def test_outlier_test():
endog, exog, labels = get_duncan_data()
ndarray_mod = OLS(endog, exog).fit()
rstudent = [
3.1345185839, -2.3970223990, 2.0438046359, -1.9309187757, 1.8870465798,
-1.7604905300, -1.7040324156, 1.6024285876, -1.4332485037,
-1.1044851583, 1.0688582315, 1.0185271840, -0.9024219332,
-0.9023876471, -0.8830953936, 0.8265782334, 0.8089220547, 0.7682770197,
0.7319491074, -0.6665962829, 0.5227352794, -0.5135016547, 0.5083881518,
0.4999224372, -0.4980818221, -0.4759717075, -0.4293565820,
-0.4114056499, -0.3779540862, 0.3556874030, 0.3409200462, 0.3062248646,
0.3038999429, -0.3030815773, -0.1873387893, 0.1738050251, 0.1424246593,
-0.1292266025, 0.1272066463, -0.0798902878, 0.0788467222, 0.0722556991,
0.0505098280, 0.0233215136, 0.0007112055
]
unadj_p = [
0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120,
0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623,
0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561,
0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039,
0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703,
0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263,
0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039,
0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197,
0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989
]
bonf_p = [
0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354,
4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133,
16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245, 20.1026416,
21.0763395, 22.8943818, 27.1787396, 27.4660532, 27.6257642, 27.8911043,
27.9489466, 28.6479487, 30.1460253, 30.7313018, 31.8336506, 32.5754218,
33.0707100, 34.2442399, 34.3233506, 34.3512109, 38.3543568, 38.8293308,
39.9348989, 40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391,
43.1982627, 44.1678127, 44.9746195
]
bonf_p = np.array(bonf_p)
bonf_p[bonf_p > 1] = 1
sorted_labels = [
"minister", "reporter", "contractor", "insurance.agent", "machinist",
"store.clerk", "conductor", "factory.owner", "mail.carrier",
"streetcar.motorman", "carpenter", "coal.miner", "bartender",
"bookkeeper", "soda.clerk", "chemist", "RR.engineer", "professor",
"electrician", "gas.stn.attendant", "auto.repairman", "watchman",
"banker", "machine.operator", "dentist", "waiter", "shoe.shiner",
"welfare.worker", "plumber", "physician", "pilot", "engineer",
"accountant", "lawyer", "undertaker", "barber", "store.manager",
"truck.driver", "cook", "janitor", "policeman", "architect", "teacher",
"taxi.driver", "author"
]
res2 = np.c_[rstudent, unadj_p, bonf_p]
res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True)
np.testing.assert_almost_equal(res.values, res2, 7)
np.testing.assert_equal(res.index.tolist(),
sorted_labels) # pylint: disable-msg=E1103
data = pd.DataFrame(np.column_stack((endog, exog)),
columns='y const var1 var2'.split(),
index=labels)
# check `order` with pandas bug in #3971
res_pd = OLS.from_formula('y ~ const + var1 + var2 - 0', data).fit()
res_outl2 = oi.outlier_test(res_pd, method='b', order=True)
assert_almost_equal(res_outl2.values, res2, 7)
assert_equal(res_outl2.index.tolist(), sorted_labels)
res_outl1 = res_pd.outlier_test(method='b')
res_outl1 = res_outl1.sort_values(['unadj_p'], ascending=True)
assert_almost_equal(res_outl1.values, res2, 7)
assert_equal(res_outl1.index.tolist(), sorted_labels)
