# statsmodels.stats.rates.confint_poisson_2indep¶

statsmodels.stats.rates.confint_poisson_2indep(count1, exposure1, count2, exposure2, method=`'score'`, compare=`'ratio'`, alpha=`0.05`, method_mover=`'score'`)[source]

Confidence interval for ratio or difference of 2 indep poisson rates.

Parameters:
count1`int`

Number of events in first sample.

exposure1`float`

Total exposure (time * subjects) in first sample.

count2`int`

Number of events in second sample.

exposure2`float`

Total exposure (time * subjects) in second sample.

method`str`

Method for the test statistic and the p-value. Defaults to ‘score’. see Notes.

ratio:

• ‘wald’: NOT YET, method W1A, wald test, variance based on observed rates

• ‘waldcc’ :

• ‘score’: method W2A, score test, variance based on estimate under the Null hypothesis

• ‘wald-log’: W3A, uses log-ratio, variance based on observed rates

• ‘score-log’ W4A, uses log-ratio, variance based on estimate under the Null hypothesis

• ‘sqrt’: W5A, based on variance stabilizing square root transformation

• ‘sqrtcc’ :

• ‘exact-cond’: NOT YET, exact conditional test based on binomial distribution This uses `binom_test` which is minlike in the two-sided case.

• ‘cond-midp’: NOT YET, midpoint-pvalue of exact conditional test

• ‘mover’ :

diff:

• ‘wald’,

• ‘waldccv’

• ‘score’

• ‘mover’

compare{‘diff’, ‘ratio’}

Default is “ratio”. If compare is diff, then the hypothesis test is for diff = rate1 - rate2. If compare is ratio, then the hypothesis test is for the rate ratio defined by ratio = rate1 / rate2.

alternative`str`

The alternative hypothesis, H1, has to be one of the following

• ‘two-sided’: H1: ratio of rates is not equal to ratio_null (default)

• ‘larger’ : H1: ratio of rates is larger than ratio_null

• ‘smaller’ : H1: ratio of rates is smaller than ratio_null

alpha`float` `in` (0, 1)

Significance level, nominal coverage of the confidence interval is 1 - alpha.

Returns:
tuple (low, upp)`confidence` limits.

Last update: Oct 29, 2023