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.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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n1,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1.5}
self.kwds_extra = {'alternative': 'larger'}
self.cls = smp.TTestIndPower
def __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': res2.h, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1}
self.kwds_extra = {'alternative':'smaller'}
self.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 = 0.21707518167191
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 setup_class(cls):
#> 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'
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 __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
self.kwds_extra = {'alternative': 'larger'}
self.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 = 0.006063932667926375
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 __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
def __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1}
self.kwds_extra = {'alternative': 'larger'}
self.cls = smp.TTestIndPower
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 __init__(self):
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'
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 __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power': res2.power}
self.kwds_extra = {'alternative': 'larger'}
self.cls = smp.TTestPower
def __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n1,
'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1.5}
self.kwds_extra = {'alternative': 'two-sided'}
self.cls = smp.TTestIndPower
def __init__(self):
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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs': res2.n,
'alpha': res2.sig_level, 'power': res2.power}
self.kwds_extra = {'alternative': 'larger'}
self.cls = smp.TTestPower
def __init__(self):
# 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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
# keyword for which we don't look for root:
self.kwds_extra = {'ratio': 0, 'alternative':'smaller'}
self.cls = smp.NormalIndPower
def __init__(self):
# 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'
self.res2 = res2
self.kwds = {'effect_size': res2.d, 'nobs1': res2.n,
'alpha': res2.sig_level, 'power':res2.power}
# keyword for which we don't look for root:
self.kwds_extra = {'ratio': 0}
self.cls = smp.NormalIndPower
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.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):
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 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=0.01,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):
#> 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'
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):
#> 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 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=0.1,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 __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 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.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):
# 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 do not look for root:
cls.kwds_extra = {'ratio': 0, 'alternative': 'smaller'}
cls.cls = smp.NormalIndPower
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 do not look for root:
cls.kwds_extra = {'ratio': 0}
cls.cls = smp.NormalIndPower