def test_regi_err(self):
#Artficial:
n = 256
x1 = np.random.uniform(-10, 10, (n, 1))
x2 = np.random.uniform(1, 5, (n, 1))
q = x2 + np.random.normal(0, 1, (n, 1))
x = np.hstack((x1, x2))
y = np.dot(np.hstack((np.ones(
(n, 1)), x)), np.array([[1], [0.5], [2]])) + np.random.normal(
0, 1, (n, 1))
latt = int(np.sqrt(n))
regi = [0] * (n / 2) + [1] * (n / 2)
model = TSLS_Regimes(y,
x1,
regimes=regi,
q=q,
yend=x2,
regime_err_sep=True,
sig2n_k=False)
model1 = TSLS(y[0:(n / 2)].reshape((n / 2), 1),
x1[0:(n / 2)],
yend=x2[0:(n / 2)],
q=q[0:(n / 2)],
sig2n_k=False)
model2 = TSLS(y[(n / 2):n].reshape((n / 2), 1),
x1[(n / 2):n],
yend=x2[(n / 2):n],
q=q[(n / 2):n],
sig2n_k=False)
tbetas = np.vstack((model1.betas, model2.betas))
np.testing.assert_allclose(model.betas, tbetas)
vm = np.hstack((model1.vm.diagonal(), model2.vm.diagonal()))
np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
#Columbus:
reg = TSLS_Regimes(self.y,
self.x,
regimes=self.regimes,
yend=self.yd,
q=self.q,
regime_err_sep=False)
tbetas = np.array([
[80.23408166],
[5.48218125],
[82.98396737],
[0.49775429],
[-3.72663211],
[-1.27451485],
])
np.testing.assert_allclose(tbetas, reg.betas, RTOL)
vm = np.array([495.16048523, 78.89742341, 0., 0., -47.12971066, 0.])
np.testing.assert_allclose(reg.vm[0], vm, RTOL)
u_3 = np.array([[25.57676372], [-17.94922796], [-26.71588759]])
np.testing.assert_allclose(reg.u[0:3], u_3, RTOL)
predy_3 = np.array([[-9.85078372], [36.75098196], [57.34266859]])
np.testing.assert_allclose(reg.predy[0:3], predy_3, RTOL)
chow_regi = np.array([[0.00616179,
0.93743265], [0.3447218, 0.55711631],
[0.37093662, 0.54249417]])
np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
np.testing.assert_allclose(reg.chow.joint[0], 1.1353790779821029, RTOL)
def test_basic(self):
reg = TSLS(self.y, self.X, self.yd, self.q)
betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
np.testing.assert_array_almost_equal(reg.betas, betas, 7)
h_0 = np.array([ 1. , 19.531, 5.03 ])
np.testing.assert_array_almost_equal(reg.h.toarray()[0], h_0)
hth = np.array([[ 49. , 704.371999 , 139.75 ],
[ 704.371999 , 11686.67338121, 2246.12800625],
[ 139.75 , 2246.12800625, 498.5851 ]])
np.testing.assert_array_almost_equal(reg.hth, hth, 7)
hthi = np.array([[ 0.1597275 , -0.00762011, -0.01044191],
[-0.00762011, 0.00100135, -0.0023752 ],
[-0.01044191, -0.0023752 , 0.01563276]])
np.testing.assert_array_almost_equal(reg.hthi, hthi, 7)
self.assertEqual(reg.k, 3)
self.assertEqual(reg.kstar, 1)
self.assertAlmostEqual(reg.mean_y, 35.128823897959187, 7)
self.assertEqual(reg.n, 49)
pfora1a2 = np.array([[ 9.58156106, -0.22744226, -0.13820537],
[ 0.02580142, 0.08226331, -0.03143731],
[-3.13896453, -0.33487872, 0.20690965]])
np.testing.assert_array_almost_equal(reg.pfora1a2, pfora1a2, 7)
predy_5 = np.array([[-28.68949467], [ 28.99484984], [ 55.07344824], [ 38.26609504], [ 57.57145851]])
np.testing.assert_array_almost_equal(reg.predy[0:5], predy_5, 7)
q_5 = np.array([[ 5.03], [ 4.27], [ 3.89], [ 3.7 ], [ 2.83]])
np.testing.assert_array_equal(reg.q[0:5], q_5)
