def test_binom_test():
#> bt = binom.test(51,235,(1/6),alternative="less")
#> cat_items(bt, "binom_test_less.")
binom_test_less = Holder()
binom_test_less.statistic = 51
binom_test_less.parameter = 235
binom_test_less.p_value = 0.982022657605858
binom_test_less.conf_int = [0, 0.2659460862574313]
binom_test_less.estimate = 0.2170212765957447
binom_test_less.null_value = 1. / 6
binom_test_less.alternative = 'less'
binom_test_less.method = 'Exact binomial test'
binom_test_less.data_name = '51 and 235'
#> bt = binom.test(51,235,(1/6),alternative="greater")
#> cat_items(bt, "binom_test_greater.")
binom_test_greater = Holder()
binom_test_greater.statistic = 51
binom_test_greater.parameter = 235
binom_test_greater.p_value = 0.02654424571169085
binom_test_greater.conf_int = [0.1735252778065201, 1]
binom_test_greater.estimate = 0.2170212765957447
binom_test_greater.null_value = 1. / 6
binom_test_greater.alternative = 'greater'
binom_test_greater.method = 'Exact binomial test'
binom_test_greater.data_name = '51 and 235'
#> bt = binom.test(51,235,(1/6),alternative="t")
#> cat_items(bt, "binom_test_2sided.")
binom_test_2sided = Holder()
binom_test_2sided.statistic = 51
binom_test_2sided.parameter = 235
binom_test_2sided.p_value = 0.0437479701823997
binom_test_2sided.conf_int = [0.1660633298083073, 0.2752683640289254]
binom_test_2sided.estimate = 0.2170212765957447
binom_test_2sided.null_value = 1. / 6
binom_test_2sided.alternative = 'two.sided'
binom_test_2sided.method = 'Exact binomial test'
binom_test_2sided.data_name = '51 and 235'
alltests = [('larger', binom_test_greater), ('smaller', binom_test_less),
('two-sided', binom_test_2sided)]
for alt, res0 in alltests:
# only p-value is returned
res = smprop.binom_test(51, 235, prop=1. / 6, alternative=alt)
#assert_almost_equal(res[0], res0.statistic)
assert_almost_equal(res, res0.p_value, decimal=13)
# R binom_test returns Copper-Pearson confint
ci_2s = smprop.proportion_confint(51, 235, alpha=0.05, method='beta')
ci_low, ci_upp = smprop.proportion_confint(51,
235,
alpha=0.1,
method='beta')
assert_almost_equal(ci_2s, binom_test_2sided.conf_int, decimal=13)
assert_almost_equal(ci_upp, binom_test_less.conf_int[1], decimal=13)
assert_almost_equal(ci_low, binom_test_greater.conf_int[0], decimal=13)
def test_binom_test():
#> bt = binom.test(51,235,(1/6),alternative="less")
#> cat_items(bt, "binom_test_less.")
binom_test_less = Holder()
binom_test_less.statistic = 51
binom_test_less.parameter = 235
binom_test_less.p_value = 0.982022657605858
binom_test_less.conf_int = [0, 0.2659460862574313]
binom_test_less.estimate = 0.2170212765957447
binom_test_less.null_value = 1. / 6
binom_test_less.alternative = 'less'
binom_test_less.method = 'Exact binomial test'
binom_test_less.data_name = '51 and 235'
#> bt = binom.test(51,235,(1/6),alternative="greater")
#> cat_items(bt, "binom_test_greater.")
binom_test_greater = Holder()
binom_test_greater.statistic = 51
binom_test_greater.parameter = 235
binom_test_greater.p_value = 0.02654424571169085
binom_test_greater.conf_int = [0.1735252778065201, 1]
binom_test_greater.estimate = 0.2170212765957447
binom_test_greater.null_value = 1. / 6
binom_test_greater.alternative = 'greater'
binom_test_greater.method = 'Exact binomial test'
binom_test_greater.data_name = '51 and 235'
#> bt = binom.test(51,235,(1/6),alternative="t")
#> cat_items(bt, "binom_test_2sided.")
