Source code for statsmodels.stats.rates

'''Test for ratio of Poisson intensities in two independent samples

Author: Josef Perktold

'''

import numpy as np
from scipy import stats

from statsmodels.stats.base import HolderTuple
from statsmodels.stats.weightstats import _zstat_generic2

[docs]def test_poisson_2indep(count1, exposure1, count2, exposure2, ratio_null=1, method='score', alternative='two-sided', etest_kwds=None): '''test for ratio of two sample Poisson intensities If the two Poisson rates are g1 and g2, then the Null hypothesis is - H0: g1 / g2 = ratio_null against one of the following alternatives - H1_2-sided: g1 / g2 != ratio_null - H1_larger: g1 / g2 > ratio_null - H1_smaller: g1 / g2 < ratio_null 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. ratio: float ratio of the two Poisson rates under the Null hypothesis. Default is 1. method : string Method for the test statistic and the p-value. Defaults to `'score'`. Current Methods are based on Gu et. al 2008. Implemented are 'wald', 'score' and 'sqrt' based asymptotic normal distribution, and the exact conditional test 'exact-cond', and its mid-point version 'cond-midp'. method='etest' and method='etest-wald' provide pvalues from `etest_poisson_2indep` using score or wald statistic respectively. see Notes. alternative : string 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 etest_kwds: dictionary Additional parameters to be passed to the etest_poisson_2indep function, namely ygrid. Returns ------- results : instance of HolderTuple class The two main attributes are test statistic `statistic` and p-value `pvalue`. Notes ----- - 'wald': method W1A, wald test, variance based on separate estimates - 'score': method W2A, score test, variance based on estimate under Null - 'wald-log': W3A - 'score-log' W4A - 'sqrt': W5A, based on variance stabilizing square root transformation - 'exact-cond': exact conditional test based on binomial distribution - 'cond-midp': midpoint-pvalue of exact conditional test - 'etest': etest with score test statistic - 'etest-wald': etest with wald test statistic References ---------- Gu, Ng, Tang, Schucany 2008: Testing the Ratio of Two Poisson Rates, Biometrical Journal 50 (2008) 2, 2008 See Also -------- tost_poisson_2indep etest_poisson_2indep ''' # shortcut names y1, n1, y2, n2 = count1, exposure1, count2, exposure2 d = n2 / n1 r = ratio_null r_d = r / d if method in ['score']: stat = (y1 - y2 * r_d) / np.sqrt((y1 + y2) * r_d) dist = 'normal' elif method in ['wald']: stat = (y1 - y2 * r_d) / np.sqrt(y1 + y2 * r_d**2) dist = 'normal' elif method in ['sqrt']: stat = 2 * (np.sqrt(y1 + 3 / 8.) - np.sqrt((y2 + 3 / 8.) * r_d)) stat /= np.sqrt(1 + r_d) dist = 'normal' elif method in ['exact-cond', 'cond-midp']: from statsmodels.stats import proportion bp = r_d / (1 + r_d) y_total = y1 + y2 stat = None # TODO: why y2 in here and not y1, check definition of H1 "larger" pvalue = proportion.binom_test(y1, y_total, prop=bp, alternative=alternative) if method in ['cond-midp']: # not inplace in case we still want binom pvalue pvalue = pvalue - 0.5 * stats.binom.pmf(y1, y_total, bp) dist = 'binomial' elif method.startswith('etest'): if method.endswith('wald'): method_etest = 'wald' else: method_etest = 'score' if etest_kwds is None: etest_kwds = {} stat, pvalue = etest_poisson_2indep( count1, exposure1, count2, exposure2, ratio_null=ratio_null, method=method_etest, alternative=alternative, **etest_kwds) dist = 'poisson' else: raise ValueError('method not recognized') if dist == 'normal': stat, pvalue = _zstat_generic2(stat, 1, alternative) rates = (y1 / n1, y2 / n2) ratio = rates / rates res = HolderTuple(statistic=stat, pvalue=pvalue, distribution=dist, method=method, alternative=alternative, rates=rates, ratio=ratio, ratio_null=ratio_null) return res
[docs]def etest_poisson_2indep(count1, exposure1, count2, exposure2, ratio_null=1, method='score', alternative='2-sided', ygrid=None): """E-test for ratio of two sample Poisson rates If the two Poisson rates are g1 and g2, then the Null hypothesis is - H0: g1 / g2 = ratio_null against one of the following alternatives - H1_2-sided: g1 / g2 != ratio_null - H1_larger: g1 / g2 > ratio_null - H1_smaller: g1 / g2 < ratio_null Parameters ---------- count1 : int Number of events in first sample exposure1 : float Total exposure (time * subjects) in first sample count2 : int Number of events in first sample exposure2 : float Total exposure (time * subjects) in first sample ratio : float ratio of the two Poisson rates under the Null hypothesis. Default is 1. method : {"score", "wald"} Method