def test_model(self):
# Only spatial lag
reg = HET.GM_Combo_Het(self.y, self.X, w=self.w, step1c=True)
betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
np.testing.assert_array_almost_equal(reg.betas,betas,7)
u = np.array([ 25.65156033])
np.testing.assert_array_almost_equal(reg.u[0],u,7)
ef = np.array([ 31.87664403])
np.testing.assert_array_almost_equal(reg.e_filtered[0],ef,7)
ep = np.array([ 28.30648145])
np.testing.assert_array_almost_equal(reg.e_pred[0],ep,7)
pe = np.array([ 52.16052155])
np.testing.assert_array_almost_equal(reg.predy_e[0],pe,7)
predy = np.array([ 54.81544267])
np.testing.assert_array_almost_equal(reg.predy[0],predy,7)
n = 49
self.assertAlmostEqual(reg.n,n)
k = 4
self.assertAlmostEqual(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_array_almost_equal(reg.y[0],y,7)
x = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_array_almost_equal(reg.x[0].toarray()[0],x,7)
yend = np.array([ 35.4585005])
np.testing.assert_array_almost_equal(reg.yend[0],yend,7)
q = np.array([ 18.594 , 24.7142675])
np.testing.assert_array_almost_equal(reg.q[0].toarray()[0],q,7)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
np.testing.assert_array_almost_equal(reg.z[0].toarray()[0],z,7)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
self.assertAlmostEqual(reg.iteration,its,7)
my = 38.436224469387746
self.assertAlmostEqual(reg.mean_y,my)
stdy = 18.466069465206047
self.assertAlmostEqual(reg.std_y,stdy)
vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
-1.01969471e+01, 2.74302006e+00],
[ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
1.95136385e-03, 3.70052723e-03],
[ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
7.65868344e-03, -7.30173070e-03],
[ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
2.78273684e-01, -6.89402590e-02],
[ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
-6.89402590e-02, 7.12034037e-02]])
np.testing.assert_array_almost_equal(reg.vm,vm,6)
pr2 = 0.3001582877472412
self.assertAlmostEqual(reg.pr2,pr2,7)
pr2_e = 0.35613102283621967
self.assertAlmostEqual(reg.pr2_e,pr2_e,7)
std_err = np.array([ 22.05035768, 0.32354439, 0.14685221, 0.52751653, 0.26683966])
np.testing.assert_array_almost_equal(reg.std_err,std_err,6)
z_stat = np.array([(2.6202684885795335, 0.00878605635338265), (2.2573385444145524, 0.023986928627746887), (-4.0351698589183433, 5.456281036278686e-05), (-0.42277935292121521, 0.67245625315942159), (2.1225002455741895, 0.033795752094112265)])
np.testing.assert_array_almost_equal(reg.z_stat,z_stat,6)
hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
7.24743592e+02, 1.70735413e+03],
[ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
1.10925200e+04, 2.23848036e+04],
[ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
2.34796298e+04, 6.70145378e+04],
[ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
1.16146226e+04, 2.30304624e+04],
[ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
2.30304624e+04, 6.69879858e+04]])
np.testing.assert_array_almost_equal(reg.hth,hth,4)
def autospace(y,
x,
w,
gwk,
opvalue=0.01,
combo=False,
name_y=None,
name_x=None,
name_w=None,
name_gwk=None,
name_ds=None):
"""
Runs automatic spatial regression using decision tree
Accounts for both heteroskedasticity and spatial autocorrelation
No endogenous variables
Parameters
----------
y : array
nx1 array for dependent variable
x : array
Two dimensional array with n rows and one column for each
independent (exogenous) variable, excluding the constant
w : pysal W object
Spatial weights object
gwk : pysal W object
Kernel spatial weights needed for HAC estimation. Note:
matrix must have ones along the main diagonal.
opvalue : real
p-value to be used in tests; default: opvalue = 0.01
combo : boolean
flag for use of combo model rather than HAC for lag-error
model; default: combo = False
Returns
-------
results : a dictionary with
results['final model']: one of
No Space - Homoskedastic
No Space - Heteroskedastic
Spatial Lag - Homoskedastic
Spatial Lag - Heteroskedastic
Spatial Error - Homoskedastic
Spatial Error - Heteroskedastic
Spatial Lag with Spatial Error - HAC
Spatial Lag with Spatial Error - Homoskedastic
Spatial Lag with Spatial Error - Heteroskedastic
Robust Tests not Significant - Check Model
results['heteroskedasticity']: True or False
results['spatial lag']: True or False
results['spatial error']: True or False
results['regression1']: regression object with base model (OLS)
results['regression2']: regression object with final model
"""
results = {}
results['spatial error'] = False
results['spatial lag'] = False
r1 = ols.OLS(y,
x,
w=w,
gwk=gwk,
spat_diag=True,
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_gwk=name_gwk,
name_ds=name_ds)
results['regression1'] = r1
Het = r1.koenker_bassett['pvalue']
if Het < opvalue:
Hetflag = True
else:
Hetflag = False
results['heteroskedasticity'] = Hetflag
model = lmChoice(r1, opvalue)
if model == "MIXED":
if not combo:
r2 = twosls_sp.GM_Lag(y,
x,
w=w,
gwk=gwk,
robust='hac',
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_gwk=name_gwk,
name_ds=name_ds)
results['final model'] = "Spatial Lag with Spatial Error - HAC"
elif Hetflag:
r2 = error_sp_het.GM_Combo_Het(y,
x,
w=w,
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_ds=name_ds)
results[
'final model'] = "Spatial Lag with Spatial Error - Heteroskedastic"
else:
r2 = error_sp_hom.GM_Combo_Hom(y,
x,
w=w,
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_ds=name_ds)
results[
'final model'] = "Spatial Lag with Spatial Error - Homoskedastic"
elif model == "ERROR":
results['spatial error'] = True
if Hetflag:
r2 = error_sp_het.GM_Error_Het(y,
x,
w,
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_ds=name_ds)
results['final model'] = "Spatial Error - Heteroskedastic"
else:
r2 = error_sp_hom.GM_Error_Hom(y,
x,
w,
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_ds=name_ds)
results['final model'] = "Spatial Error - Homoskedastic"
elif model == "LAG":
results['spatial lag'] = True
if Hetflag:
r2 = twosls_sp.GM_Lag(y,
x,
w=w,
robust='white',
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_ds=name_ds)
results['final model'] = "Spatial Lag - Heteroskedastic"
else:
r2 = twosls_sp.GM_Lag(y,
x,
w=w,
name_y=name_y,
name_x=name_x,
name_w=name_w,
name_ds=name_ds)
results['final model'] = "Spatial Lag - Homoskedastic"
else:
if Hetflag:
r2 = ols.OLS(y,
x,
robust='white',
name_y=name_y,
name_x=name_x,
name_ds=name_ds)
results['final model'] = "No Space - Heteroskedastic"
else:
r2 = r1
results['final model'] = "No Space - Homoskedastic"
results['regression2'] = r2
return results
