statsmodels.stats.power.TTestPower.plot_power

TTestPower.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 x-axis

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 of nobs.

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'

axNone or axis 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.

titlestr

title for the axis. Use an empty string, '', to avoid a title.

plt_kwds{None, dict}

not used yet

kwdsdict

These remaining keyword arguments are used as arguments to the power function. Many power function support alternative as a keyword argument, two-sample test support ratio.

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 has effect_size, nobs and alpha as the first three arguments. If the second argument is nobs1, then the number of observations in the plot are those for the first sample. TODO: fix this for FTestPower and GofChisquarePower

TODO: maybe add line variable, if we want more than nobs and effectsize