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_power_solver_warn():
# messing up the solver to trigger warning
# I wrote this with scipy 0.9,
# convergence behavior of scipy 0.11 is different,
# fails at a different case, but is successful where it failed before
pow_ = 0.69219411243824214 # from previous function
nip = smp.NormalIndPower()
# using nobs, has one backup (fsolve)
nip.start_bqexp['nobs1'] = {'upp': 50, 'low': -20}
val = nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1,
alternative='larger')
assert_almost_equal(val, 1600, decimal=4)
assert_equal(nip.cache_fit_res[0], 1)
assert_equal(len(nip.cache_fit_res), 3)
# case that has convergence failure, and should warn
nip.start_ttp['nobs1'] = np.nan
from statsmodels.tools.sm_exceptions import ConvergenceWarning
assert_warns(ConvergenceWarning, nip.solve_power, 0.1, nobs1=None,
alpha=0.01, power=pow_, ratio=1, alternative='larger')
# this converges with scipy 0.11 ???
# nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1, alternative='larger')
with warnings.catch_warnings(): # python >= 2.6
warnings.simplefilter("ignore")
val = nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1,
alternative='larger')
assert_equal(nip.cache_fit_res[0], 0)
assert_equal(len(nip.cache_fit_res), 3)
def test_power_solver():
# messing up the solver to trigger backup
nip = smp.NormalIndPower()
# check result
es0 = 0.1
pow_ = nip.solve_power(es0,
nobs1=1600,
alpha=0.01,
power=None,
ratio=1,
alternative='larger')
# value is regression test
assert_almost_equal(pow_, 0.69219411243824214, decimal=5)
es = nip.solve_power(None,
nobs1=1600,
alpha=0.01,
power=pow_,
ratio=1,
alternative='larger')
assert_almost_equal(es, es0, decimal=4)
assert_equal(nip.cache_fit_res[0], 1)
assert_equal(len(nip.cache_fit_res), 2)
# cause first optimizer to fail
nip.start_bqexp['effect_size'] = {'upp': -10, 'low': -20}
nip.start_ttp['effect_size'] = 0.14
es = nip.solve_power(None,
nobs1=1600,
alpha=0.01,
power=pow_,
ratio=1,
alternative='larger')
assert_almost_equal(es, es0, decimal=4)
assert_equal(nip.cache_fit_res[0], 1)
assert_equal(len(nip.cache_fit_res), 3, err_msg=repr(nip.cache_fit_res))
nip.start_ttp['effect_size'] = np.nan
es = nip.solve_power(None,
nobs1=1600,
alpha=0.01,
power=pow_,
ratio=1,
alternative='larger')
assert_almost_equal(es, es0, decimal=4)
assert_equal(nip.cache_fit_res[0], 1)
assert_equal(len(nip.cache_fit_res), 4)
# I let this case fail, could be fixed for some statistical tests
# (we shouldn't get here in the first place)
# effect size is negative, but last stage brentq uses [1e-8, 1-1e-8]
assert_raises(ValueError,
nip.solve_power,
None,
nobs1=1600,
alpha=0.01,
power=0.005,
ratio=1,
alternative='larger')
def F_transf(r, n, confval=0.95, power=0.8):
'''
Fisher transformation and the significance of a correlation r
:param iterable r: vectror with all the correlation tested
:param int n: sample size
:param float confval: confidence value
:param float power: power threshold (as beta for type 2 error)
'''
fisher = np.arctanh(r)
confval = 1-((1-self.confval)/2)
z_val = norm.isf(1-confval) / sqrt(self.n-3)
ns = map(lambda x: smp.NormalIndPower().solve_power(
np.arctanh(x), alpha=0.05, ratio=0, power=0.8, alternative='two-sided')
+ 3, fisher)
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")
print('norm_pow', norm_pow, norm_pow - norm_pow_R)
