def test_error_gm(self): #2 equations
y_var0 = ['HR80', 'HR90']
x_var0 = [['PS80', 'UE80'], ['PS90', 'UE90']]
bigy0, bigX0, bigyvars0, bigXvars0 = sur_dictxy(
self.db, y_var0, x_var0)
reg = SURerrorGM(bigy0,bigX0,self.w,\
name_bigy=bigyvars0,name_bigX=bigXvars0,spat_diag=False,\
name_w="natqueen",name_ds="natregimes",nonspat_diag=True)
dict_compare(reg.bSUR,{0: np.array([[ 3.9774686 ],[ 0.8902122 ],[ 0.43050364]]),\
1: np.array([[ 2.93679118],
[ 1.11002827],
[ 0.48761542]])},RTOL)
dict_compare(reg.sur_inf,{0: np.array([[ 3.72514769e-01, 1.06773447e+01, 1.29935073e-26],
[ 1.42242969e-01, 6.25839157e+00, 3.88968202e-10],
[ 4.32238809e-02, 9.95985619e+00, 2.28392844e-23]]),\
1: np.array([[ 3.36949019e-01, 8.71583239e+00, 2.88630055e-18],
[ 1.34136264e-01, 8.27537784e+00, 1.28048921e-16],
[ 4.03310502e-02, 1.20903229e+01, 1.18818750e-33]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.lamsur,
np.array([[0.55099267], [0.52364925]]),
RTOL)
np.testing.assert_allclose(
reg.corr, np.array([[1., 0.29038532], [0.29038532, 1.]]), RTOL)
np.testing.assert_allclose(
reg.surchow,
[[5.5135078, 1., 0.01887016], [1.77544155, 1., 0.18271008],
[1.14089432, 1., 0.28546343]],
rtol=RTOL,
atol=ATOL)
def test_3SLS_3eq(self): #Three equations, no endogenous
y_var1 = ['HR60','HR70','HR80']
x_var1 = [['RD60','PS60'],['RD70','PS70','UE70'],['RD80','PS80']]
bigy1,bigX1,bigyvars1,bigXvars1 = sur_dictxy(self.db,y_var1,x_var1)
reg = SURlagIV(bigy1,bigX1,w=self.w,name_bigy=bigyvars1,name_bigX=bigXvars1,\
name_ds="NAT",name_w="nat_queen")
print reg.summary
dict_compare(reg.b2SLS,{0: np.array([[ 2.42754085],[ 1.48928052],[ 0.33812558],\
[ 0.45567848]]), 1: np.array([[ 4.83887747],[ 2.86272903],[ 0.96950417],\
[-0.12928124],[ 0.33328525]]), 2: np.array([[ 6.69407561],[ 3.81449588],\
[ 1.44603996],[ 0.03355501]])},RTOL)
dict_compare(reg.b3SLS,{0: np.array([[ 2.1646724 ],[ 1.31916307],[ 0.3398716 ],
[ 0.51336281]]), 1: np.array([[ 4.87587006],[ 2.68927603],
[ 0.94945336],[-0.145607 ],[ 0.33901794]]), 2: np.array([[ 6.48848271],
[ 3.53936913],[ 1.34731149],[ 0.06309451]])},RTOL)
dict_compare(reg.tsls_inf,{0: np.array([[ 3.51568531e-01, 6.15718476e+00, 7.40494437e-10],\
[ 1.86875349e-01, 7.05905340e+00, 1.67640650e-12],\
[ 9.04557549e-02, 3.75732426e+00, 1.71739894e-04],\
[ 7.48661202e-02, 6.85707782e+00, 7.02833502e-12]]),\
1: np.array([[ 4.72342840e-01, 1.03227352e+01, 5.56158073e-25],\
[ 2.12539934e-01, 1.26530388e+01, 1.07629786e-36],\
[ 1.21325632e-01, 7.82566179e+00, 5.04993280e-15],\
[ 4.61662438e-02, -3.15397123e+00, 1.61064963e-03],\
[ 5.41804741e-02, 6.25719766e+00, 3.91956530e-10]]),\
2: np.array([[ 3.36526688e-001, 1.92807374e+001, 7.79572152e-083],\
[ 1.59012676e-001, 2.22584087e+001, 9.35079396e-110],\
[ 1.08370073e-001, 1.24325052e+001, 1.74091603e-035],\
[ 4.61776859e-002, 1.36634202e+000, 1.71831639e-001]])},RTOL)
