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 F-test
- for the one sample F-test 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
orfloat
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
str
, ‘two-sided’ (default
)or
‘one-sided’ extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. ‘one-sided’ assumes we are in the relevant tail.
- effect_size
- Returns:
- value
float
The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters.
- value
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.