def test_compare_nonnested(self):
res = self.res
res2 = self.res2
# jt = jtest(fm, lm(ginv ~ ggdp + tbilrate))
# Estimate Std. Error t value Pr(>|t|)
jtest = [
("M1 + fitted(M2)", 1.591505670785873, 0.7384552861695823, 2.155182176352370, 0.032354572525314450, "*"),
("M2 + fitted(M1)", 1.305687653016899, 0.4808385176653064, 2.715438978051544, 0.007203854534057954, "**"),
]
jt1 = smsdia.compare_j(res2, res)
assert_almost_equal(jt1, jtest[0][3:5], decimal=13)
jt2 = smsdia.compare_j(res, res2)
assert_almost_equal(jt2, jtest[1][3:5], decimal=14)
# Estimate Std. Error z value Pr(>|z|)
coxtest = [
("fitted(M1) ~ M2", -0.782030488930356, 0.599696502782265, -1.304043770977755, 1.922186587840554e-01, " "),
(
"fitted(M2) ~ M1",
-2.248817107408537,
0.392656854330139,
-5.727181590258883,
1.021128495098556e-08,
"***",
),
]
ct1 = smsdia.compare_cox(res, res2)
assert_almost_equal(ct1, coxtest[0][3:5], decimal=13)
ct2 = smsdia.compare_cox(res2, res)
assert_almost_equal(ct2, coxtest[1][3:5], decimal=12)
# TODO should be approx
# Res.Df Df F Pr(>F)
encomptest = [
("M1 vs. ME", 198, -1, 4.644810213266983, 0.032354572525313666, "*"),
("M2 vs. ME", 198, -1, 7.373608843521585, 0.007203854534058054, "**"),
]
# Estimate Std. Error t value
petest = [
("M1 + log(fit(M1))-fit(M2)", -229.281878354594596, 44.5087822087058598, -5.15139, 6.201281252449979e-07),
("M2 + fit(M1)-exp(fit(M2))", 0.000634664704814, 0.0000462387010349, 13.72583, 1.319536115230356e-30),
]
def test_compare_nonnested(self):
res = self.res
res2 = self.res2
#jt = jtest(fm, lm(ginv ~ ggdp + tbilrate))
#Estimate Std. Error t value Pr(>|t|)
jtest = [('M1 + fitted(M2)', 1.591505670785873, 0.7384552861695823,
2.155182176352370, 0.032354572525314450, '*'),
('M2 + fitted(M1)', 1.305687653016899, 0.4808385176653064,
2.715438978051544, 0.007203854534057954, '**')]
jt1 = smsdia.compare_j(res2, res)
assert_almost_equal(jt1, jtest[0][3:5], decimal=13)
jt2 = smsdia.compare_j(res, res2)
assert_almost_equal(jt2, jtest[1][3:5], decimal=14)
#Estimate Std. Error z value Pr(>|z|)
coxtest = [('fitted(M1) ~ M2', -0.782030488930356, 0.599696502782265,
-1.304043770977755, 1.922186587840554e-01, ' '),
('fitted(M2) ~ M1', -2.248817107408537, 0.392656854330139,
-5.727181590258883, 1.021128495098556e-08, '***')]
ct1 = smsdia.compare_cox(res, res2)
assert_almost_equal(ct1, coxtest[0][3:5], decimal=13)
ct2 = smsdia.compare_cox(res2, res)
assert_almost_equal(ct2, coxtest[1][3:5], decimal=12)
#TODO should be approx
# Res.Df Df F Pr(>F)
encomptest = [('M1 vs. ME', 198, -1, 4.644810213266983,
0.032354572525313666, '*'),
('M2 vs. ME', 198, -1, 7.373608843521585,
0.007203854534058054, '**')]
# Estimate Std. Error t value
petest = [('M1 + log(fit(M1))-fit(M2)', -229.281878354594596,
44.5087822087058598, -5.15139, 6.201281252449979e-07),
('M2 + fit(M1)-exp(fit(M2))', 0.000634664704814,
0.0000462387010349, 13.72583, 1.319536115230356e-30)]
