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
yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None,
None, 2, True)
self.X = np.hstack((np.ones(self.y.shape), self.X))
self.X = SP.csr_matrix(self.X)
gwk = pysal.kernelW_from_shapefile(
pysal.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,
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):
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_array_almost_equal(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_array_almost_equal(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.kernelW_from_shapefile(pysal.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_array_almost_equal(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_array_almost_equal(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_array_almost_equal(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
np.testing.assert_array_almost_equal(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_array_almost_equal(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_array_almost_equal(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
yd2, q2 = utils.set_endog(self.y, self.X, self.w, None, None, 2, True)
self.X = np.hstack((np.ones(self.y.shape),self.X))
self.X = SP.csr_matrix(self.X)
base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, 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_array_almost_equal(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_array_almost_equal(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
yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 2, True)
self.X = np.hstack((np.ones(self.y.shape), self.X))
self.X = SP.csr_matrix(self.X)
base_gm_lag = BaseGM_Lag(self.y, self.X, yend=yd2, q=q2, w=self.w, w_lags=2, robust="white")
tbetas = np.array([[4.53017056e01], [6.20888617e-01], [-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_array_almost_equal(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_array_almost_equal(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.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.kernelW_from_shapefile(pysal.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_array_almost_equal(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_array_almost_equal(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 = 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_array_almost_equal(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 53.0829123 , 1.02511494, 0.57589064, 0.59891744 ])
np.testing.assert_array_almost_equal(dbetas, se_betas)
def test_lag_q(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 = utils.set_endog(self.y, self.X, self.w, yd, q, 2, False)
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, lag_q=False)
tbetas = np.array( [[ 108.83261383], [ -0.48041099], [ -1.18950006], [ -0.56140186]])
np.testing.assert_array_almost_equal(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([ 58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_array_almost_equal(dbetas, se_betas)
def test_lag_q(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.spreg.utils.set_endog(self.y, self.X, self.w, yd, q, 2, False)
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, lag_q=False)
tbetas = np.array([[108.83261383], [-0.48041099], [-1.18950006], [-0.56140186]])
np.testing.assert_array_almost_equal(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_array_almost_equal(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.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_array_almost_equal(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([53.0829123, 1.02511494, 0.57589064, 0.59891744])
np.testing.assert_array_almost_equal(dbetas, se_betas)
def regimes_printout(model):
stds = diagnostics.se_betas(model)
tp = np.array(diagnostics.t_stat(model))
res = pd.DataFrame({'Coefficient': pd.Series(model.betas.flatten()), \
'Std. Error': pd.Series(stds) , \
'T-Stat': pd.Series(tp[:, 0].flatten()), \
'P-value': pd.Series(tp[:, 1].flatten())})
res = res.reindex(columns = ['Coefficient', 'Std. Error', 'T-Stat', 'P-value'])
inds = []
for lbl in model.name_x:
r, v = lbl.split('_-_')
inds.append((r, v))
inds = np.array(inds)
res['Regime'] = inds[:, 0]
res['Variable'] = inds[:, 1]
res = res.set_index(['Regime', 'Variable'])
res[''] = res['P-value'].apply(star_significance)
return res
def regimes_printout(model):
stds = diagnostics.se_betas(model)
tp = np.array(diagnostics.t_stat(model))
res = pd.DataFrame({'Coefficient': pd.Series(model.betas.flatten()), \
'Std. Error': pd.Series(stds) , \
'T-Stat': pd.Series(tp[:, 0].flatten()), \
'P-value': pd.Series(tp[:, 1].flatten())})
res = res.reindex(
columns=['Coefficient', 'Std. Error', 'T-Stat', 'P-value'])
inds = []
for lbl in model.name_x:
r, v = lbl.split('_-_')
inds.append((r, v))
inds = np.array(inds)
res['Regime'] = inds[:, 0]
res['Variable'] = inds[:, 1]
res = res.set_index(['Regime', 'Variable'])
res[''] = res['P-value'].apply(star_significance)
return res
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)
def test_se_betas(self):
obs = diagnostics.se_betas(reg)
exp = np.array([4.73548613, 0.33413076, 0.10319868])
np.testing.assert_array_almost_equal(obs, exp)
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)
def test_se_betas(self):
obs = diagnostics.se_betas(reg)
exp = np.array([4.73548613, 0.33413076, 0.10319868])
np.testing.assert_array_almost_equal(obs,exp)