binom_test_2sided = Holder()
binom_test_2sided.statistic = 51
binom_test_2sided.parameter = 235
binom_test_2sided.p_value = 0.0437479701823997
binom_test_2sided.conf_int = [0.1660633298083073, 0.2752683640289254]
binom_test_2sided.estimate = 0.2170212765957447
binom_test_2sided.null_value = 1. / 6
binom_test_2sided.alternative = 'two.sided'
binom_test_2sided.method = 'Exact binomial test'
binom_test_2sided.data_name = '51 and 235'
alltests = [('larger', binom_test_greater),
('smaller', binom_test_less),
('two-sided', binom_test_2sided)]
for alt, res0 in alltests:
# only p-value is returned
res = smprop.binom_test(51, 235, prop=1. / 6, alternative=alt)
#assert_almost_equal(res[0], res0.statistic)
assert_almost_equal(res, res0.p_value, decimal=13)
# R binom_test returns Copper-Pearson confint
ci_2s = smprop.proportion_confint(51, 235, alpha=0.05, method='beta')
ci_low, ci_upp = smprop.proportion_confint(51, 235, alpha=0.1,
method='beta')
assert_almost_equal(ci_2s, binom_test_2sided.conf_int, decimal=13)
assert_almost_equal(ci_upp, binom_test_less.conf_int[1], decimal=13)
assert_almost_equal(ci_low, binom_test_greater.conf_int[0], decimal=13)
def get_results(cls):
cls.k = 1
# results from R WRS2
cls.res_basic = np.array([
342.705882352941, 92.3342348150314, 380.157894736842,
92.9416968861829, 129679.029239766
])
# results from R PairedData
ytt1 = Holder()
ytt1.statistic = 3.71157981694944
ytt1.parameter = 16
ytt1.p_value = 0.00189544440273015
ytt1.conf_int = np.array([146.966048669017, 538.445716036866])
ytt1.estimate = 342.705882352941
ytt1.null_value = 0
ytt1.alternative = 'two.sided'
ytt1.method = 'One sample Yuen test, trim=0.0526315789473684'
ytt1.data_name = 'x'
cls.ytt1 = ytt1
tost_clinic_all_multi.method = 'step.up' tost_clinic_all_multi.var_equal = '''FALSE''' tost_clinic_all_multi.FWER = 0.05 #t-tests #> tt = t.test(clinic$var1[16:30], clinic$var1[1:15], data=clinic, mu=-0., alternative="two.sided", paired=TRUE) #> cat_items(tt, prefix="ttest_clinic_paired_1.") ttest_clinic_paired_1 = Holder() ttest_clinic_paired_1.statistic = 1.213001548676048 ttest_clinic_paired_1.parameter = 14 ttest_clinic_paired_1.p_value = 0.245199929713149 ttest_clinic_paired_1.conf_int = (-0.1264911434745851, 0.4558244768079186) ttest_clinic_paired_1.estimate = 0.1646666666666667 ttest_clinic_paired_1.null_value = 0 ttest_clinic_paired_1.alternative = 'two.sided' ttest_clinic_paired_1.method = 'Paired t-test' ttest_clinic_paired_1.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> ttless = t.test(clinic$var1[1:15], clinic$var1[16:30],, data=clinic, mu=-0., alternative="less", paired=FALSE) #> cat_items(ttless, prefix="ttest_clinic_paired_1_l.") ttest_clinic_paired_1_l = Holder() ttest_clinic_paired_1_l.statistic = 0.650438363512706 ttest_clinic_paired_1_l.parameter = 26.7484787582315 ttest_clinic_paired_1_l.p_value = 0.739521349864458 ttest_clinic_paired_1_l.conf_int = (-np.inf, 0.596019631405587) ttest_clinic_paired_1_l.estimate = (3.498, 3.333333333333333) ttest_clinic_paired_1_l.null_value = 0 ttest_clinic_paired_1_l.alternative = 'less' ttest_clinic_paired_1_l.method = 'Welch Two Sample t-test' ttest_clinic_paired_1_l.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]'
