'Calculate the power of a F-test.
Parameters
effect_size : float
standardized effect size, mean divided by the standard deviation.
effect size has to be positive.
df_num : int or float
numerator degrees of freedom.
df_denom : int or float
denominator degrees of freedom.
alpha : float in interval (0,1)
significance level, e.g. 0.05, is the probability of a type I
error, that is wrong rejections if the Null Hypothesis is true.
ncc : int
degrees of freedom correction for non-centrality parameter.
see Notes
Returns
power : float
Power of the test, e.g. 0.8, is one minus the probability of a
type II error. Power is the probability that the test correctly
rejects the Null Hypothesis if the Alternative Hypothesis is true.
Notes
sample size is given implicitly by df_num
set ncc=0 to match t-test, or f-test in LikelihoodModelResults.
ncc=1 matches the non-centrality parameter in R::pwr::pwr.f2.test
ftest_power with ncc=0 should also be correct for f_test in regression
models, with df_num and d_denom as defined there. (not verified yet)'

def power(self, effect_size, df_num, df_denom, alpha, ncc=1):

    pow_ = ftest_power(effect_size, df_num, df_denom, alpha, ncc=ncc)
    return pow_