assert_array_equal(res_outl2.index, res_outl1.index)
# additional keywords in method
res_outl3 = res_pd.outlier_test(method='b', order=True)
assert_equal(res_outl3.index.tolist(), sorted_labels)
res_outl4 = res_pd.outlier_test(method='b', order=True, cutoff=0.15)
assert_equal(res_outl4.index.tolist(), sorted_labels[:1])
def test_outlier_test():
# results from R with NA -> 1. Just testing interface here because
# outlier_test is just a wrapper
labels = [
"accountant",
"pilot",
"architect",
"author",
"chemist",
"minister",
"professor",
"dentist",
"reporter",
"engineer",
"undertaker",
"lawyer",
"physician",
"welfare.worker",
"teacher",
"conductor",
"contractor",
"factory.owner",
"store.manager",
"banker",
"bookkeeper",
"mail.carrier",
"insurance.agent",
"store.clerk",
"carpenter",
"electrician",
"RR.engineer",
"machinist",
"auto.repairman",
"plumber",
"gas.stn.attendant",
"coal.miner",
"streetcar.motorman",
"taxi.driver",
"truck.driver",
"machine.operator",
"barber",
"bartender",
"shoe.shiner",
"cook",
"soda.clerk",
"watchman",
"janitor",
"policeman",
"waiter",
]
# Duncan's prestige data from car
exog = [
[1.0, 62.0, 86.0],
[1.0, 72.0, 76.0],
[1.0, 75.0, 92.0],
[1.0, 55.0, 90.0],
[1.0, 64.0, 86.0],
[1.0, 21.0, 84.0],
[1.0, 64.0, 93.0],
[1.0, 80.0, 100.0],
[1.0, 67.0, 87.0],
[1.0, 72.0, 86.0],
[1.0, 42.0, 74.0],
[1.0, 76.0, 98.0],
[1.0, 76.0, 97.0],
[1.0, 41.0, 84.0],
[1.0, 48.0, 91.0],
[1.0, 76.0, 34.0],
[1.0, 53.0, 45.0],
[1.0, 60.0, 56.0],
[1.0, 42.0, 44.0],
[1.0, 78.0, 82.0],
[1.0, 29.0, 72.0],
[1.0, 48.0, 55.0],
[1.0, 55.0, 71.0],
[1.0, 29.0, 50.0],
[1.0, 21.0, 23.0],
[1.0, 47.0, 39.0],
[1.0, 81.0, 28.0],
[1.0, 36.0, 32.0],
[1.0, 22.0, 22.0],
[1.0, 44.0, 25.0],
[1.0, 15.0, 29.0],
[1.0, 7.0, 7.0],
[1.0, 42.0, 26.0],
[1.0, 9.0, 19.0],
[1.0, 21.0, 15.0],
[1.0, 21.0, 20.0],
[1.0, 16.0, 26.0],
[1.0, 16.0, 28.0],
[1.0, 9.0, 17.0],
[1.0, 14.0, 22.0],
[1.0, 12.0, 30.0],
[1.0, 17.0, 25.0],
[1.0, 7.0, 20.0],
[1.0, 34.0, 47.0],
[1.0, 8.0, 32.0],
]
endog = [
82.0,
83.0,
90.0,
76.0,
90.0,
87.0,
93.0,
90.0,
52.0,
88.0,
57.0,
89.0,
97.0,
59.0,
73.0,
38.0,
76.0,
81.0,
45.0,
92.0,
39.0,
34.0,
41.0,
16.0,
33.0,
53.0,
67.0,
57.0,
26.0,
29.0,
10.0,
15.0,
19.0,
10.0,
13.0,
24.0,
20.0,
7.0,
3.0,
16.0,
6.0,
11.0,
8.0,
41.0,
10.0,
]
ndarray_mod = OLS(endog, exog).fit()
rstudent = [
3.1345185839,
-2.3970223990,
2.0438046359,
-1.9309187757,
1.8870465798,
-1.7604905300,
-1.7040324156,
1.6024285876,
-1.4332485037,
-1.1044851583,
1.0688582315,
1.0185271840,
-0.9024219332,
-0.9023876471,
-0.8830953936,
0.8265782334,
0.8089220547,
0.7682770197,
0.7319491074,
-0.6665962829,
0.5227352794,
-0.5135016547,
0.5083881518,
0.4999224372,
-0.4980818221,
-0.4759717075,
-0.4293565820,
-0.4114056499,
-0.3779540862,
0.3556874030,
0.3409200462,
0.3062248646,
0.3038999429,
-0.3030815773,
-0.1873387893,
0.1738050251,
0.1424246593,
-0.1292266025,
0.1272066463,
-0.0798902878,
0.0788467222,
0.0722556991,
0.0505098280,
0.0233215136,
0.0007112055,
]
unadj_p = [
0.003177202,
0.021170298,
0.047432955,
0.060427645,
0.066248120,
0.085783008,
0.095943909,
0.116738318,
0.159368890,
0.275822623,
0.291386358,
0.314400295,
0.372104049,
0.372122040,
0.382333561,
0.413260793,
0.423229432,
0.446725370,
0.468363101,
0.508764039,
0.603971990,
0.610356737,
0.613905871,
0.619802317,
0.621087703,
0.636621083,
