diff_diff.SimulationPowerResults

class diff_diff.SimulationPowerResults[source]

Bases: object

Results from simulation-based power analysis.

power

Estimated power (proportion of simulations rejecting H0).

Type:: float

power_se

Standard error of power estimate.

Type:: float

power_ci

Confidence interval for power estimate.

Type:: Tuple[float, float]

rejection_rate

Proportion of simulations with p-value < alpha.

Type:: float

mean_estimate

Mean treatment effect estimate across simulations.

Type:: float

std_estimate

Standard deviation of estimates across simulations.

Type:: float

mean_se

Mean standard error across simulations.

Type:: float

coverage

Proportion of CIs containing true effect.

Type:: float

n_simulations

Number of simulations performed.

Type:: int

effect_sizes

Effect sizes tested (if multiple).

Type:: List[float]

powers

Power at each effect size (if multiple).

Type:: List[float]

true_effect

True treatment effect used in simulation.

Type:: float

alpha

Significance level.

Type:: float

estimator_name

Name of the estimator used.

Type:: str

__init__(power, power_se, power_ci, rejection_rate, mean_estimate, std_estimate, mean_se, coverage, n_simulations, effect_sizes, powers, true_effect, alpha, estimator_name, simulation_results=None)

Parameters:

power (float)
power_se (float)
power_ci (Tuple[float, float])
rejection_rate (float)
mean_estimate (float)
std_estimate (float)
mean_se (float)
coverage (float)
n_simulations (int)
effect_sizes (List[float])
powers (List[float])
true_effect (float)
alpha (float)
estimator_name (str)
simulation_results (List[Dict[str, Any]] | None)

Return type:

None

Methods

`__init__`(power, power_se, power_ci, ...[, ...])
`power_curve_df`()	Get power curve data as a DataFrame.
`print_summary`()	Print the summary to stdout.
`summary`()	Generate a formatted summary of simulation power results.
`to_dataframe`()	Convert results to a pandas DataFrame.
`to_dict`()	Convert results to a dictionary.

Attributes

`simulation_results`
`power`
`power_se`
`power_ci`
`rejection_rate`
`mean_estimate`
`std_estimate`
`mean_se`
`coverage`
`n_simulations`
`effect_sizes`
`powers`
`true_effect`
`alpha`
`estimator_name`
`bias`
`rmse`

power: float

power_se: float

power_ci: Tuple[float, float]

rejection_rate: float

mean_estimate: float

std_estimate: float

mean_se: float

coverage: float

n_simulations: int

effect_sizes: List[float]

powers: List[float]

true_effect: float

alpha: float

estimator_name: str

bias: float

rmse: float

simulation_results: List[Dict[str, Any]] | None = None

__post_init__()[source]: Compute derived statistics.

__repr__()[source]

Concise string representation.

Return type:: str

summary()[source]

Generate a formatted summary of simulation power results.

Returns:: Formatted summary table.
Return type:: str

print_summary()[source]

Print the summary to stdout.

Return type:: None

to_dict()[source]

Convert results to a dictionary.

Returns:: Dictionary containing simulation power results.
Return type:: Dict[str, Any]

to_dataframe()[source]

Convert results to a pandas DataFrame.

Returns:: DataFrame with simulation power results.
Return type:: pd.DataFrame

power_curve_df()[source]

Get power curve data as a DataFrame.

Returns:: DataFrame with effect_size and power columns.
Return type:: pd.DataFrame

__init__(power, power_se, power_ci, rejection_rate, mean_estimate, std_estimate, mean_se, coverage, n_simulations, effect_sizes, powers, true_effect, alpha, estimator_name, simulation_results=None)

Parameters:

power (float)
power_se (float)
power_ci (Tuple[float, float])
rejection_rate (float)
mean_estimate (float)
std_estimate (float)
mean_se (float)
coverage (float)
n_simulations (int)
effect_sizes (List[float])
powers (List[float])
true_effect (float)
alpha (float)
estimator_name (str)
simulation_results (List[Dict[str, Any]] | None)

Return type:

None