def test_glmmexpfam_qs_none():
nsamples = 10
random = RandomState(0)
X = random.randn(nsamples, 5)
K = linear_eye_cov().value()
z = random.multivariate_normal(0.2 * ones(nsamples), K)
ntri = random.randint(1, 30, nsamples)
nsuc = zeros(nsamples, dtype=int)
for (i, ni) in enumerate(ntri):
nsuc[i] += sum(z[i] + 0.2 * random.randn(ni) > 0)
ntri = ascontiguousarray(ntri)
glmm = GLMMExpFam(nsuc, ("binomial", ntri), X, None)
assert_allclose(glmm.lml(), -38.30173374439622, atol=ATOL, rtol=RTOL)
glmm.fix("beta")
glmm.fix("scale")
glmm.fit(verbose=False)
assert_allclose(glmm.lml(), -32.03927471370041, atol=ATOL, rtol=RTOL)
glmm.unfix("beta")
glmm.unfix("scale")
glmm.fit(verbose=False)
assert_allclose(glmm.lml(), -19.575736561760586, atol=ATOL, rtol=RTOL)
def test_glmmexpfam_optimize():
nsamples = 10
random = RandomState(0)
X = random.randn(nsamples, 5)
K = linear_eye_cov().value()
z = random.multivariate_normal(0.2 * ones(nsamples), K)
QS = economic_qs(K)
ntri = random.randint(1, 30, nsamples)
nsuc = zeros(nsamples, dtype=int)
for (i, ni) in enumerate(ntri):
nsuc[i] += sum(z[i] + 0.2 * random.randn(ni) > 0)
ntri = ascontiguousarray(ntri)
glmm = GLMMExpFam(nsuc, ("binomial", ntri), X, QS)
assert_allclose(glmm.lml(), -29.102168129099287, atol=ATOL, rtol=RTOL)
glmm.fix("beta")
glmm.fix("scale")
glmm.fit(verbose=False)
assert_allclose(glmm.lml(), -27.635788105778012, atol=ATOL, rtol=RTOL)
glmm.unfix("beta")
glmm.unfix("scale")
glmm.fit(verbose=False)
assert_allclose(glmm.lml(), -19.68486269551159, atol=ATOL, rtol=RTOL)
def test_glmmexpfam_bernoulli_probit_problematic():
random = RandomState(1)
N = 30
G = random.randn(N, N + 50)
y = bernoulli_sample(0.0, G, random_state=random)
G = ascontiguousarray(G, dtype=float)
_stdnorm(G, 0, out=G)
G /= sqrt(G.shape[1])
QS = economic_qs_linear(G)
S0 = QS[1]
S0 /= S0.mean()
X = ones((len(y), 1))
model = GLMMExpFam(y, "probit", X, QS=(QS[0], QS[1]))
model.delta = 0
model.fix("delta")
model.fit(verbose=False)
assert_allclose(model.lml(), -20.725623168378615, atol=ATOL, rtol=RTOL)
assert_allclose(model.delta, 0.0001220703125, atol=1e-3)
assert_allclose(model.scale, 0.33022865011938707, atol=ATOL, rtol=RTOL)
assert_allclose(model.beta, [-0.002617161564786044], atol=ATOL, rtol=RTOL)
h20 = model.scale * (1 - model.delta) / (model.scale + 1)
model.unfix("delta")
model.delta = 0.5
model.scale = 1.0
model.fit(verbose=False)
assert_allclose(model.lml(), -20.725623168378522, atol=ATOL, rtol=RTOL)
assert_allclose(model.delta, 0.5017852859580029, atol=1e-3)
assert_allclose(model.scale, 0.9928931515372, atol=ATOL, rtol=RTOL)
assert_allclose(model.beta, [-0.003203427206253548], atol=ATOL, rtol=RTOL)
h21 = model.scale * (1 - model.delta) / (model.scale + 1)
assert_allclose(h20, h21, atol=ATOL, rtol=RTOL)
def test_glmmexpfam_bernoulli_problematic():
random = RandomState(1)
N = 30
G = random.randn(N, N + 50)
y = bernoulli_sample(0.0, G, random_state=random)
G = ascontiguousarray(G, dtype=float)
_stdnorm(G, 0, out=G)
G /= sqrt(G.shape[1])
QS = economic_qs_linear(G)
S0 = QS[1]
S0 /= S0.mean()
X = ones((len(y), 1))
model = GLMMExpFam(y, "bernoulli", X, QS=(QS[0], QS[1]))
model.delta = 0
model.fix("delta")
model.fit(verbose=False)
assert_allclose(model.lml(), -20.727007958026853, atol=ATOL, rtol=RTOL)
assert_allclose(model.delta, 0, atol=1e-3)
assert_allclose(model.scale, 0.879915823030081, atol=ATOL, rtol=RTOL)
assert_allclose(model.beta, [-0.00247856564728], atol=ATOL, rtol=RTOL)