statsmodels.stats.rates.test_poisson¶

statsmodels.stats.rates.test_poisson(count, nobs, value, method=None, alternative='two-sided', dispersion=1)[source]¶

Test for one sample poisson mean or rate

Parameters:¶

count : array_like¶: Observed count, number of events.
nobs : arrat_like¶: Currently this is total exposure time of the count variable. This will likely change.
value : float, array_like¶: This is the value of poisson rate under the null hypothesis.
method : str¶: Method to use for confidence interval. This is required, there is currently no default method. See Notes for available methods.
alternative : {'two-sided', 'smaller', 'larger'}¶: alternative hypothesis, which can be two-sided or either one of the one-sided tests.
dispersion : float¶: Dispersion scale coefficient for Poisson QMLE. Default is that the data follows a Poisson distribution. Dispersion different from 1 correspond to excess-dispersion in Poisson quasi-likelihood (GLM). Dispersion coeffficient different from one is currently only used in wald and score method.

Return type:¶

HolderTuple instance with test statistic, pvalue and other attributes.

Notes

The implementatio of the hypothesis test is mainly based on the references for the confidence interval, see confint_poisson.

Available methods are:

“score” : based on score test, uses variance under null value
“wald” : based on wald test, uses variance base on estimated rate.
“waldccv” : based on wald test with 0.5 count added to variance computation. This does not use continuity correction for the center of the confidence interval.
“exact-c” central confidence interval based on gamma distribution
“midp-c” : based on midp correction of central exact confidence interval. this uses numerical inversion of the test function. not vectorized.
“sqrt” : based on square root transformed counts
“sqrt-a” based on Anscombe square root transformation of counts + 3/8.