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.
- exposure
arrat_like
Currently this is total exposure time of the count variable.
- prob
float
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_new
float
Exposure of the new or predicted observation.
- method
str
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.
- alpha
float
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-sider”, “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
)of
limits
of
tolerance
interval.The
confidence
interval
is
a
closed
interval
,that
is
both
low
and
upp
are
in
the
interval.
See also
References
Hahn, Gerald J, and William Q Meeker. 2010. Statistical Intervals: A Guide for Practitioners.