def test_profile_inference(self):
# Smoke test
np.random.seed(9814)
k_fe = 2
gsize = 3
n_grp = 100
exog = np.random.normal(size=(n_grp * gsize, k_fe))
exog_re = np.ones((n_grp * gsize, 1))
groups = np.kron(np.arange(n_grp), np.ones(gsize))
vca = np.random.normal(size=n_grp * gsize)
vcb = np.random.normal(size=n_grp * gsize)
errors = 0
g_errors = np.kron(np.random.normal(size=100), np.ones(gsize))
errors += g_errors + exog_re[:, 0]
rc = np.random.normal(size=n_grp)
errors += np.kron(rc, np.ones(gsize)) * vca
rc = np.random.normal(size=n_grp)
errors += np.kron(rc, np.ones(gsize)) * vcb
errors += np.random.normal(size=n_grp * gsize)
endog = exog.sum(1) + errors
vc = {"a": {}, "b": {}}
for k in range(n_grp):
ii = np.flatnonzero(groups == k)
vc["a"][k] = vca[ii][:, None]
vc["b"][k] = vcb[ii][:, None]
rslt = MixedLM(endog, exog, groups=groups, exog_re=exog_re, exog_vc=vc).fit()
rslt.profile_re(0, vtype="re", dist_low=1, num_low=3, dist_high=1, num_high=3)
rslt.profile_re("b", vtype="vc", dist_low=0.5, num_low=3, dist_high=0.5, num_high=3)
def test_profile_inference(self):
# Smoke test
np.random.seed(9814)
k_fe = 2
gsize = 3
n_grp = 100
exog = np.random.normal(size=(n_grp * gsize, k_fe))
exog_re = np.ones((n_grp * gsize, 1))
groups = np.kron(np.arange(n_grp), np.ones(gsize))
vca = np.random.normal(size=n_grp * gsize)
vcb = np.random.normal(size=n_grp * gsize)
errors = 0
g_errors = np.kron(np.random.normal(size=100), np.ones(gsize))
errors += g_errors + exog_re[:, 0]
rc = np.random.normal(size=n_grp)
errors += np.kron(rc, np.ones(gsize)) * vca
rc = np.random.normal(size=n_grp)
errors += np.kron(rc, np.ones(gsize)) * vcb
errors += np.random.normal(size=n_grp * gsize)
endog = exog.sum(1) + errors
vc = {"a": {}, "b": {}}
for k in range(n_grp):
ii = np.flatnonzero(groups == k)
vc["a"][k] = vca[ii][:, None]
vc["b"][k] = vcb[ii][:, None]
rslt = MixedLM(endog, exog, groups=groups,
exog_re=exog_re, exog_vc=vc).fit()
rslt.profile_re(0, vtype='re', dist_low=1, num_low=3, dist_high=1,
num_high=3)
rslt.profile_re('b', vtype='vc', dist_low=0.5, num_low=3,
dist_high=0.5, num_high=3)
def test_profile(self):
np.random.seed(9814)
exog = np.random.normal(size=(300, 4))
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = np.kron(np.random.normal(size=100), [1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mdf1 = MixedLM(endog, exog, groups).fit(niter_em=10)
mdf1.profile_re(0, dist_low=0.1, num_low=1, dist_high=0.1, num_high=1)
def test_profile(self):
np.random.seed(9814)
exog = np.random.normal(size=(300, 4))
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = np.kron(np.random.normal(size=100), [1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mdf1 = MixedLM(endog, exog, groups).fit(niter_em=10)
mdf1.profile_re(0, dist_low=0.1, num_low=1, dist_high=0.1, num_high=1)
def test_profile(self):
# Smoke test
np.random.seed(9814)
k_fe = 4
gsize = 3
n_grp = 100
exog = np.random.normal(size=(n_grp * gsize, k_fe))
groups = np.kron(np.arange(n_grp), np.ones(gsize))
g_errors = np.kron(np.random.normal(size=100), np.ones(gsize))
endog = exog.sum(1) + g_errors + np.random.normal(size=n_grp * gsize)
rslt = MixedLM(endog, exog, groups).fit(niter_em=10)
prof = rslt.profile_re(0, dist_low=0.1, num_low=1, dist_high=0.1,
num_high=1)
def test_profile(self):
# Smoke test
np.random.seed(9814)
k_fe = 4
gsize = 3
n_grp = 100
exog = np.random.normal(size=(n_grp * gsize, k_fe))
groups = np.kron(np.arange(n_grp), np.ones(gsize))
g_errors = np.kron(np.random.normal(size=100), np.ones(gsize))
endog = exog.sum(1) + g_errors + np.random.normal(size=n_grp * gsize)
rslt = MixedLM(endog, exog, groups).fit(niter_em=10)
prof = rslt.profile_re(0, dist_low=0.1, num_low=1, dist_high=0.1,
num_high=1)