for the test statistic that defines the rejection region. alternative : string 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 ygrid : None or 1-D ndarray Grid values for counts of the Poisson distribution used for computing the pvalue. By default truncation is based on an upper tail Poisson quantiles. Returns ------- stat_sample : float test statistic for the sample pvalue : float References ---------- Gu, Ng, Tang, Schucany 2008: Testing the Ratio of Two Poisson Rates, Biometrical Journal 50 (2008) 2, 2008 """ y1, n1, y2, n2 = count1, exposure1, count2, exposure2 d = n2 / n1 r = ratio_null r_d = r / d eps = 1e-20 # avoid zero division in stat_func if method in ['score']: def stat_func(x1, x2): return (x1 - x2 * r_d) / np.sqrt((x1 + x2) * r_d + eps) # TODO: do I need these? return_results ? # rate2_cmle = (y1 + y2) / n2 / (1 + r_d) # rate1_cmle = rate2_cmle * r # rate1 = rate1_cmle # rate2 = rate2_cmle elif method in ['wald']: def stat_func(x1, x2): return (x1 - x2 * r_d) / np.sqrt(x1 + x2 * r_d**2 + eps) # rate2_mle = y2 / n2 # rate1_mle = y1 / n1 # rate1 = rate1_mle # rate2 = rate2_mle else: raise ValueError('method not recognized') # The sampling distribution needs to be based on the null hypotheis # use constrained MLE from 'score' calculation rate2_cmle = (y1 + y2) / n2 / (1 + r_d) rate1_cmle = rate2_cmle * r rate1 = rate1_cmle rate2 = rate2_cmle mean1 = n1 * rate1 mean2 = n2 * rate2 stat_sample = stat_func(y1, y2) # The following uses a fixed truncation for evaluating the probabilities # It will currently only work for small counts, so that sf at truncation # point is small # We can make it depend on the amount of truncated sf. # Some numerical optimization or checks for large means need to be added. if ygrid is None: threshold = stats.poisson.isf(1e-13, max(mean1, mean2)) threshold = max(threshold, 100) # keep at least 100 y_grid = np.arange(threshold + 1) pdf1 = stats.poisson.pmf(y_grid, mean1) pdf2 = stats.poisson.pmf(y_grid, mean2) stat_space = stat_func(y_grid[:, None], y_grid[None, :]) # broadcasting eps = 1e-15 # correction for strict inequality check if alternative in ['two-sided', '2-sided', '2s']: mask = np.abs(stat_space) >= np.abs(stat_sample) - eps elif alternative in ['larger', 'l']: mask = stat_space >= stat_sample - eps elif alternative in ['smaller', 's']: mask = stat_space <= stat_sample + eps else: raise ValueError('invalid alternative') pvalue = ((pdf1[:, None] * pdf2[None, :])[mask]).sum() return stat_sample, pvalue
[docs]def tost_poisson_2indep(count1, exposure1, count2, exposure2, low, upp, method='score'): '''Equivalence test based on two one-sided `test_proportions_2indep` This assumes that we have two independent binomial samples. The Null and alternative hypothesis for equivalence testing are - H0: g1 / g2 <= low or upp <= g1 / g2 - H1: low < g1 / g2 < upp where g1 and g2 are the 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 low, upp : equivalence margin for the ratio of Poisson rates method: string Method for the test statistic and the p-value. Defaults to `'score'`. Current Methods are based on Gu et. al 2008 Implemented are 'wald', 'score' and 'sqrt' based asymptotic normal distribution, and the exact conditional test 'exact-cond', and its mid-point version 'cond-midp', see Notes Returns ------- results : instance of HolderTuple class The two main attributes are test statistic `statistic` and p-value `pvalue`. Notes ----- - 'wald': method W1A, wald test, variance based on separate estimates - 'score': method W2A, score test, variance based on estimate under Null - 'wald-log': W3A not implemented - 'score-log' W4A not implemented - 'sqrt': W5A, based on variance stabilizing square root transformation - 'exact-cond': exact conditional test based on binomial distribution - 'cond-midp': midpoint-pvalue of exact conditional test The latter two are only verified for one-sided example. References ---------- Gu, Ng, Tang, Schucany 2008: Testing the Ratio of Two Poisson Rates, Biometrical Journal 50 (2008) 2, 2008 See Also -------- test_poisson_2indep ''' tt1 = test_poisson_2indep(count1, exposure1, count2, exposure2, ratio_null=low, method=method, alternative='larger') tt2 = test_poisson_2indep(count1, exposure1, count2, exposure2, ratio_null=upp, method=method, alternative='smaller') idx_max = 0 if tt1.pvalue < tt2.pvalue else 1 res = HolderTuple(statistic=[tt1.statistic, tt2.statistic][idx_max], pvalue=[tt1.pvalue, tt2.pvalue][idx_max], method=method, results_larger=tt1, results_smaller=tt2, title="Equivalence test for 2 independent Poisson rates" ) return res