statsmodels.stats.rates.power_poisson_diff_2indep¶
-
statsmodels.stats.rates.power_poisson_diff_2indep(rate1, rate2, nobs1, nobs_ratio=
1
, alpha=0.05
, value=0
, method_var='score'
, alternative='two-sided'
, return_results=True
)[source]¶ Power of ztest for the difference between two independent poisson rates.
- Parameters:¶
- rate1
float
Poisson rate for the first sample, treatment group, under the alternative hypothesis.
- rate2
float
Poisson rate for the second sample, reference group, under the alternative hypothesis.
- nobs1
float
orint
Number of observations in sample 1.
- nobs_ratio
float
Sample size ratio, nobs2 = nobs_ratio * nobs1.
- alpha
float
in
interval
(0,1) Significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true.
- value
float
Difference between rates 1 and 2 under the null hypothesis.
- method_var{“score”, “alt”}
The variance of the test statistic for the null hypothesis given the rates uder the alternative, can be either equal to the rates under the alternative
method_var="alt"
, or estimated under the constrained of the null hypothesis,method_var="score"
.- alternative
str
, ‘two-sided’ (default
), ‘larger’, ‘smaller’ Alternative hypothesis whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either ‘larger’, ‘smaller’.
- return_resultsbool
If true, then a results instance with extra information is returned, otherwise only the computed power is returned.
- rate1
- Returns:¶
- results
results
instance
orfloat
If return_results is False, then only the power is returned. If return_results is True, then a results instance with the information in attributes is returned.
- powerfloat
Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true.
Other attributes in results instance include :
- std_null
standard error of difference under the null hypothesis (without sqrt(nobs1))
- std_alt
standard error of difference under the alternative hypothesis (without sqrt(nobs1))
- results
References
[1]Stucke, Kathrin, and Meinhard Kieser. 2013. “Sample Size Calculations for Noninferiority Trials with Poisson Distributed Count Data.” Biometrical Journal 55 (2): 203–16. https://doi.org/10.1002/bimj.201200142.
[2]PASS manual chapter 436