reg = SURlagIV(bigy1,bigX1,w=self.w,w_lags=2,name_bigy=bigyvars1,name_bigX=bigXvars1,\
name_ds="NAT",name_w="nat_queen")
dict_compare(reg.b3SLS,{0: np.array([[ 1.77468937],[ 1.14510457],[ 0.30768813],\
[ 0.5989414 ]]), 1: np.array([[ 4.26823484],[ 2.43651351],[ 0.8683601 ],[-0.12672555],\
[ 0.4208373 ]]), 2: np.array([[ 6.02334209],[ 3.38056146],[ 1.30003556],[ 0.12992573]])},RTOL)
dict_compare(reg.tsls_inf,{0: np.array([[ 3.27608281e-01, 5.41710779e+00, 6.05708284e-08],\
[ 1.76245578e-01, 6.49721025e+00, 8.18230736e-11],\
[ 8.95068772e-02, 3.43759205e+00, 5.86911195e-04],\
[ 6.94610221e-02, 8.62269771e+00, 6.53949186e-18]]),\
1: np.array([[ 4.52225005e-01, 9.43829906e+00, 3.78879655e-21],\
[ 2.03807701e-01, 1.19549629e+01, 6.11608551e-33],\
[ 1.19004906e-01, 7.29684281e+00, 2.94598624e-13],\
[ 4.57552474e-02, -2.76963964e+00, 5.61183429e-03],\
[ 5.13101239e-02, 8.20183745e+00, 2.36740266e-16]]),\
2: np.array([[ 3.27580342e-001, 1.83873735e+001, 1.65820984e-075],\
[ 1.55771577e-001, 2.17020429e+001, 1.96247435e-104],\
[ 1.06817752e-001, 1.21705946e+001, 4.45822889e-034],\
[ 4.48871540e-002, 2.89449691e+000, 3.79766647e-003]])},RTOL)
def test_error_vm(self): #Asymptotic variance matrix
y_var0 = ['HR80', 'HR90']
x_var0 = [['PS80', 'UE80'], ['PS90', 'UE90']]
bigy0, bigX0, bigyvars0, bigXvars0 = sur_dictxy(
self.db, y_var0, x_var0)
reg = SURerrorML(bigy0,bigX0,self.w,spat_diag=True,vm=True,\
name_bigy=bigyvars0,name_bigX=bigXvars0,\
name_w="natqueen",name_ds="natregimes")
dict_compare(reg.bSUR,{0: np.array([[ 4.0222855 ],[ 0.88489646],[ 0.42402853]]),\
1: np.array([[ 3.04923009],[ 1.10972634],[ 0.47075682]])},RTOL)
dict_compare(reg.sur_inf,{0: np.array([[ 3.669218e-01, 1.096224e+01, 5.804195e-28],\
[ 1.412908e-01, 6.262946e+00, 3.777726e-10],\
[ 4.267954e-02, 9.935169e+00, 2.926783e-23]]),\
1: np.array([[ 3.31399691e-01, 9.20106497e+00, 3.54419478e-20],\
[ 1.33525912e-01, 8.31094371e+00, 9.49439563e-17],\
[ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.vm, np.array([[ 4.14625293e-04, 2.38494923e-05, -3.48748935e-03,\
-5.55994101e-04, -1.63239040e-04],[ 2.38494923e-05, 4.53642714e-04, -2.00602452e-04,\
-5.46893937e-04, -3.10498019e-03],[ -3.48748935e-03, -2.00602452e-04, 7.09989591e-01,\
2.11105214e-01, 6.39785285e-02],[ -5.55994101e-04, -5.46893937e-04, 2.11105214e-01,\
3.42890248e-01, 1.91931389e-01],[ -1.63239040e-04, -3.10498019e-03, 6.39785285e-02,\
1.91931389e-01, 5.86933821e-01]]),RTOL)
np.testing.assert_allclose(reg.lamsetp,(np.array([[ 0.02036235],\
[ 0.02129889]]), np.array([[ 26.69730489],[ 23.68454458]]), np.array([[ 5.059048e-157],\
[ 5.202838e-124]])),rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.joinlam, (1207.81269, 2, 5.330924e-263),
rtol=RTOL,
atol=ATOL)
np.testing.assert_allclose(
reg.surchow, [(5.1073696860799931, 1, 0.023824413482255974),
(1.9524745281321374, 1, 0.16232044613203933),
(0.79663667463065702, 1, 0.37210085476281407)],
