def test_glmmexpfam_precise():
nsamples = 10
random = RandomState(0)
X = random.randn(nsamples, 5)
K = linear_eye_cov().value()
QS = economic_qs(K)
ntri = random.randint(1, 30, nsamples)
nsuc = [random.randint(0, i) for i in ntri]
glmm = GLMMExpFam(nsuc, ["binomial", ntri], X, QS)
glmm.beta = asarray([1.0, 0, 0.5, 0.1, 0.4])
glmm.scale = 1.0
assert_allclose(glmm.lml(), -44.74191041468836, atol=ATOL, rtol=RTOL)
glmm.scale = 2.0
assert_allclose(glmm.lml(), -36.19907331929086, atol=ATOL, rtol=RTOL)
glmm.scale = 3.0
assert_allclose(glmm.lml(), -33.02139830387104, atol=ATOL, rtol=RTOL)
glmm.scale = 4.0
assert_allclose(glmm.lml(), -31.42553401678996, atol=ATOL, rtol=RTOL)
glmm.scale = 5.0
assert_allclose(glmm.lml(), -30.507029479473243, atol=ATOL, rtol=RTOL)
glmm.scale = 6.0
assert_allclose(glmm.lml(), -29.937569702301232, atol=ATOL, rtol=RTOL)
glmm.delta = 0.1
assert_allclose(glmm.lml(), -30.09977907145003, atol=ATOL, rtol=RTOL)
assert_allclose(glmm._check_grad(), 0, atol=1e-3, rtol=RTOL)
def test_glmmexpfam_delta_one_zero():
random = RandomState(1)
n = 30
X = random.randn(n, 6)
K = dot(X, X.T)
K /= K.diagonal().mean()
QS = economic_qs(K)
ntri = random.randint(1, 30, n)
nsuc = [random.randint(0, i) for i in ntri]
glmm = GLMMExpFam(nsuc, ("binomial", ntri), X, QS)
glmm.beta = asarray([1.0, 0, 0.5, 0.1, 0.4, -0.2])
glmm.delta = 0
assert_allclose(glmm.lml(), -113.24570457063275)
assert_allclose(glmm._check_grad(step=1e-4), 0, atol=1e-2)
glmm.fit(verbose=False)
assert_allclose(glmm.lml(), -98.21144899310399, atol=ATOL, rtol=RTOL)
assert_allclose(glmm.delta, 0, atol=ATOL, rtol=RTOL)
glmm.delta = 1
assert_allclose(glmm.lml(), -98.00058169240869, atol=ATOL, rtol=RTOL)
assert_allclose(glmm._check_grad(step=1e-4), 0, atol=1e-1)
glmm.fit(verbose=False)
assert_allclose(glmm.lml(), -72.82680948264196, atol=ATOL, rtol=RTOL)
assert_allclose(glmm.delta, 0.9999999850988439, atol=ATOL, rtol=RTOL)
def test_glmmexpfam_scale_very_high():
nsamples = 10
random = RandomState(0)
X = random.randn(nsamples, 5)
K = linear_eye_cov().value()
QS = economic_qs(K)
ntri = random.randint(1, 30, nsamples)
nsuc = [random.randint(0, i) for i in ntri]
glmm = GLMMExpFam(nsuc, ("binomial", ntri), X, QS)
glmm.beta = asarray([1.0, 0, 0.5, 0.1, 0.4])
glmm.scale = 30.0
assert_allclose(glmm.lml(), -29.632791380478736, atol=ATOL, rtol=RTOL)
assert_allclose(glmm._check_grad(), 0, atol=1e-3)
def test_glmmexpfam_delta1():
nsamples = 10
random = RandomState(0)
X = random.randn(nsamples, 5)
K = linear_eye_cov().value()
QS = economic_qs(K)
ntri = random.randint(1, 30, nsamples)
nsuc = [random.randint(0, i) for i in ntri]
glmm = GLMMExpFam(nsuc, ("binomial", ntri), X, QS)
glmm.beta = asarray([1.0, 0, 0.5, 0.1, 0.4])
glmm.delta = 1
assert_allclose(glmm.lml(), -47.09677870648636, atol=ATOL, rtol=RTOL)
assert_allclose(glmm._check_grad(), 0, atol=1e-4)