statsmodels.stats.rates.confint_quantile_poisson#

statsmodels.stats.rates.confint_quantile_poisson(count, exposure, prob, exposure_new=1.0, method=None, alpha=0.05, alternative='two-sided')[source]#

Confidence interval for quantile of poisson random variable

Parameters:

countarray_like: Observed count, number of events.
exposurearray_like: Currently this is total exposure time of the count variable.
probfloat in (0, 1): Probability for the quantile, e.g. 0.95 to get the upper 95% quantile. With known mean mu, the quantile would be poisson.ppf(prob, mu).
exposure_newfloat: Exposure of the new or predicted observation.
methodstr: Method to used for confidence interval of the estimate of the poisson rate, used in confint_poisson. This is required, there is currently no default method.
alphafloat in (0, 1): Significance level for the confidence interval of the estimate of the Poisson rate. Nominal coverage of the confidence interval is 1 - alpha.
alternative{“two-sided”, “larger”, “smaller”}: The tolerance interval can be two-sided or one-sided. Alternative “larger” provides the upper bound of the confidence interval, larger counts are outside the interval.

Returns:

tuple (low, upp): Limits of tolerance interval. The confidence interval is a closed interval, that is both low and upp are in the interval.