rtol=RTOL,
atol=ATOL)
np.testing.assert_allclose(
reg.likrlambda, (1014.0319285186415, 2, 6.3938800607190098e-221),
rtol=RTOL,
atol=ATOL)
np.testing.assert_allclose(
reg.lrtest, (287.95821154104488, 1, 1.3849971230596533e-64),
rtol=RTOL,
atol=ATOL)
np.testing.assert_allclose(
reg.lamtest, (1.8693306894921564, 1, 0.17155175615429052),
rtol=RTOL,
atol=ATOL)
def test_error_3eq_gm(self): #Three equation example, unequal K
y_var1 = ['HR60', 'HR70', 'HR80']
x_var1 = [['RD60', 'PS60'], ['RD70', 'PS70', 'UE70'], ['RD80', 'PS80']]
bigy1, bigX1, bigyvars1, bigXvars1 = sur_dictxy(
self.db, y_var1, x_var1)
reg = SURerrorGM(bigy1,bigX1,self.w,name_bigy=bigyvars1,name_bigX=bigXvars1,\
name_w="natqueen",name_ds="natregimes")
print reg.summary
dict_compare(
reg.bSUR, {
0:
np.array([[4.46897583], [2.15287009], [0.5979781]]),
1:
np.array([[7.10380031], [3.44965826], [1.10254808],
[-0.15962263]]),
2:
np.array([[6.91299706], [3.70234954], [1.40532701]])
}, RTOL)
dict_compare(reg.sur_inf,{0: np.array([[ 1.44081634e-001, 3.10169709e+001, 3.18308523e-211],
[ 1.25725320e-001, 1.71236000e+001, 9.89616102e-066],
[ 1.11848242e-001, 5.34633439e+000, 8.97533244e-008]]),\
1: np.array([[ 3.08054448e-001, 2.30602101e+001, 1.16187890e-117],
[ 1.54010409e-001, 2.23988643e+001, 4.03738963e-111],
[ 1.37435180e-001, 8.02231335e+000, 1.03772013e-015],
[ 5.51073953e-002, -2.89657361e+000, 3.77262126e-003]]),\
2: np.array([[ 1.60807064e-001, 4.29893867e+001, 0.00000000e+000],
[ 1.27136514e-001, 2.91210559e+001, 1.94342017e-186],
[ 1.21987743e-001, 1.15202312e+001, 1.04330705e-030]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(
reg.lamsur, np.array([[0.40589647], [0.42900222], [0.41682256]]),
RTOL)
np.testing.assert_allclose(
reg.corr,
np.array([[1., 0.22987815,
0.13516187], [0.22987815, 1., 0.2492023],
[0.13516187, 0.2492023, 1.]]), RTOL)
def test_3SLS_3eq_2or(self): # Second order spatial lags, no instrument lags
y_var2 = ['HR60','HR70','HR80']
x_var2 = [['RD60','PS60'],['RD70','PS70','MA70'],['RD80','PS80']]
yend_var2 = [['UE60','DV60'],['UE70','DV70'],['UE80','DV80']]
q_var2 = [['FH60','FP59','GI59'],['FH70','FP69','GI69'],['FH80','FP79','GI79']]
bigy2,bigX2,bigyvars2,bigXvars2 = sur_dictxy(self.db,y_var2,x_var2)
bigyend2,bigyendvars2 = sur_dictZ(self.db,yend_var2)
bigq2,bigqvars2 = sur_dictZ(self.db,q_var2)
reg = SURlagIV(bigy2,bigX2,bigyend2,bigq2,w=self.w,w_lags=2,lag_q=False,\
name_bigy=bigyvars2,name_bigX=bigXvars2,\
name_bigyend=bigyendvars2,name_bigq=bigqvars2,\
name_ds="NAT",name_w="nat_queen")
dict_compare(reg.b3SLS,{0: np.array([[-2.40071969],[ 1.2933015 ],[ 0.53165876],[ 0.04883189],[ 1.6663233 ],\
[ 0.76473297]]), 1: np.array([[ 7.24987963],[ 2.96110365],[ 0.86322179],[-0.17847268],[-1.1332928 ],\
[ 2.69573919],[ 0.48295237]]), 2: np.array([[-7.55692635],[ 3.17561152],[ 0.37487877],[ 0.1816544 ],\
[ 2.45768258],[ 0.27716717]])},RTOL)
