def __init__(self): res2 = Holder() #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 #converted to res2 by hand res2.f = 0.25 res2.n = 200 res2.k = 10 res2.alpha = 0.1592 res2.power = 0.8408 res2.method = 'Multiple regression power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.f, 'nobs': res2.n, 'alpha': res2.alpha, '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 = {'k_groups': res2.k} # rootfinding doesn't work #self.args_names = ['effect_size','nobs', 'alpha']#, 'k_groups'] self.cls = smp.FTestAnovaPower # precision for test_power self.decimal = 4
def setup_class(cls): res2 = Holder() #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 #converted to res2 by hand res2.f = 0.25 res2.n = 200 res2.k = 10 res2.alpha = 0.1592 res2.power = 0.8408 res2.method = 'Multiple regression power calculation' cls.res2 = res2 cls.kwds = {'effect_size': res2.f, 'nobs': res2.n, 'alpha': res2.alpha, '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 = {'k_groups': res2.k} # rootfinding doesn't work #cls.args_names = ['effect_size','nobs', 'alpha']#, 'k_groups'] cls.cls = smp.FTestAnovaPower # precision for test_power cls.decimal = 4
def setup_class(cls): res2 = Holder() res2.n = 300 res2.prop1 = 0.70 res2.sig_level = 0.05 res2.alternative = 'two-sided' res2.power = 0.6083127 res2.strict = False res2.effect_size = (2*(np.arcsin(np.sqrt(0.78))-np.arcsin(np.sqrt(0.70)))) cls.res2 = res2 cls.kwds = {'nobs1': res2.n, 'alpha': res2.sig_level, 'power': res2.power, 'effect_size': res2.effect_size} cls.kwds_extra = {'alternative': res2.alternative, 'strict': res2.strict, 'prop1': res2.prop1} cls.cls = smp.ProportionTestPower
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=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() #> 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 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 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 __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=0.05,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=0.1,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="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 __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): 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): #> 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): #> 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): #> 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 __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 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 __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): 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 __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 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 __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 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 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): # 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 __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): # 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