larger'}
cls.cls = smp.TTestPower
def __init__(self):
#> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="two.sample",alternative="two.sided")
#> cat_items(p, prefix='tt_power2_1.')
res2 = Holder()
res2.n = 30
res2.d = 1
res2.sig_level = 0.05
res2.power = 0.9995636009612725
res2.alternative = 'two.sided'
res2.note = 'NULL'
res2.method = 'One-sample t test power calculation'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
self.kwds_extra = {}
self.cls = smp.TTestPower
def setup_class(cls):
res2 = Holder()
#> np = pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less")
#> cat_items(np, "res2.")
res2.h = 0.01
res2.n = 80
res2.sig_level = 0.05
res2.power = 0.0438089705093578
res2.alternative = 'less'
res2.method = ('Difference of proportion power calculation for' +
' binomial distribution (arcsine transformation)')
res2.note = 'same sample sizes'
cls.res2 = res2
cls.kwds = {'effect_size': res2.h, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1}
cls.kwds_extra = {'alternative':'smaller'}
cls.cls = smp.NormalIndPower
def setup_class(cls):
res2 = Holder()
#> p = pwr.t.test(d=0.2,n=20,sig.level=0.05,type="one.sample",alternative="two.sided")
#> cat_items(p, "res2.")
res2.n = 20
res2.d = 0.2
res2.sig_level = 0.05
res2.power = 0.1359562887679666
res2.alternative = 'two.sided'
res2.note = '''NULL'''
res2.method = 'One-sample t test power calculation'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
cls.kwds_extra = {}
cls.cls = smp.TTestPower
def setup_class(cls):
# forcing one-sample by using ratio=0
res2 = Holder()
#> np = pwr.norm.test(d=0.01,n=40,sig.level=0.05,alternative="less")
#> cat_items(np, "res2.")
res2.d = 0.01
res2.n = 40
res2.sig_level = 0.05
res2.power = 0.0438089705093578
res2.alternative = 'less'
res2.method = 'Mean power calculation for normal distribution with known variance'
cls.res2 = res2
cls.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
# keyword for which we don't look for root:
cls.kwds_extra = {'ratio': 0, 'alternative':'smaller'}
cls.cls = smp.NormalIndPower
def setup_class(cls):
# one example from test_gof, results_power
res2 = Holder()
res2.w = 0.1
res2.N = 5
res2.df = 4
res2.sig_level = 0.05
res2.power = 0.05246644635810126
res2.method = 'Chi squared power calculation'
res2.note = 'N is the number of observations'
cls.res2 = res2
cls.kwds = {'effect_size': res2.w, 'nobs': res2.N,
'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
cls.kwds_extra = {'n_bins': res2.df + 1}
cls.cls = smp.GofChisquarePower
def setup_class(cls):
# forcing one-sample by using ratio=0
#> example from above
# results copied not directly from R
res2 = Holder()
res2.n = 40
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}
# keyword for which we don't look for root:
cls.kwds_extra = {'ratio': 0}
cls.cls = smp.NormalIndPower
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
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
def __init__(self):
res2 = Holder()
#> p = pwr.t.test(d=0.2,n=20,sig.level=0.05,type="one.sample",alternative="less")
#> cat_items(p, "res2.")
res2.n = 20
res2.d = 0.2
res2.sig_level = 0.05
res2.power = 0.006063932667926375
res2.alternative = 'less'
res2.note = '''NULL'''
res2.method = 'One-sample t test power calculation'
self.res2 = res2
self.kwds = {
'effect_size': res2.d,
'nobs': res2.n,
'alpha': res2.sig_level,
'power': res2.power
}
self.kwds_extra = {'alternative': 'smaller'}
self.cls = smp.TTestPower
def __init__(self):
#> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="two.sample",alternative="two.sided")
#> cat_items(p, prefix='tt_power2_1.')
res2 = Holder()
res2.n = 30
res2.d = 1
res2.sig_level = 0.05
res2.power = 0.967708258242517
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
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)