0.669911674,
0.682917818,
0.707414459,
0.723898263,
0.734904667,
0.760983108,
0.762741124,
0.763360242,
0.852319039,
0.862874018,
0.887442197,
0.897810225,
0.899398691,
0.936713197,
0.937538115,
0.942749758,
0.959961394,
0.981506948,
0.999435989,
]
bonf_p = [
0.1429741,
0.9526634,
2.1344830,
2.7192440,
2.9811654,
3.8602354,
4.3174759,
5.2532243,
7.1716001,
12.4120180,
13.1123861,
14.1480133,
16.7446822,
16.7454918,
17.2050103,
18.5967357,
19.0453245,
20.1026416,
21.0763395,
22.8943818,
27.1787396,
27.4660532,
27.6257642,
27.8911043,
27.9489466,
28.6479487,
30.1460253,
30.7313018,
31.8336506,
32.5754218,
33.0707100,
34.2442399,
34.3233506,
34.3512109,
38.3543568,
38.8293308,
39.9348989,
40.4014601,
40.4729411,
42.1520939,
42.1892152,
42.4237391,
43.1982627,
44.1678127,
44.9746195,
]
bonf_p = np.array(bonf_p)
bonf_p[bonf_p > 1] = 1
sorted_labels = [
"minister",
"reporter",
"contractor",
"insurance.agent",
"machinist",
"store.clerk",
"conductor",
"factory.owner",
"mail.carrier",
"streetcar.motorman",
"carpenter",
"coal.miner",
"bartender",
"bookkeeper",
"soda.clerk",
"chemist",
"RR.engineer",
"professor",
"electrician",
"gas.stn.attendant",
"auto.repairman",
"watchman",
"banker",
"machine.operator",
"dentist",
"waiter",
"shoe.shiner",
"welfare.worker",
"plumber",
"physician",
"pilot",
"engineer",
"accountant",
"lawyer",
"undertaker",
"barber",
"store.manager",
"truck.driver",
"cook",
"janitor",
"policeman",
"architect",
"teacher",
"taxi.driver",
"author",
]
res2 = np.c_[rstudent, unadj_p, bonf_p]
res = oi.outlier_test(ndarray_mod, method="b", labels=labels, order=True)
np.testing.assert_almost_equal(res.values, res2, 7)
np.testing.assert_equal(res.index.tolist(), sorted_labels) # pylint: disable-msg=E1103
def test_outlier_test():
endog, exog, labels = get_duncan_data()
ndarray_mod = OLS(endog, exog).fit()
rstudent = [3.1345185839, -2.3970223990, 2.0438046359, -1.9309187757,
1.8870465798, -1.7604905300, -1.7040324156, 1.6024285876,
-1.4332485037, -1.1044851583, 1.0688582315, 1.0185271840,
-0.9024219332, -0.9023876471, -0.8830953936, 0.8265782334,
0.8089220547, 0.7682770197, 0.7319491074, -0.6665962829,
0.5227352794, -0.5135016547, 0.5083881518, 0.4999224372,
-0.4980818221, -0.4759717075, -0.4293565820, -0.4114056499,
-0.3779540862, 0.3556874030, 0.3409200462, 0.3062248646,
0.3038999429, -0.3030815773, -0.1873387893, 0.1738050251,
0.1424246593, -0.1292266025, 0.1272066463, -0.0798902878,
0.0788467222, 0.0722556991, 0.0505098280, 0.0233215136,
0.0007112055]
unadj_p = [0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120,
0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623,
0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561,
0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039,
0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703,
0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263,
0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039,
0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197,
0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989]
bonf_p = [0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354,
4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133,
16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245,
20.1026416, 21.0763395, 22.8943818, 27.1787396, 27.4660532,
27.6257642, 27.8911043, 27.9489466, 28.6479487, 30.1460253,