dict_compare(reg.tsls_inf,{0: np.array([[ 7.28635609e-01, -3.29481522e+00, 9.84864177e-04],\
[ 2.44756930e-01, 5.28402406e+00, 1.26376643e-07],\
[ 1.26021571e-01, 4.21879172e+00, 2.45615028e-05],\
[ 1.03323393e-01, 4.72612122e-01, 6.36489932e-01],\
[ 3.48694501e-01, 4.77874843e+00, 1.76389726e-06],\
[ 6.10435763e-02, 1.25276568e+01, 5.26966810e-36]]),\
1: np.array([[ 1.76286536e+00, 4.11255436e+00, 3.91305295e-05],\
[ 2.78649343e-01, 1.06266306e+01, 2.24061686e-26],\
[ 1.28607242e-01, 6.71207766e+00, 1.91872523e-11],\
[ 3.21721548e-02, -5.54742685e+00, 2.89904383e-08],\
[ 2.09773378e-01, -5.40246249e+00, 6.57322045e-08],\
[ 3.06806758e-01, 8.78644007e+00, 1.54373978e-18],\
[ 5.88231798e-02, 8.21023915e+00, 2.20748374e-16]]),\
2: np.array([[ 1.10429601e+00, -6.84320712e+00, 7.74395589e-12],\
[ 1.81002635e-01, 1.75445597e+01, 6.54581911e-69],\
[ 1.33983129e-01, 2.79795505e+00, 5.14272697e-03],\
[ 7.56814009e-02, 2.40025154e+00, 1.63838090e-02],\
[ 1.83365858e-01, 1.34031635e+01, 5.79398038e-41],\
[ 4.61324726e-02, 6.00807101e+00, 1.87743612e-09]])},RTOL)
def test_3SLS(self): #2 equations, same K in each
y_var0 = ['HR80','HR90']
x_var0 = [['PS80','UE80'],['PS90','UE90']]
bigy0,bigX0,bigyvars0,bigXvars0 = sur_dictxy(self.db,y_var0,x_var0)
reg = SURlagIV(bigy0,bigX0,w=self.w,name_bigy=bigyvars0,name_bigX=bigXvars0,\
name_ds="NAT",name_w="nat_queen")
dict_compare(reg.b3SLS,{0: np.array([[ 4.79766641],[ 0.66900706],[ 0.45430715],\
[-0.13665465]]), 1: np.array([[ 2.27972563],[ 0.99252289],[ 0.52280565],[ 0.06909469]])},RTOL)
dict_compare(reg.tsls_inf,{0: np.array([[ 4.55824001e+00, 1.05252606e+00, 2.92558259e-01],\
[ 3.54744447e-01, 1.88588453e+00, 5.93105171e-02],\
[ 7.79071951e-02, 5.83138887e+00, 5.49679157e-09],\
[ 6.74318852e-01, -2.02655838e-01, 8.39404043e-01]]),\
1: np.array([[ 3.90351092e-01, 5.84019280e+00, 5.21404469e-09],\
[ 1.21674079e-01, 8.15722547e+00, 3.42808098e-16],\
[ 4.47686969e-02, 1.16779288e+01, 1.65273681e-31],\
[ 7.99640809e-02, 8.64071585e-01, 3.87548567e-01]])},RTOL)
np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.525751],
[ 0.525751, 1. ]]),RTOL)
np.testing.assert_allclose(reg.surchow,[(0.3178787640240518, 1, 0.57288522734425285),\
(1.0261877219299562, 1, 0.31105574708021311),\
(0.76852435750330428, 1, 0.38067394159083323),\
(0.099802260814129934, 1, 0.75206705793155604)],RTOL)
def test_3SLS_3eq_end(self): #Three equations, two endogenous, three instruments
y_var2 = ['HR60','HR70','HR80']
x_var2 = [['RD60','PS60'],['RD70','PS70','MA70'],['RD80','PS80']]
yend_var2 = [['UE60','DV60'],['UE70','DV70'],['UE80','DV80']]
q_var2 = [['FH60','FP59','GI59'],['FH70','FP69','GI69'],['FH80','FP79','GI79']]
bigy2,bigX2,bigyvars2,bigXvars2 = sur_dictxy(self.db,y_var2,x_var2)
bigyend2,bigyendvars2 = sur_dictZ(self.db,yend_var2)
bigq2,bigqvars2 = sur_dictZ(self.db,q_var2)
