本文整理汇总了Python中statsmodels.regression.mixed_linear_model.MixedLMParams.from_components方法的典型用法代码示例。如果您正苦于以下问题：Python MixedLMParams.from_components方法的具体用法？Python MixedLMParams.from_components怎么用？Python MixedLMParams.from_components使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类statsmodels.regression.mixed_linear_model.MixedLMParams的用法示例。
在下文中一共展示了MixedLMParams.from_components方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_compare_numdiff
# 需要导入模块: from statsmodels.regression.mixed_linear_model import MixedLMParams [as 别名]
# 或者: from statsmodels.regression.mixed_linear_model.MixedLMParams import from_components [as 别名]
def test_compare_numdiff(self, use_sqrt, reml, profile_fe):
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp * grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp * grpsize, k_re))
exog_re[:, 0] = 1
exog_vc = np.random.normal(size=(n_grp * grpsize, 3))
slopes = np.random.normal(size=(n_grp, k_re))
slopes[:, -1] *= 2
slopes = np.kron(slopes, np.ones((grpsize, 1)))
slopes_vc = np.random.normal(size=(n_grp, 3))
slopes_vc = np.kron(slopes_vc, np.ones((grpsize, 1)))
slopes_vc[:, -1] *= 2
re_values = (slopes * exog_re).sum(1)
vc_values = (slopes_vc * exog_vc).sum(1)
err = np.random.normal(size=n_grp * grpsize)
endog = exog_fe.sum(1) + re_values + vc_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
vc = {"a": {}, "b": {}}
for i in range(n_grp):
ix = np.flatnonzero(groups == i)
vc["a"][i] = exog_vc[ix, 0:2]
vc["b"][i] = exog_vc[ix, 2:3]
model = MixedLM(
endog,
exog_fe,
groups,
exog_re,
exog_vc=vc,
use_sqrt=use_sqrt)
rslt = model.fit(reml=reml)
loglike = loglike_function(
model, profile_fe=profile_fe, has_fe=not profile_fe)
try:
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
vcomp = np.random.normal(size=2)**2
params = MixedLMParams.from_components(
fe_params, cov_re=cov_re, vcomp=vcomp)
params_vec = params.get_packed(
has_fe=not profile_fe, use_sqrt=use_sqrt)
# Check scores
gr = -model.score(params, profile_fe=profile_fe)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-3)
# Check Hessian matrices at the MLE (we don't have
# the profile Hessian matrix and we don't care
# about the Hessian for the square root
# transformed parameter).
if (profile_fe is False) and (use_sqrt is False):
hess = -model.hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed(
use_sqrt=False, has_fe=True)
loglike_h = loglike_function(
model, profile_fe=False, has_fe=True)
nhess = nd.approx_hess(params_vec, loglike_h)
assert_allclose(hess, nhess, rtol=1e-3)
except AssertionError:
# See GH#5628; because this test fails unpredictably but only on
# OSX, we only xfail it there.
if PLATFORM_OSX:
pytest.xfail("fails on OSX due to unresolved "
"numerical differences")
else:
raise示例2: test_compare_numdiff
# 需要导入模块: from statsmodels.regression.mixed_linear_model import MixedLMParams [as 别名]
# 或者: from statsmodels.regression.mixed_linear_model.MixedLMParams import from_components [as 别名]
def test_compare_numdiff(self):
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
for use_sqrt in False, True:
for reml in False, True:
for profile_fe in False, True:
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp * grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp * grpsize, k_re))
exog_re[:, 0] = 1
exog_vc = np.random.normal(size=(n_grp * grpsize, 3))
slopes = np.random.normal(size=(n_grp, k_re))
slopes[:, -1] *= 2
slopes = np.kron(slopes, np.ones((grpsize, 1)))
slopes_vc = np.random.normal(size=(n_grp, 3))
slopes_vc = np.kron(slopes_vc, np.ones((grpsize, 1)))
slopes_vc[:, -1] *= 2
re_values = (slopes * exog_re).sum(1)
vc_values = (slopes_vc * exog_vc).sum(1)
err = np.random.normal(size=n_grp * grpsize)
endog = exog_fe.sum(1) + re_values + vc_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
vc = {"a": {}, "b": {}}
for i in range(n_grp):
ix = np.flatnonzero(groups == i)
vc["a"][i] = exog_vc[ix, 0:2]
vc["b"][i] = exog_vc[ix, 2:3]
model = MixedLM(endog, exog_fe, groups, exog_re, exog_vc=vc, use_sqrt=use_sqrt)
rslt = model.fit(reml=reml)
loglike = loglike_function(model, profile_fe=profile_fe, has_fe=not profile_fe)
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
vcomp = np.random.normal(size=2) ** 2
params = MixedLMParams.from_components(fe_params, cov_re=cov_re, vcomp=vcomp)
params_vec = params.get_packed(has_fe=not profile_fe, use_sqrt=use_sqrt)
# Check scores
gr = -model.score(params, profile_fe=profile_fe)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-3)
# Check Hessian matrices at the MLE (we don't have
# the profile Hessian matrix and we don't care
# about the Hessian for the square root
# transformed parameter).
if (profile_fe is False) and (use_sqrt is False):
hess = -model.hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed(use_sqrt=False, has_fe=True)
loglike_h = loglike_function(model, profile_fe=False, has_fe=True)
nhess = nd.approx_hess(params_vec, loglike_h)
assert_allclose(hess, nhess, rtol=1e-3)示例3: test_compare_numdiff
# 需要导入模块: from statsmodels.regression.mixed_linear_model import MixedLMParams [as 别名]
# 或者: from statsmodels.regression.mixed_linear_model.MixedLMParams import from_components [as 别名]
def test_compare_numdiff(self):
import statsmodels.tools.numdiff as nd
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 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(3558)
exog_fe = np.random.normal(size=(n_grp*grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp*grpsize, k_re))
exog_re[:, 0] = 1
slopes = np.random.normal(size=(n_grp, k_re))
slopes = np.kron(slopes, np.ones((grpsize,1)))
re_values = (slopes * exog_re).sum(1)
err = np.random.normal(size=n_grp*grpsize)
endog = exog_fe.sum(1) + re_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
if jl == 0:
md = MixedLM(endog, exog_fe, groups, exog_re)
score = lambda x: -md.score_sqrt(x)
hessian = lambda x : -md.hessian_sqrt(x)
else:
md = MixedLM(endog, exog_fe, groups, exog_re, use_sqrt=False)
score = lambda x: -md.score_full(x)
hessian = lambda x: -md.hessian_full(x)
md.reml = reml
md.cov_pen = cov_pen
loglike = lambda x: -md.loglike(x)
rslt = md.fit()
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
params = MixedLMParams.from_components(fe_params, cov_re)
if jl == 0:
params_vec = params.get_packed()
else:
params_vec = params.get_packed(use_sqrt=False)
# Check scores
gr = score(params)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-2)
# Hessian matrices don't agree well away from
# the MLE.
#if cov_pen_wt == 0:
# hess = hessian(params)
# nhess = nd.approx_hess(params_vec, loglike)
# assert_allclose(hess, nhess, rtol=1e-2)
# Check Hessian matrices at the MLE.
if cov_pen_wt == 0:
hess = hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed()
nhess = nd.approx_hess(params_vec, loglike)
assert_allclose(hess, nhess, rtol=1e-2)