def setup(self):
self.n_success = np.array([73, 90, 114, 75])
self.nobs = np.array([86, 93, 136, 82])
self.res_ppt_pvals_raw = np.array([
0.00533824886503131, 0.8327574849753566, 0.1880573726722516,
0.002026764254350234, 0.1309487516334318, 0.1076118730631731
])
self.res_ppt_pvals_holm = np.array([
0.02669124432515654, 0.8327574849753566, 0.4304474922526926,
0.0121605855261014, 0.4304474922526926, 0.4304474922526926
])
res_prop_test = Holder()
res_prop_test.statistic = 11.11938768628861
res_prop_test.parameter = 3
res_prop_test.p_value = 0.011097511366581344
res_prop_test.estimate = np.array([
0.848837209302326, 0.967741935483871, 0.838235294117647,
0.9146341463414634
]).reshape(4, 1, order='F')
res_prop_test.null_value = '''NULL'''
res_prop_test.conf_int = '''NULL'''
res_prop_test.alternative = 'two.sided'
res_prop_test.method = '4-sample test for equality of proportions ' + \
'without continuity correction'
res_prop_test.data_name = 'smokers2 out of patients'
self.res_prop_test = res_prop_test
#> pt = prop.test(smokers2, patients, p=rep(c(0.9), 4), correct=FALSE)
#> cat_items(pt, "res_prop_test_val.")
res_prop_test_val = Holder()
res_prop_test_val.statistic = np.array([13.20305530710751
]).reshape(1, 1, order='F')
res_prop_test_val.parameter = np.array([4]).reshape(1, 1, order='F')
res_prop_test_val.p_value = 0.010325090041836
res_prop_test_val.estimate = np.array([
0.848837209302326, 0.967741935483871, 0.838235294117647,
0.9146341463414634
]).reshape(4, 1, order='F')
res_prop_test_val.null_value = np.array([0.9, 0.9, 0.9,
0.9]).reshape(4, 1, order='F')
res_prop_test_val.conf_int = '''NULL'''
res_prop_test_val.alternative = 'two.sided'
res_prop_test_val.method = '4-sample test for given proportions without continuity correction'
res_prop_test_val.data_name = 'smokers2 out of patients, null probabilities rep(c(0.9), 4)'
self.res_prop_test_val = res_prop_test_val
#> pt = prop.test(smokers2[1], patients[1], p=0.9, correct=FALSE)
#> cat_items(pt, "res_prop_test_1.")
res_prop_test_1 = Holder()
res_prop_test_1.statistic = 2.501291989664086
res_prop_test_1.parameter = 1
res_prop_test_1.p_value = 0.113752943640092
res_prop_test_1.estimate = 0.848837209302326
res_prop_test_1.null_value = 0.9
res_prop_test_1.conf_int = np.array(
[0.758364348004061, 0.9094787701686766])
res_prop_test_1.alternative = 'two.sided'
res_prop_test_1.method = '1-sample proportions test without continuity correction'
res_prop_test_1.data_name = 'smokers2[1] out of patients[1], null probability 0.9'
self.res_prop_test_1 = res_prop_test_1
assert_almost_equal(res1a, res1, decimal=12) # test for ztest and z confidence interval against R BSDA z.test # Note: I needed to calculate the pooled standard deviation for R # std = np.std(np.concatenate((x-x.mean(),y-y.mean())), ddof=2) # > zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667) # > cat_items(zt, "ztest.") ztest_ = Holder() ztest_.statistic = 6.55109865675183 ztest_.p_value = 5.711530850508982e-11 ztest_.conf_int = np.array([1.230415246535603, 2.280948389828034]) ztest_.estimate = np.array([7.01818181818182, 5.2625]) ztest_.null_value = 0 ztest_.alternative = 