reg = SURlagIV(bigy2,bigX2,bigyend2,bigq2,w=self.w,name_bigy=bigyvars2,name_bigX=bigXvars2,\
name_bigyend=bigyendvars2,name_bigq=bigqvars2,spat_diag=True,name_ds="NAT",name_w="nat_queen")
dict_compare(reg.b2SLS,{0: np.array([[-2.36265226],[ 1.69785946],[ 0.65777251],[-0.07519173],[ 2.15755822],\
[ 0.69200015]]), 1: np.array([[ 8.13716008],[ 3.28583832],[ 0.90311859],[-0.21702098],[-1.04365606],\
[ 2.8597322 ],[ 0.39935589]]), 2: np.array([[-5.8117312 ],[ 3.49934818],[ 0.56523782],[ 0.09653315],\
[ 2.31166815],[ 0.20602185]])},RTOL)
dict_compare(reg.b3SLS,{0: np.array([[-2.33115839],[ 1.43097732],[ 0.57312948],[ 0.03474891],[ 1.78825098],\
[ 0.7145636 ]]), 1: np.array([[ 8.34932294],[ 3.28396774],[ 0.95119978],[-0.19323687],[-1.1750583 ],\
[ 2.75925141],[ 0.38544424]]), 2: np.array([[-5.2395274 ],[ 3.38941755],[ 0.55897901],[ 0.08212108],\
[ 2.19387428],[ 0.21582944]])},RTOL)
dict_compare(reg.tsls_inf,{0: np.array([[ 7.31246733e-01, -3.18792315e+00, 1.43298614e-03],\
[ 2.07089585e-01, 6.90994348e+00, 4.84846854e-12],\
[ 1.15296751e-01, 4.97090750e+00, 6.66402399e-07],\
[ 8.75272616e-02, 3.97006755e-01, 6.91362479e-01],\
[ 3.10638495e-01, 5.75669472e+00, 8.57768262e-09],\
[ 5.40333500e-02, 1.32244919e+01, 6.33639937e-40]]),\
1: np.array([[ 1.71703190e+00, 4.86264870e+00, 1.15825305e-06],\
[ 2.79253520e-01, 1.17598079e+01, 6.28772226e-32],\
[ 1.27575632e-01, 7.45596763e+00, 8.92106480e-14],\
[ 3.31742265e-02, -5.82490950e+00, 5.71435564e-09],\
[ 2.19785746e-01, -5.34638083e+00, 8.97303096e-08],\
[ 3.29882178e-01, 8.36435430e+00, 6.04450321e-17],\
[ 5.54968909e-02, 6.94533032e+00, 3.77575814e-12]]),\
2: np.array([[ 9.77398092e-01, -5.36068920e+00, 8.29050465e-08],\
[ 1.67632600e-01, 2.02193222e+01, 6.61862485e-91],\
[ 1.24321379e-01, 4.49624202e+00, 6.91650078e-06],\
[ 6.94834624e-02, 1.18187957e+00, 2.37253491e-01],\
[ 1.68013780e-01, 1.30577045e+01, 5.74336064e-39],\
[ 4.16751208e-02, 5.17885587e+00, 2.23250870e-07]])},RTOL)
np.testing.assert_allclose(reg.joinrho,(215.897034, 3, 1.54744730e-46))
def test_error(self): #2 equations
y_var0 = ['HR80', 'HR90']
x_var0 = [['PS80', 'UE80'], ['PS90', 'UE90']]
bigy0, bigX0, bigyvars0, bigXvars0 = sur_dictxy(
self.db, y_var0, x_var0)
reg = SURerrorML(bigy0,bigX0,self.w,\
name_bigy=bigyvars0,name_bigX=bigXvars0,spat_diag=True,\
name_w="natqueen",name_ds="natregimes",nonspat_diag=False)
dict_compare(
reg.bSUR0, {
0: np.array([[5.18423225], [0.67757925], [0.25706498]]),
1: np.array([[3.79731807], [1.02411196], [0.35895674]])
}, RTOL)
dict_compare(reg.bSUR,{0: np.array([[ 4.0222855 ],[ 0.88489646],[ 0.42402853]]),\
1: np.array([[ 3.04923009],[ 1.10972634],[ 0.47075682]])},RTOL)
dict_compare(reg.sur_inf,{0: np.array([[ 3.669218e-01, 1.096224e+01, 5.804195e-28],\
[ 1.412908e-01, 6.262946e+00, 3.777726e-10],\
[ 4.267954e-02, 9.935169e+00, 2.926783e-23]]),\
1: np.array([[ 3.31399691e-01, 9.20106497e+00, 3.54419478e-20],\
