gemseo / uncertainty / statistics / tolerance_interval

lognormal module

Computation of tolerance intervals from a data-fitted log-normal distribution.

class gemseo.uncertainty.statistics.tolerance_interval.lognormal.LogNormalToleranceInterval(size, mean, std, location)

Bases: gemseo.uncertainty.statistics.tolerance_interval.normal.NormalToleranceInterval

Computation of tolerance intervals from a data-fitted log-normal distribution.

The formulae come from the R library tolerance 1.

1

Derek S. Young, tolerance: An R Package for Estimating Tolerance Intervals, Journal of Statistical Software, 36(5), 2010

Parameters

• size (int) – The number of samples.

• mean (float) – The estimation of the mean of the natural logarithm of a log-normal distributed random variable.

• std (float) – The estimation of the standard deviation of the natural logarithm of a log-normal distributed random variable.

• location (float) – The estimation of the location of the log-normal distributed.

Return type

None

compute(coverage, confidence=0.95, side=ToleranceIntervalSide.BOTH)

Compute a tolerance interval.

Parameters

• coverage (float) – A minimum percentage of belonging to the TI.

• confidence (float) –

  A level of confidence in [0,1].

  By default it is set to 0.95.

• side (gemseo.uncertainty.statistics.tolerance_interval.distribution.ToleranceIntervalSide) –

  The type of the tolerance interval characterized by its sides of interest, either a lower-sided tolerance interval \([a, +\infty[\), an upper-sided tolerance interval \(]-\infty, b]\), or a two-sided tolerance interval \([c, d]\).

  By default it is set to BOTH.

Returns

The tolerance bounds.

Return type

tuple[numpy.ndarray, numpy.ndarray]

docstring_processor(parent_doc: Optional[str], child_func: Callable) → None: Callable[[Optional[str], Callable], str] = <docstring_inheritance.processors.google.GoogleDocstringProcessor object>

Parameters

• parent_doc (Optional[str]) –

• child_func (Callable) –

Return type

None