statsmodels.stats.power.NormalIndPower.plot_power¶

NormalIndPower.
plot_power
(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds)¶ Plot power with number of observations or effect size on xaxis
 Parameters
 dep_var{‘nobs’, ‘effect_size’, ‘alpha’}
This specifies which variable is used for the horizontal axis. If dep_var=’nobs’ (default), then one curve is created for each value of
effect_size
. If dep_var=’effect_size’ or alpha, then one curve is created for each value ofnobs
. nobs{scalar, array_like}
specifies the values of the number of observations in the plot
 effect_size{scalar, array_like}
specifies the values of the effect_size in the plot
 alpha{
float
, array_like} The significance level (type I error) used in the power calculation. Can only be more than a scalar, if
dep_var='alpha'
 ax
None
oraxis
instance
If ax is None, than a matplotlib figure is created. If ax is a matplotlib axis instance, then it is reused, and the plot elements are created with it.
 title
str
title for the axis. Use an empty string,
''
, to avoid a title. plt_kwds{
None
,dict
} not used yet
 kwds
dict
These remaining keyword arguments are used as arguments to the power function. Many power function support
alternative
as a keyword argument, twosample test supportratio
.
 Returns
Figure
If ax is None, the created figure. Otherwise the figure to which ax is connected.
Notes
This works only for classes where the
power
method haseffect_size
,nobs
andalpha
as the first three arguments. If the second argument isnobs1
, then the number of observations in the plot are those for the first sample. TODO: fix this for FTestPower and GofChisquarePowerTODO: maybe add line variable, if we want more than nobs and effectsize