self.assertAlmostEqual(reg.sig2n_k, 587.56797852699822, 7)
self.assertAlmostEqual(reg.sig2n, 551.5944288212637, 7)
self.assertAlmostEqual(reg.sig2, 551.5944288212637, 7)
self.assertAlmostEqual(reg.std_y, 16.732092091229699, 7)
u_5 = np.array([[ 44.41547467], [-10.19309584], [-24.44666724], [ -5.87833504], [ -6.83994851]])
np.testing.assert_array_almost_equal(reg.u[0:5], u_5, 7)
self.assertAlmostEqual(reg.utu, 27028.127012241919, 7)
varb = np.array([[ 0.41526237, 0.01879906, -0.01730372],
[ 0.01879906, 0.00362823, -0.00184604],
[-0.01730372, -0.00184604, 0.0011406 ]])
np.testing.assert_array_almost_equal(reg.varb, varb, 7)
vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
[ 10.36945783, 2.0013142 , -1.01826408],
[ -9.54463414, -1.01826408, 0.62914915]])
np.testing.assert_array_almost_equal(reg.vm, vm, 7)
x_0 = np.array([ 1. , 19.531])
np.testing.assert_array_almost_equal(reg.x.toarray()[0], x_0, 7)
y_5 = np.array([[ 15.72598 ], [ 18.801754], [ 30.626781], [ 32.38776 ], [ 50.73151 ]])
np.testing.assert_array_almost_equal(reg.y[0:5], y_5, 7)
yend_5 = np.array([[ 80.467003], [ 44.567001], [ 26.35 ], [ 33.200001], [ 23.225 ]])
np.testing.assert_array_almost_equal(reg.yend[0:5], yend_5, 7)
z_0 = np.array([ 1. , 19.531 , 80.467003])
np.testing.assert_array_almost_equal(reg.z.toarray()[0], z_0, 7)
zthhthi = np.array([[ 1.00000000e+00, -1.66533454e-16, 4.44089210e-16],
[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
[ 1.26978671e+01, 1.05598709e+00, 3.70212359e+00]])
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
self.assertAlmostEqual(reg.pr2, 0.27936137128173893, 7)
z_stat = np.array([[ 5.84526447e+00, 5.05764078e-09],
[ 3.67601567e-01, 7.13170346e-01],
[ -1.99468913e+00, 4.60767956e-02]])
np.testing.assert_array_almost_equal(reg.z_stat, z_stat, 7)
title = 'TWO STAGE LEAST SQUARES'
self.assertEqual(reg.title, title)
def test_names(self):
w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
name_x = ['inc']
name_y = 'crime'
name_yend = ['hoval']
name_q = ['discbd']
name_w = 'queen'
name_gwk = 'k=5'
name_ds = 'columbus'
reg = TSLS(self.y, self.X, self.yd, self.q,
spat_diag=True, w=w, robust='hac', gwk=gwk,
name_x=name_x, name_y=name_y, name_q=name_q, name_w=name_w,
name_yend=name_yend, name_gwk=name_gwk, name_ds=name_ds)
betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
np.testing.assert_array_almost_equal(reg.betas, betas, 7)
vm = np.array([[ 225.0795089 , 17.11660041, -12.22448566],
[ 17.67097154, 2.47483461, -1.4183641 ],
[ -12.45093722, -1.40495464, 0.8700441 ]])
np.testing.assert_array_almost_equal(reg.vm, vm, 7)
self.assertListEqual(reg.name_x, ['CONSTANT']+name_x)
self.assertListEqual(reg.name_yend, name_yend)
self.assertListEqual(reg.name_q, name_q)
self.assertEqual(reg.name_y, name_y)
self.assertEqual(reg.name_w, name_w)
self.assertEqual(reg.name_gwk, name_gwk)
self.assertEqual(reg.name_ds, name_ds)
def test_n_k(self):
reg = TSLS(self.y, self.X, self.yd, self.q, sig2n_k=True)
betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
np.testing.assert_array_almost_equal(reg.betas, betas, 7)
