def test_init_white_(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
base_gm_lag = GM_Lag(self.y, self.X, w=self.w, w_lags=2, robust='white')
tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295 ])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
gwk = pysal.lib.weights.Kernel.from_shapefile(pysal.lib.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
base_gm_lag = GM_Lag(self.y, self.X, w=self.w, w_lags=2, robust='hac', gwk=gwk)
tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([ 19.08513569, 0.51769543, 0.18244862, 0.35460553])
np.testing.assert_allclose(dbetas, se_betas)
def test_lag_q(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49,1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49,1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49,1))
reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2, lag_q=False)
tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49,1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49,1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49,1))
reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2)
tbetas = np.array([[ 100.79359082], [ -0.50215501], [ -1.14881711], [ -0.38235022]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_white_(self):
w_lags = 2
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w, None, None, w_lags, True)
self.X = np.hstack((np.ones(self.y.shape),self.X))
base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, w_lags=w_lags, robust='white')
tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([ 20.47077481, 0.50613931, 0.20138425, 0.38028295 ])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49, 1))
X = SP.csr_matrix(X)
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2)
tbetas = np.array([[100.79359082], [-0.50215501], [-1.14881711],
[-0.38235022]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([53.0829123, 1.02511494, 0.57589064, 0.59891744])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
w_lags = 2
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w, None, None, w_lags, True)
self.X = np.hstack((np.ones(self.y.shape),self.X))
gwk = pysal.lib.weights.Kernel.from_shapefile(pysal.lib.examples.get_path('columbus.shp'),k=15,function='triangular', fixed=False)
base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, w_lags=w_lags, robust='hac', gwk=gwk)
tbetas = np.array([[ 4.53017056e+01], [ 6.20888617e-01], [ -4.80723451e-01], [ 2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([ 19.08513569, 0.51769543, 0.18244862, 0.35460553])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_white_(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
base_gm_lag = GM_Lag(self.y,
self.X,
w=self.w,
w_lags=2,
robust='white')
tbetas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([20.47077481, 0.50613931, 0.20138425, 0.38028295])
np.testing.assert_allclose(dbetas, se_betas)
def test_lag_q(self):
w_lags = 2
X = np.array(self.db.by_col("INC"))
self.X = np.reshape(X, (49,1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49,1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49,1))
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, w_lags, False)
self.X = np.hstack((np.ones(self.y.shape),self.X))
reg = BaseGM_Lag(self.y, self.X, w=self.w.sparse, yend=yd2, q=q2, w_lags=w_lags, lag_q=False)
tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
w_lags = 2
X = np.array(self.db.by_col("INC"))
self.X = np.reshape(X, (49,1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49,1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49,1))
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, w_lags, True)
self.X = np.hstack((np.ones(self.y.shape),self.X))
reg = BaseGM_Lag(self.y, self.X, w=self.w.sparse, yend=yd2, q=q2, w_lags=w_lags)
tbetas = np.array([[ 100.79359082], [ -0.50215501], [ -1.14881711], [ -0.38235022]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
self.X = np.reshape(X, (49, 1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w, yd,
q, 2, True)
self.X = np.hstack((np.ones(self.y.shape), self.X))
self.X = SP.csr_matrix(self.X)
reg = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2)
tbetas = np.array([[100.79359082], [-0.50215501], [-1.14881711],
[-0.38235022]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([53.0829123, 1.02511494, 0.57589064, 0.59891744])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_hac_(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
gwk = pysal.lib.weights.Kernel.from_shapefile(
pysal.lib.examples.get_path('columbus.shp'),
k=15,
function='triangular',
fixed=False)
base_gm_lag = GM_Lag(self.y,
self.X,
w=self.w,
w_lags=2,
robust='hac',
gwk=gwk)
tbetas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([19.08513569, 0.51769543, 0.18244862, 0.35460553])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_white_(self):
w_lags = 2
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w,
None, None, w_lags, True)
self.X = np.hstack((np.ones(self.y.shape), self.X))
base_gm_lag = BaseGM_Lag(self.y,
self.X,
yend=yd2,
q=q2,
w=self.w.sparse,
w_lags=w_lags,
robust='white')
tbetas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([20.47077481, 0.50613931, 0.20138425, 0.38028295])
np.testing.assert_allclose(dbetas, se_betas)
def test_lag_q(self):
w_lags = 2
X = np.array(self.db.by_col("INC"))
self.X = np.reshape(X, (49, 1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
yd2, q2 = pysal.model.spreg.utils.set_endog(self.y, self.X, self.w, yd,
q, w_lags, False)
self.X = np.hstack((np.ones(self.y.shape), self.X))
reg = BaseGM_Lag(self.y,
self.X,
w=self.w.sparse,
yend=yd2,
q=q2,
w_lags=w_lags,
lag_q=False)
tbetas = np.array([[108.83261383], [-0.48041099], [-1.18950006],
[-0.56140186]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_allclose(dbetas, se_betas)
def test_se_betas(self):
obs = diagnostics.se_betas(reg)
exp = np.array([4.73548613, 0.33413076, 0.10319868])
np.testing.assert_allclose(obs,exp, RTOL)
