diff_diff.PreTrendsPowerCurve#

class diff_diff.PreTrendsPowerCurve[source]#

Bases: object

Power curve across violation magnitudes.

M_values#

Grid of violation magnitudes tested.

Type:: np.ndarray

powers#

Power at each violation magnitude.

Type:: np.ndarray

mdv#

Minimum detectable violation.

Type:: float

alpha#

Significance level.

Type:: float

target_power#

Target power level.

Type:: float

violation_type#

Type of violation pattern.

Type:: str

pretest_form#

Pretest acceptance-region form ('nis' or 'wald') used to compute the curve. NIS and Wald curves can differ materially under correlated Σ_22; persisting the form prevents callers from misinterpreting a serialized/plotted curve.

Type:: str

Methods

`__init__`(M_values, powers, mdv, alpha, ...)
`plot`([ax, show_mdv, show_target, color, ...])	Plot the power curve.
`to_dataframe`()	Convert to DataFrame with M, power, and pretest_form columns.

Attributes

`pretest_form`
`M_values`
`powers`
`mdv`
`alpha`
`target_power`
`violation_type`

__init__(M_values, powers, mdv, alpha, target_power, violation_type, pretest_form='wald')#

Parameters:

M_values (ndarray)
powers (ndarray)
mdv (float)
alpha (float)
target_power (float)
violation_type (str)
pretest_form (Literal['nis', 'wald'])

Return type:

None

classmethod __new__(*args, **kwargs)#