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):
        #> 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()
        #> 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 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)