30.7313018, 31.8336506, 32.5754218, 33.0707100, 34.2442399,
34.3233506, 34.3512109, 38.3543568, 38.8293308, 39.9348989,
40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391,
43.1982627, 44.1678127, 44.9746195]
bonf_p = np.array(bonf_p)
bonf_p[bonf_p > 1] = 1
sorted_labels = ["minister", "reporter", "contractor", "insurance.agent",
"machinist", "store.clerk", "conductor", "factory.owner",
"mail.carrier", "streetcar.motorman", "carpenter", "coal.miner",
"bartender", "bookkeeper", "soda.clerk", "chemist", "RR.engineer",
"professor", "electrician", "gas.stn.attendant", "auto.repairman",
"watchman", "banker", "machine.operator", "dentist", "waiter",
"shoe.shiner", "welfare.worker", "plumber", "physician", "pilot",
"engineer", "accountant", "lawyer", "undertaker", "barber",
"store.manager", "truck.driver", "cook", "janitor", "policeman",
"architect", "teacher", "taxi.driver", "author"]
res2 = np.c_[rstudent, unadj_p, bonf_p]
res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True)
np.testing.assert_almost_equal(res.values, res2, 7)
np.testing.assert_equal(res.index.tolist(), sorted_labels) # pylint: disable-msg=E1103
data = pd.DataFrame(np.column_stack((endog, exog)),
columns='y const var1 var2'.split(),
index=labels)
# check `order` with pandas bug in #3971
res_pd = OLS.from_formula('y ~ const + var1 + var2 - 0', data).fit()
res_outl2 = oi.outlier_test(res_pd, method='b', order=True)
assert_almost_equal(res_outl2.values, res2, 7)
assert_equal(res_outl2.index.tolist(), sorted_labels)
res_outl1 = res_pd.outlier_test(method='b')
res_outl1 = res_outl1.sort_values(['unadj_p'], ascending=True)
assert_almost_equal(res_outl1.values, res2, 7)
assert_equal(res_outl1.index.tolist(), sorted_labels)
assert_array_equal(res_outl2.index, res_outl1.index)
# additional keywords in method
res_outl3 = res_pd.outlier_test(method='b', order=True)
assert_equal(res_outl3.index.tolist(), sorted_labels)
res_outl4 = res_pd.outlier_test(method='b', order=True, cutoff=0.15)
assert_equal(res_outl4.index.tolist(), sorted_labels[:1])
def test_outlier_test():
# results from R with NA -> 1. Just testing interface here because
# outlier_test is just a wrapper
labels = [
'accountant', 'pilot', 'architect', 'author', 'chemist', 'minister',
'professor', 'dentist', 'reporter', 'engineer', 'undertaker', 'lawyer',
'physician', 'welfare.worker', 'teacher', 'conductor', 'contractor',
'factory.owner', 'store.manager', 'banker', 'bookkeeper',
'mail.carrier', 'insurance.agent', 'store.clerk', 'carpenter',
'electrician', 'RR.engineer', 'machinist', 'auto.repairman', 'plumber',
'gas.stn.attendant', 'coal.miner', 'streetcar.motorman', 'taxi.driver',
'truck.driver', 'machine.operator', 'barber', 'bartender',
'shoe.shiner', 'cook', 'soda.clerk', 'watchman', 'janitor',
'policeman', 'waiter'
]
#Duncan's prestige data from car
exog = [[1.0, 62.0, 86.0], [1.0, 72.0, 76.0], [1.0, 75.0, 92.0],
[1.0, 55.0, 90.0], [1.0, 64.0, 86.0], [1.0, 21.0, 84.0],
[1.0, 64.0, 93.0], [1.0, 80.0, 100.0], [1.0, 67.0, 87.0],
[1.0, 72.0, 86.0], [1.0, 42.0, 74.0], [1.0, 76.0, 98.0],
[1.0, 76.0, 97.0], [1.0, 41.0, 84.0], [1.0, 48.0, 91.0],
[1.0, 76.0, 34.0], [1.0, 53.0, 45.0], [1.0, 60.0, 56.0],
[1.0, 42.0, 44.0], [1.0, 78.0, 82.0], [1.0, 29.0, 72.0],
[1.0, 48.0, 55.0], [1.0, 55.0, 71.0], [1.0, 29.0, 50.0],
