diff_diff.SimulationSampleSizeResults#

class diff_diff.SimulationSampleSizeResults[source]#

Bases: object

Results from simulation-based sample size search.

required_n#

Required number of units to achieve target power.

Type:: int

power_at_n#

Power achieved at the required N.

Type:: float

target_power#

Target power used in the search.

Type:: float

alpha#

Significance level.

Type:: float

effect_size#

Effect size used in the search.

Type:: float

n_simulations_per_step#

Number of simulations per bisection step.

Type:: int

n_steps#

Number of bisection steps performed.

Type:: int

search_path#

Diagnostic trace of {n_units, power} at each step.

Type:: list of dict

estimator_name#

Name of the estimator used.

Type:: str

effective_n_units#

Effective sample size when it differs from required_n (e.g., due to DDD grid rounding). None when no rounding occurred or when the search already snapped to the estimator’s grid.

Type:: int or None

Methods

`__init__`(required_n, power_at_n, ...[, ...])
`summary`()	Generate a formatted summary.
`to_dataframe`()	Convert results to a single-row DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`effective_n_units`
`survey_config`
`required_n`
`power_at_n`
`target_power`
`alpha`
`effect_size`
`n_simulations_per_step`
`n_steps`
`search_path`
`estimator_name`

__init__(required_n, power_at_n, target_power, alpha, effect_size, n_simulations_per_step, n_steps, search_path, estimator_name, effective_n_units=None, survey_config=None)#

Parameters:

required_n (int)
power_at_n (float)
target_power (float)
alpha (float)
effect_size (float)
n_simulations_per_step (int)
n_steps (int)
search_path (List[Dict[str, float]])
estimator_name (str)
effective_n_units (int | None)
survey_config (Any | None)

Return type:

None

classmethod __new__(*args, **kwargs)#