statsmodels.stats.power.FTestPower.solve_power¶

FTestPower.
solve_power
(effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1)[source]¶ solve for any one parameter of the power of a Ftest
 for the one sample Ftest the keywords are:
 effect_size, df_num, df_denom, alpha, power
Exactly one needs to be
None
, all others need numeric values.Parameters: effect_size : float
standardized effect size, mean divided by the standard deviation. effect size has to be positive.
nobs : int or float
sample size, number of observations.
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.
power : float in interval (0,1)
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.
alternative : string, ‘twosided’ (default) or ‘onesided’
extra argument to choose whether the power is calculated for a twosided (default) or one sided test. ‘onesided’ assumes we are in the relevant tail.
Returns: value : float
The value of the parameter that was set to None in the call. The value solves the power equation given the remainding parameters.
Notes
The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses
brentq
with a prior search for bounds. If this fails to find a root,fsolve
is used. Iffsolve
also fails, then, foralpha
,power
andeffect_size
,brentq
with fixed bounds is used. However, there can still be cases where this fails.