[1.0, 21.0, 23.0], [1.0, 47.0, 39.0], [1.0, 81.0, 28.0],
[1.0, 36.0, 32.0], [1.0, 22.0, 22.0], [1.0, 44.0, 25.0],
[1.0, 15.0, 29.0], [1.0, 7.0, 7.0], [1.0, 42.0, 26.0],
[1.0, 9.0, 19.0], [1.0, 21.0, 15.0], [1.0, 21.0, 20.0],
[1.0, 16.0, 26.0], [1.0, 16.0, 28.0], [1.0, 9.0, 17.0],
[1.0, 14.0, 22.0], [1.0, 12.0, 30.0], [1.0, 17.0, 25.0],
[1.0, 7.0, 20.0], [1.0, 34.0, 47.0], [1.0, 8.0, 32.0]]
endog = [
82., 83., 90., 76., 90., 87., 93., 90., 52., 88., 57., 89., 97., 59.,
73., 38., 76., 81., 45., 92., 39., 34., 41., 16., 33., 53., 67., 57.,
26., 29., 10., 15., 19., 10., 13., 24., 20., 7., 3., 16., 6., 11., 8.,
41., 10.
]
ndarray_mod = OLS(endog, exog).fit()
rstudent = [
3.1345185839, -2.3970223990, 2.0438046359, -1.9309187757, 1.8870465798,
-1.7604905300, -1.7040324156, 1.6024285876, -1.4332485037,
-1.1044851583, 1.0688582315, 1.0185271840, -0.9024219332,
-0.9023876471, -0.8830953936, 0.8265782334, 0.8089220547, 0.7682770197,
0.7319491074, -0.6665962829, 0.5227352794, -0.5135016547, 0.5083881518,
0.4999224372, -0.4980818221, -0.4759717075, -0.4293565820,
-0.4114056499, -0.3779540862, 0.3556874030, 0.3409200462, 0.3062248646,
0.3038999429, -0.3030815773, -0.1873387893, 0.1738050251, 0.1424246593,
-0.1292266025, 0.1272066463, -0.0798902878, 0.0788467222, 0.0722556991,
0.0505098280, 0.0233215136, 0.0007112055
]
unadj_p = [
0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120,
0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623,
0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561,
0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039,
0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703,
0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263,
0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039,
0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197,
0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989
]
bonf_p = [
0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354,
4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133,
16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245, 20.1026416,
21.0763395, 22.8943818, 27.1787396, 27.4660532, 27.6257642, 27.8911043,
27.9489466, 28.6479487, 30.1460253, 30.7313018, 31.8336506, 32.5754218,
33.0707100, 34.2442399, 34.3233506, 34.3512109, 38.3543568, 38.8293308,
39.9348989, 40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391,
43.1982627, 44.1678127, 44.9746195
]
bonf_p = np.array(bonf_p)
bonf_p[bonf_p > 1] = 1
sorted_labels = [
"minister", "reporter", "contractor", "insurance.agent", "machinist",
"store.clerk", "conductor", "factory.owner", "mail.carrier",
"streetcar.motorman", "carpenter", "coal.miner", "bartender",
"bookkeeper", "soda.clerk", "chemist", "RR.engineer", "professor",
"electrician", "gas.stn.attendant", "auto.repairman", "watchman",
"banker", "machine.operator", "dentist", "waiter", "shoe.shiner",
"welfare.worker", "plumber", "physician", "pilot", "engineer",
"accountant", "lawyer", "undertaker", "barber", "store.manager",
"truck.driver", "cook", "janitor", "policeman", "architect", "teacher",
"taxi.driver", "author"
]
res2 = np.c_[rstudent, unadj_p, bonf_p]
res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True)
np.testing.assert_almost_equal(res.values, res2, 7)
np.testing.assert_equal(res.index.tolist(),
sorted_labels) # pylint: disable-msg=E1103
