def fit(self, framework='GLM', Quasi=False):
"""
Method that fits a particular count model usign the appropriate
estimation technique. Models include Poisson GLM, Negative Binomial GLM,
Quasi-Poisson - at the moment Poisson GLM is the only option.
TODO: add zero inflated variants and hurdle variants.
Parameters
----------
framework : string
estimation framework; default is GLM
"GLM" | "QUASI" |
"""
if (framework.lower() == 'glm'):
if not Quasi:
results = GLM(self.y,
self.X,
family=Poisson(),
constant=self.constant).fit()
else:
results = GLM(self.y,
self.X,
family=QuasiPoisson(),
constant=self.constant).fit()
return CountModelResults(results)
else:
raise NotImplemented(
'Poisson GLM is the only count model currently implemented')
def test_BS_NN_Offset(self):
est_Int = self.BS_NN_OFF.by_col(' est_Intercept')
se_Int = self.BS_NN_OFF.by_col(' se_Intercept')
t_Int = self.BS_NN_OFF.by_col(' t_Intercept')
est_OCC = self.BS_NN_OFF.by_col(' est_OCC_TEC')
se_OCC = self.BS_NN_OFF.by_col(' se_OCC_TEC')
t_OCC = self.BS_NN_OFF.by_col(' t_OCC_TEC')
est_OWN = self.BS_NN_OFF.by_col(' est_OWNH')
se_OWN = self.BS_NN_OFF.by_col(' se_OWNH')
t_OWN = self.BS_NN_OFF.by_col(' t_OWNH')
est_POP = self.BS_NN_OFF.by_col(' est_POP65')
se_POP = self.BS_NN_OFF.by_col(' se_POP65')
t_POP = self.BS_NN_OFF.by_col(' t_POP65')
est_UNEMP = self.BS_NN_OFF.by_col(' est_UNEMP')
se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP')
t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP')
yhat = self.BS_NN_OFF.by_col(' yhat')
pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1))
model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(),
kernel='bisquare', fixed=False)
rslt = model.fit()
AICc = get_AICc(rslt)
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 367.0)
self.assertAlmostEquals(np.floor(AIC), 361.0)
self.assertAlmostEquals(np.floor(BIC), 451.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02,
atol=1e-02)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01,
atol=1e-02)
np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03,
atol=1e-02)
np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02)
np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01,
atol=1e-02)
np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04,
atol=1e-02)
np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02)
np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01,
atol=1e-02)
np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03,
atol=1e-02)
np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02)
np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01,
atol=1e-02)
np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04,
atol=1e-02)
np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02,
atol=1e-02)
np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01,
atol=1e-02)
np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02)
np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02)
def test_golden_adapt_AICc_Poisson_wo_offset(self):
bw1 = 51.0
bw2 = Sel_BW(self.coords,
self.y,
self.X,
kernel='bisquare',
family=Poisson(),
fixed=False).search(criterion='AICc')
assert_allclose(bw1, bw2)
def test_PoissonGLM(self):
model = CountModel(self.y, self.X, family=Poisson())
results = model.fit('GLM')
np.testing.assert_allclose(results.params,
[3.92159085, 0.01183491, -0.01371397],
atol=1.0e-8)
self.assertIsInstance(results.family, Poisson)
self.assertEqual(results.n, 49)
self.assertEqual(results.k, 3)
self.assertEqual(results.df_model, 2)
self.assertEqual(results.df_resid, 46)
np.testing.assert_allclose(results.yhat, [
51.26831574, 50.15022766, 40.06142973, 34.13799739, 28.76119226,
42.6836241, 55.64593703, 34.08277997, 40.90389582, 37.19727958,
23.47459217, 26.12384057, 29.78303507, 25.96888223, 29.14073823,
26.04369592, 34.18996367, 32.28924005, 27.42284396, 72.69207879,
33.05316347, 36.52276972, 49.2551479, 35.33439632, 24.07252457,
31.67153709, 27.81699478, 25.38021219, 24.31759259, 23.13586161,
48.40724678, 48.57969818, 31.92596006, 43.3679231, 34.32925819,
51.78908089, 34.49778584, 27.56236198, 48.34273194, 57.50829097,
50.66038226, 54.68701352, 35.77103116, 43.21886784, 40.07615759,
