def test_probs(self, close_figures):
nobs = self.nobs
probs = self.res.predict_prob()
freq = np.bincount(self.endog) / nobs
tzi = dia.test_chisquare_prob(self.res, probs[:, :2])
# regression numbers
tzi1 = (0.387770845, 0.5334734738)
assert_allclose(tzi[:2], tzi1, rtol=5e-5)
# smoke test for plot
dia.plot_probs(freq, probs.mean(0))
def plot_probs(self, label='predicted', upp_xlim=None,
fig=None):
"""Plot observed versus predicted frequencies for entire sample.
"""
probs_predicted = self.probs_predicted.sum(0)
k_probs = len(probs_predicted)
freq = np.bincount(self.results.model.endog.astype(int),
minlength=k_probs)[:k_probs]
fig = plot_probs(freq, probs_predicted,
label=label, upp_xlim=upp_xlim,
fig=fig)
return fig
def test_probs(self):
nobs = self.nobs
probs = self.res.predict_prob()
freq = np.bincount(self.endog) / nobs
tzi = dia.test_chisquare_prob(self.res, probs[:, :2])
# regression numbers
tzi1 = (0.387770845, 0.5334734738)
assert_allclose(tzi[:2], tzi1)
# smoke test for plot
try:
import matplotlib.pyplot as plt
except ImportError:
return
fig = dia.plot_probs(freq, probs.mean(0))
plt.close(fig)
def test_probs(self):
nobs = self.nobs
probs = self.res.predict_prob()
freq = np.bincount(self.endog) / nobs
tzi = dia.test_chisquare_prob(self.res, probs[:, :2])
# regression numbers
tzi1 = (0.387770845, 0.5334734738)
assert_allclose(tzi[:2], tzi1, rtol=5e-5)
# smoke test for plot
try:
import matplotlib.pyplot as plt
except ImportError:
return
fig = dia.plot_probs(freq, probs.mean(0))
plt.close(fig)