def test_normal_power_explicit():
# a few initial test cases for NormalIndPower
sigma = 1
d = 0.3
nobs = 80
alpha = 0.05
res1 = smp.normal_power(d, nobs/2., 0.05)
res2 = smp.NormalIndPower().power(d, nobs, 0.05)
res3 = smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None)
res_R = 0.475100870572638
assert_almost_equal(res1, res_R, decimal=13)
assert_almost_equal(res2, res_R, decimal=13)
assert_almost_equal(res3, res_R, decimal=13)
norm_pow = smp.normal_power(-0.01, nobs/2., 0.05)
norm_pow_R = 0.05045832927039234
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided")
assert_almost_equal(norm_pow, norm_pow_R, decimal=11)
norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05,
alternative="larger")
norm_pow_R = 0.056869534873146124
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater")
assert_almost_equal(norm_pow, norm_pow_R, decimal=11)
# Note: negative effect size is same as switching one-sided alternative
# TODO: should I switch to larger/smaller instead of "one-sided" options
norm_pow = smp.NormalIndPower().power(-0.01, nobs, 0.05,
alternative="larger")
norm_pow_R = 0.0438089705093578
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less")
assert_almost_equal(norm_pow, norm_pow_R, decimal=11)
def test_normal_power_explicit():
# a few initial test cases for NormalIndPower
sigma = 1
d = 0.3
nobs = 80
alpha = 0.05
res1 = smp.normal_power(d, nobs/2., 0.05)
res2 = smp.NormalIndPower().power(d, nobs, 0.05)
res3 = smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None)
res_R = 0.475100870572638
assert_almost_equal(res1, res_R, decimal=13)
assert_almost_equal(res2, res_R, decimal=13)
assert_almost_equal(res3, res_R, decimal=13)
norm_pow = smp.normal_power(-0.01, nobs/2., 0.05)
norm_pow_R = 0.05045832927039234
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided")
assert_almost_equal(norm_pow, norm_pow_R, decimal=13)
norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05,
alternative="larger")
norm_pow_R = 0.056869534873146124
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater")
assert_almost_equal(norm_pow, norm_pow_R, decimal=13)
# Note: negative effect size is same as switching one-sided alternative
# TODO: should I switch to larger/smaller instead of "one-sided" options
norm_pow = smp.NormalIndPower().power(-0.01, nobs, 0.05,
alternative="larger")
norm_pow_R = 0.0438089705093578
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less")
assert_almost_equal(norm_pow, norm_pow_R, decimal=13)
Created on Sat Mar 02 14:38:17 2013
Author: Josef Perktold
"""
from __future__ import print_function
import numpy as np
import statsmodels.stats.power as smp
import statsmodels.stats.proportion as smpr
sigma = 1
d = 0.3
nobs = 80
alpha = 0.05
print(smp.normal_power(d, nobs / 2, 0.05))
print(smp.NormalIndPower().power(d, nobs, 0.05))
print(smp.NormalIndPower().solve_power(effect_size=0.3,
nobs1=80,
alpha=0.05,
power=None))
print(0.475100870572638, 'R')
norm_pow = smp.normal_power(-0.01, nobs / 2, 0.05)
norm_pow_R = 0.05045832927039234
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided")
print('norm_pow', norm_pow, norm_pow - norm_pow_R)
norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05, alternative="larger")
norm_pow_R = 0.056869534873146124
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater")
"""
Created on Sat Mar 02 14:38:17 2013
Author: Josef Perktold
"""
from __future__ import print_function
import statsmodels.stats.power as smp
sigma=1
d=0.3
nobs=80
alpha=0.05
print(smp.normal_power(d, nobs/2, 0.05))
print(smp.NormalIndPower().power(d, nobs, 0.05))
print(smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None))
print(0.475100870572638, 'R')
norm_pow = smp.normal_power(-0.01, nobs/2, 0.05)
norm_pow_R = 0.05045832927039234
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided")
print('norm_pow', norm_pow, norm_pow - norm_pow_R)
norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05, alternative="larger")
norm_pow_R = 0.056869534873146124
#value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater")
print('norm_pow', norm_pow, norm_pow - norm_pow_R)
# Note: negative effect size is same as switching one-sided alternative