49.98658004, 43.13352883, 40.28520774, 46.28910294
])
np.testing.assert_allclose(
results.cov_params,
[[1.70280610e-02, -6.18628383e-04, -2.21386966e-04],
[-6.18628383e-04, 2.61733917e-05, 6.77496445e-06],
[-2.21386966e-04, 6.77496445e-06, 3.75463502e-06]])
np.testing.assert_allclose(results.std_err,
[0.13049161, 0.00511599, 0.00193769],
atol=1.0e-8)
np.testing.assert_allclose(
results.pvalues,
[2.02901657e-198, 2.07052532e-002, 1.46788805e-012])
np.testing.assert_allclose(results.tvalues,
[30.0524361, 2.31331634, -7.07748998])
np.testing.assert_allclose(results.resid, [
28.73168426, -5.15022766, -14.06142973, -1.13799739, -5.76119226,
-13.6836241, 19.35406297, 2.91722003, 12.09610418, 58.80272042,
-3.47459217, -6.12384057, 12.21696493, 17.03111777, -11.14073823,
-7.04369592, 7.81003633, 27.71075995, 3.57715604, 8.30792121,
-13.05316347, -6.52276972, -1.2551479, 17.66560368, -6.07252457,
-11.67153709, 6.18300522, -2.38021219, 7.68240741, -1.13586161,
-16.40724678, -8.57969818, -7.92596006, -15.3679231, -7.32925819,
-15.78908089, 8.50221416, -4.56236198, -8.34273194, 4.49170903,
-8.66038226, -10.68701352, -9.77103116, -9.21886784, -12.07615759,
26.01341996, -1.13352883, -13.28520774, -10.28910294
])
self.assertAlmostEqual(results.deviance, 230.46013824817649)
self.assertAlmostEqual(results.llf, -247.42592089969378)
self.assertAlmostEqual(results.AIC, 500.85184179938756)
self.assertAlmostEqual(results.D2, 0.388656011675)
self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)
def test_Dispersion(self):
model = CountModel(self.y, self.X, family=Poisson())
results = model.fit('GLM')
phi = phi_disp(results)
alpha1 = alpha_disp(results)
alpha2 = alpha_disp(results, lambda x: x**2)
np.testing.assert_allclose(phi, [5.39968689, 2.3230411, 0.01008847],
atol=1.0e-8)
np.testing.assert_allclose(alpha1, [4.39968689, 2.3230411,
0.01008847], atol=1.0e-8)
np.testing.assert_allclose(alpha2, [0.10690133, 2.24709978,
0.01231683], atol=1.0e-8)
def test_GS_NN(self):
est_Int = self.GS_NN.by_col(' est_Intercept')
se_Int = self.GS_NN.by_col(' se_Intercept')
t_Int = self.GS_NN.by_col(' t_Intercept')
est_OCC = self.GS_NN.by_col(' est_OCC_TEC')
se_OCC = self.GS_NN.by_col(' se_OCC_TEC')
t_OCC = self.GS_NN.by_col(' t_OCC_TEC')
est_OWN = self.GS_NN.by_col(' est_OWNH')
se_OWN = self.GS_NN.by_col(' se_OWNH')
t_OWN = self.GS_NN.by_col(' t_OWNH')
est_POP = self.GS_NN.by_col(' est_POP65')
se_POP = self.GS_NN.by_col(' se_POP65')
t_POP = self.GS_NN.by_col(' t_POP65')
est_UNEMP = self.GS_NN.by_col(' est_UNEMP')
se_UNEMP = self.GS_NN.by_col(' se_UNEMP')
t_UNEMP = self.GS_NN.by_col(' t_UNEMP')
yhat = self.GS_NN.by_col(' yhat')
pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1, 1))
model = GWR(self.coords,
self.y,
self.X,
bw=50,
family=Poisson(),
kernel='gaussian',
fixed=False)
rslt = model.fit()
AICc = get_AICc(rslt)
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 21070.0)
self.assertAlmostEquals(np.floor(AIC), 21069.0)
self.assertAlmostEquals(np.floor(BIC), 21111.0)
np.testing.assert_allclose(est_Int, rslt.params[:, 0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:, 0], rtol=1e-02)
np.testing.assert_allclose(t_Int, rslt.tvalues[:, 0], rtol=1e-02)
np.testing.assert_allclose(est_OCC, rslt.params[:, 1], rtol=1e-03)
np.testing.assert_allclose(se_OCC, rslt.bse[:, 1], rtol=1e-02)
np.testing.assert_allclose(t_OCC, rslt.tvalues[:, 1], rtol=1e-02)
np.testing.assert_allclose(est_OWN, rslt.params[:, 2], rtol=1e-04)
np.testing.assert_allclose(se_OWN, rslt.bse[:, 2], rtol=1e-02)
np.testing.assert_allclose(t_OWN, rslt.tvalues[:, 2], rtol=1e-02)
np.testing.assert_allclose(est_POP, rslt.params[:, 3], rtol=1e-02)
np.testing.assert_allclose(se_POP, rslt.bse[:, 3], rtol=1e-02)
np.testing.assert_allclose(t_POP, rslt.tvalues[:, 3], rtol=1e-02)
np.testing.assert_allclose(est_UNEMP, rslt.params[:, 4], rtol=1e-02)
np.testing.assert_allclose(se_UNEMP, rslt.bse[:, 4], rtol=1e-02)
np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:, 4], rtol=1e-02)
np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
def __init__(self, y, X, family=Poisson(), constant=True):
self.y = self._check_counts(y)
self.X = X
self.constant = constant