[ 1.33525912e-01, 8.31094371e+00, 9.49439563e-17],\
[ 4.00409716e-02, 1.17568780e+01, 6.50970965e-32]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.lamols,
np.array([[0.60205035], [0.56056348]]),
RTOL)
np.testing.assert_allclose(reg.lamsur,
np.array([[0.54361986], [0.50445451]]),
RTOL)
np.testing.assert_allclose(reg.corr,np.array([[ 1. , 0.31763719],\
[ 0.31763719, 1. ]]),RTOL)
np.testing.assert_allclose(reg.surchow,[(5.1073696860799931, 1, 0.023824413482255974),\
(1.9524745281321374, 1, 0.16232044613203933),\
(0.79663667463065702, 1, 0.37210085476281407)],rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(reg.llik, -19860.067987395596)
np.testing.assert_allclose(reg.errllik, -19497.031128906794)
np.testing.assert_allclose(reg.surerrllik, -19353.052023136348)
np.testing.assert_allclose(
reg.likrlambda, (1014.0319285186415, 2, 6.3938800607190098e-221))
def test_error_3eq(self): #Three equation example, unequal K
y_var1 = ['HR60', 'HR70', 'HR80']
x_var1 = [['RD60', 'PS60'], ['RD70', 'PS70', 'UE70'], ['RD80', 'PS80']]
bigy1, bigX1, bigyvars1, bigXvars1 = sur_dictxy(
self.db, y_var1, x_var1)
reg = SURerrorML(bigy1,bigX1,self.w,name_bigy=bigyvars1,name_bigX=bigXvars1,\
name_w="natqueen",name_ds="natregimes")
print reg.summary
dict_compare(reg.bSUR0,{0: np.array([[ 4.50407527],[ 2.39199682],[ 0.52723694]]), 1: np.array([[ 7.44509818],\
[ 3.74968571],[ 1.28811685],[-0.23526451]]), 2: np.array([[ 6.92761614],[ 3.65423052],\
[ 1.38247611]])},RTOL)
dict_compare(reg.bSUR,{0: np.array([[ 4.474891 ],[ 2.19004379],[ 0.59110509]]), 1: np.array([[ 7.15676612],\
[ 3.49581077],[ 1.12846288],[-0.17133968]]), 2: np.array([[ 6.91550936],[ 3.69351192],\
[ 1.40395543]])},RTOL)
dict_compare(reg.sur_inf,{0: np.array([[ 1.345557e-001, 3.325679e+001, 1.628656e-242],\
[ 1.205317e-001, 1.816985e+001, 8.943986e-074],\
[ 1.092657e-001, 5.409796e+000, 6.309653e-008]]),\
1: np.array([[ 2.957692e-001, 2.419713e+001, 2.384951e-129],\
[ 1.482144e-001, 2.358618e+001, 5.343318e-123],\
[ 1.344687e-001, 8.392014e+000, 4.778835e-017],\
[ 5.378335e-002, -3.185738e+000, 1.443854e-003]]),\
2: np.array([[ 1.500528e-001, 4.608718e+001, 0.000000e+000],\
[ 1.236340e-001, 2.987457e+001, 4.210941e-196],\
[ 1.194989e-001, 1.174869e+001, 7.172248e-032]])},rtol=RTOL, atol=ATOL)
np.testing.assert_allclose(
reg.lamols, np.array([[0.4248829], [0.46428101], [0.42823999]]),
RTOL)
np.testing.assert_allclose(
reg.lamsur, np.array([[0.36137603], [0.38321666], [0.37183716]]),
RTOL)
np.testing.assert_allclose(reg.corr,np.array([[ 1., 0.24563253, 0.14986527],\
[ 0.24563253, 1., 0.25945021],[ 0.14986527, 0.25945021, 1. ]]),RTOL)
np.testing.assert_allclose(reg.llik, -28695.767676078722)
np.testing.assert_allclose(reg.errllik, -28593.569427945633)
np.testing.assert_allclose(reg.surerrllik, -28393.703607018397)
np.testing.assert_allclose(
reg.lrtest, (399.7316418544724, 3, 2.5309501580053097e-86))
