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 – The value of the parameter that was set to None in the call. The value solves the power equation given the remainding parameters.
Return type: 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.