def test_score(self):
n = 200
m = 5
p = 3
pr = 2
for jl in 0, 1:
for reml in False, True:
for cov_pen_wt in 0, 10:
cov_pen = penalties.PSD(cov_pen_wt)
np.random.seed(355890504)
exog_fe = np.random.normal(size=(n * m, p))
exog_re = np.random.normal(size=(n * m, pr))
endog = exog_fe.sum(1) + np.random.normal(size=n * m)
groups = np.kron(range(n), np.ones(m))
md = MixedLM(endog, exog_fe, groups, exog_re)
md.reml = reml
md.cov_pen = cov_pen
if jl == 0:
like = lambda x: -md.loglike_sqrt(x)
score = lambda x: -md.score_sqrt(x)
else:
like = lambda x: -md.loglike(x)
score = lambda x: -md.score(x)
for kr in range(5):
fe_params = np.random.normal(size=p)
cov_re = np.random.normal(size=(pr, pr))
cov_re = np.dot(cov_re.T, cov_re)
params_prof = md._pack(fe_params, cov_re)
gr = score(params_prof)
ngr = np.zeros_like(gr)
for k in range(len(ngr)):
def f(x):
pp = params_prof.copy()
pp[k] = x
return like(pp)
ngr[k] = derivative(f, params_prof[k], dx=1e-6)
assert_almost_equal(gr / ngr,
np.ones(len(gr)),
decimal=3)
def test_score(self):
n = 200
m = 5
p = 3
pr = 2
for jl in 0,1:
for reml in False,True:
for cov_pen_wt in 0,10:
cov_pen = penalties.PSD(cov_pen_wt)
np.random.seed(355890504)
exog_fe = np.random.normal(size=(n*m, p))
exog_re = np.random.normal(size=(n*m, pr))
endog = exog_fe.sum(1) + np.random.normal(size=n*m)
groups = np.kron(range(n), np.ones(m))
md = MixedLM(endog, exog_fe, groups, exog_re)
md.reml = reml
md.cov_pen = cov_pen
if jl == 0:
like = lambda x: -md.loglike_sqrt(x)
score = lambda x: -md.score_sqrt(x)
else:
like = lambda x: -md.loglike(x)
score = lambda x: -md.score(x)
for kr in range(5):
fe_params = np.random.normal(size=p)
cov_re = np.random.normal(size=(pr,pr))
cov_re = np.dot(cov_re.T, cov_re)
params_prof = md._pack(fe_params, cov_re)
gr = score(params_prof)
ngr = np.zeros_like(gr)
for k in range(len(ngr)):
def f(x):
pp = params_prof.copy()
pp[k] = x
return like(pp)
ngr[k] = derivative(f, params_prof[k],
dx=1e-6)
assert_almost_equal(gr / ngr, np.ones(len(gr)),
decimal=3)
