statsmodels.stats.power.TTestIndPower.plot_power
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TTestIndPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds)
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plot power with number of observations or effect size on x-axis
Parameters: |
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dep_var (string in ['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 or array_like) – specifies the values of the number of observations in the plot
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effect_size (scalar or array_like) – specifies the values of the effect_size in the plot
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alpha (float or array_like) – The significance level (type I error) used in the power calculation. Can only be more than a scalar, if
dep_var='alpha'
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ax (None 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.
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title (string) – title for the axis. Use an empty string,
'' , to avoid a title. -
plt_kwds (None or dict) – not used yet
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kwds (optional keywords for power function) – 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: |
fig |
Return type: |
matplotlib figure instance |
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