'two.sided' ztest_.method = 'Two-sample z-Test' ztest_.data_name = 'x and y' # > zt = z.test(x, sigma.x=0.57676142668828667, y, # sigma.y=0.57676142668828667, alternative="less") # > cat_items(zt, "ztest_smaller.") ztest_smaller = Holder() ztest_smaller.statistic = 6.55109865675183 ztest_smaller.p_value = 0.999999999971442 ztest_smaller.conf_int = np.array([np.nan, 2.196499421109045]) ztest_smaller.estimate = np.array([7.01818181818182, 5.2625]) ztest_smaller.null_value = 0 ztest_smaller.alternative = 'less' ztest_smaller.method = 'Two-sample z-Test' ztest_smaller.data_name = 'x and y' # > zt = z.test(x, sigma.x=0.57676142668828667, y,
def setup(self):
self.n_success = np.array([ 73, 90, 114, 75])
self.nobs = np.array([ 86, 93, 136, 82])
self.res_ppt_pvals_raw = np.array([
0.00533824886503131, 0.8327574849753566, 0.1880573726722516,
0.002026764254350234, 0.1309487516334318, 0.1076118730631731
])
self.res_ppt_pvals_holm = np.array([
0.02669124432515654, 0.8327574849753566, 0.4304474922526926,
0.0121605855261014, 0.4304474922526926, 0.4304474922526926
])
res_prop_test = Holder()
res_prop_test.statistic = 11.11938768628861
res_prop_test.parameter = 3
res_prop_test.p_value = 0.011097511366581344
res_prop_test.estimate = np.array([
0.848837209302326, 0.967741935483871, 0.838235294117647,
0.9146341463414634
]).reshape(4,1, order='F')
res_prop_test.null_value = '''NULL'''
res_prop_test.conf_int = '''NULL'''
res_prop_test.alternative = 'two.sided'
res_prop_test.method = '4-sample test for equality of proportions ' + \
'without continuity correction'
res_prop_test.data_name = 'smokers2 out of patients'
self.res_prop_test = res_prop_test
#> pt = prop.test(smokers2, patients, p=rep(c(0.9), 4), correct=FALSE)
#> cat_items(pt, "res_prop_test_val.")
res_prop_test_val = Holder()
res_prop_test_val.statistic = np.array([
13.20305530710751
]).reshape(1,1, order='F')
res_prop_test_val.parameter = np.array([
4
]).reshape(1,1, order='F')
res_prop_test_val.p_value = 0.010325090041836
res_prop_test_val.estimate = np.array([
0.848837209302326, 0.967741935483871, 0.838235294117647,
0.9146341463414634
]).reshape(4,1, order='F')
res_prop_test_val.null_value = np.array([
0.9, 0.9, 0.9, 0.9
]).reshape(4,1, order='F')
res_prop_test_val.conf_int = '''NULL'''
res_prop_test_val.alternative = 'two.sided'
res_prop_test_val.method = '4-sample test for given proportions without continuity correction'
res_prop_test_val.data_name = 'smokers2 out of patients, null probabilities rep(c(0.9), 4)'
self.res_prop_test_val = res_prop_test_val
#> pt = prop.test(smokers2[1], patients[1], p=0.9, correct=FALSE)
#> cat_items(pt, "res_prop_test_1.")
res_prop_test_1 = Holder()
res_prop_test_1.statistic = 2.501291989664086
res_prop_test_1.parameter = 1
res_prop_test_1.p_value = 0.113752943640092
res_prop_test_1.estimate = 0.848837209302326
res_prop_test_1.null_value = 0.9
res_prop_test_1.conf_int = np.array([0.758364348004061,
0.9094787701686766])
res_prop_test_1.alternative = 'two.sided'
res_prop_test_1.method = '1-sample proportions test without continuity correction'
res_prop_test_1.data_name = 'smokers2[1] out of patients[1], null probability 0.9'
self.res_prop_test_1 = res_prop_test_1
