def test_simple_poisson_map():
y, exog_fe, exog_vc, ident = gen_simple_poisson(10, 10, 0.2)
exog_vc = sparse.csr_matrix(exog_vc)
glmm1 = PoissonBayesMixedGLM(y, exog_fe, exog_vc, ident, vcp_p=0.5)
rslt1 = glmm1.fit_map()
assert_allclose(glmm1.logposterior_grad(rslt1.params),
np.zeros_like(rslt1.params),
atol=1e-3)
# This should give the same answer as above
glmm2 = PoissonBayesMixedGLM(y, exog_fe, exog_vc, ident, vcp_p=0.5)
rslt2 = glmm2.fit_map()
assert_allclose(rslt1.params, rslt2.params, atol=1e-4)
# Test the predict method
for linear in False, True:
for exog in None, exog_fe:
pr1 = rslt1.predict(linear=linear, exog=exog)
pr2 = rslt2.predict(linear=linear, exog=exog)
pr3 = glmm1.predict(rslt1.params, linear=linear, exog=exog)
pr4 = glmm2.predict(rslt2.params, linear=linear, exog=exog)
assert_allclose(pr1, pr2, rtol=1e-5)
assert_allclose(pr2, pr3, rtol=1e-5)
assert_allclose(pr3, pr4, rtol=1e-5)
if not linear:
assert_equal(pr1.min() >= 0, True)
assert_equal(pr2.min() >= 0, True)
assert_equal(pr3.min() >= 0, True)
def test_simple_poisson_map():
y, exog_fe, exog_vc, ident = gen_simple_poisson(10, 10, 0.2)
exog_vc = sparse.csr_matrix(exog_vc)
glmm1 = PoissonBayesMixedGLM(y, exog_fe, exog_vc, ident, vcp_p=0.5)
rslt1 = glmm1.fit_map()
assert_allclose(
glmm1.logposterior_grad(rslt1.params),
np.zeros_like(rslt1.params),
atol=1e-3)
# This should give the same answer as above
glmm2 = PoissonBayesMixedGLM(y, exog_fe, exog_vc, ident, vcp_p=0.5)
rslt2 = glmm2.fit_map()
assert_allclose(rslt1.params, rslt2.params, atol=1e-4)
# Test the predict method
for linear in False, True:
for exog in None, exog_fe:
pr1 = rslt1.predict(linear=linear, exog=exog)
pr2 = rslt2.predict(linear=linear, exog=exog)
pr3 = glmm1.predict(rslt1.params, linear=linear, exog=exog)
pr4 = glmm2.predict(rslt2.params, linear=linear, exog=exog)
assert_allclose(pr1, pr2, rtol=1e-5)
assert_allclose(pr2, pr3, rtol=1e-5)
assert_allclose(pr3, pr4, rtol=1e-5)
if not linear:
assert_equal(pr1.min() >= 0, True)
assert_equal(pr2.min() >= 0, True)
assert_equal(pr3.min() >= 0, True)
# Check dimensions and PSD status of cov_params
for rslt in rslt1, rslt2:
cp = rslt.cov_params()
p = len(rslt.params)
assert_equal(cp.shape, np.r_[p, p])
np.linalg.cholesky(cp)
def test_simple_poisson_map():
y, exog_fe, exog_vc, ident = gen_simple_poisson(10, 10, 0.2)
exog_vc = sparse.csr_matrix(exog_vc)
glmm1 = PoissonBayesMixedGLM(y, exog_fe, exog_vc, ident, vcp_p=0.5)
rslt1 = glmm1.fit_map()
assert_allclose(
glmm1.logposterior_grad(rslt1.params),
np.zeros_like(rslt1.params),
atol=1e-3)
# This should give the same answer as above
glmm2 = PoissonBayesMixedGLM(y, exog_fe, exog_vc, ident, vcp_p=0.5)
rslt2 = glmm2.fit_map()
assert_allclose(rslt1.params, rslt2.params, atol=1e-4)
# Test the predict method
for linear in False, True:
for exog in None, exog_fe:
pr1 = rslt1.predict(linear=linear, exog=exog)
pr2 = rslt2.predict(linear=linear, exog=exog)
pr3 = glmm1.predict(rslt1.params, linear=linear, exog=exog)
pr4 = glmm2.predict(rslt2.params, linear=linear, exog=exog)
assert_allclose(pr1, pr2, rtol=1e-5)
assert_allclose(pr2, pr3, rtol=1e-5)
assert_allclose(pr3, pr4, rtol=1e-5)
if not linear:
assert_equal(pr1.min() >= 0, True)
assert_equal(pr2.min() >= 0, True)
assert_equal(pr3.min() >= 0, True)
# Check dimensions and PSD status of cov_params
for rslt in rslt1, rslt2:
cp = rslt.cov_params()
p = len(rslt.params)
assert_equal(cp.shape, np.r_[p, p])
np.linalg.cholesky(cp)