vm = np.array([[ 243.99486949, 11.04572682, -10.16711028],
[ 11.04572682, 2.13183469, -1.08467261],
[ -10.16711028, -1.08467261, 0.67018062]])
np.testing.assert_array_almost_equal(reg.vm, vm, 7)
def test_white(self):
reg = TSLS(self.y, self.X, self.yd, self.q, robust='white')
betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
np.testing.assert_array_almost_equal(reg.betas, betas, 7)
vm = np.array([[ 208.27139316, 15.6687805 , -11.53686154],
[ 15.6687805 , 2.26882747, -1.30312033],
[ -11.53686154, -1.30312033, 0.81940656]])
np.testing.assert_array_almost_equal(reg.vm, vm, 7)
self.assertEqual(reg.robust, 'white')
def test_hac(self):
gwk = pysal.kernelW_from_shapefile(pysal.examples.get_path('columbus.shp'),k=5,function='triangular', fixed=False)
reg = TSLS(self.y, self.X, self.yd, self.q, robust='hac', gwk=gwk)
betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
np.testing.assert_array_almost_equal(reg.betas, betas, 7)
vm = np.array([[ 225.0795089 , 17.11660041, -12.22448566],
[ 17.67097154, 2.47483461, -1.4183641 ],
[ -12.45093722, -1.40495464, 0.8700441 ]])
np.testing.assert_array_almost_equal(reg.vm, vm, 7)
self.assertEqual(reg.robust, 'hac')
def test_spatial(self):
w = pysal.queen_from_shapefile(pysal.examples.get_path('columbus.shp'))
reg = TSLS(self.y, self.X, self.yd, self.q, spat_diag=True, w=w)
betas = np.array([[ 88.46579584], [ 0.5200379 ], [ -1.58216593]])
np.testing.assert_array_almost_equal(reg.betas, betas, 7)
vm = np.array([[ 229.05640809, 10.36945783, -9.54463414],
[ 10.36945783, 2.0013142 , -1.01826408],
[ -9.54463414, -1.01826408, 0.62914915]])
np.testing.assert_array_almost_equal(reg.vm, vm, 7)
ak_test = np.array([ 1.16816972, 0.27977763])
np.testing.assert_array_almost_equal(reg.ak_test, ak_test, 7)
def setUp(self):
db = pysal.open(pysal.examples.get_path("columbus.dbf"), 'r')
y = np.array(db.by_col("CRIME"))
y = np.reshape(y, (49, 1))
self.y = y
X = []
X.append(db.by_col("INC"))
X = np.array(X).T
self.X = X
yd = []
yd.append(db.by_col("HOVAL"))
yd = np.array(yd).T
self.yd = yd
q = []
q.append(db.by_col("DISCBD"))
q = np.array(q).T
self.q = q
reg = TSLS(y, X, yd, q=q)
self.reg = reg
w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
w.transform = 'r'
self.w = w
from pysal.common import RTOL
# create regression object used by the apatial tests
db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
y = np.array(db.by_col("CRIME"))
y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
X = np.array(X).T
yd = []
yd.append(db.by_col("HOVAL"))
yd = np.array(yd).T
q = []
q.append(db.by_col("DISCBD"))
q = np.array(q).T
reg = TSLS(y, X, yd, q)
# create regression object for spatial test
db = pysal.open(pysal.examples.get_path("columbus.dbf"),'r')
y = np.array(db.by_col("HOVAL"))
y = np.reshape(y, (49,1))
X = np.array(db.by_col("INC"))
X = np.reshape(X, (49,1))
yd = np.array(db.by_col("CRIME"))
yd = np.reshape(yd, (49,1))
q = np.array(db.by_col("DISCBD"))
q = np.reshape(q, (49,1))
w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
w.transform = 'r'
regsp = GM_Lag(y, X, w=w, yend=yd, q=q, w_lags=2)