def test_formulas(self):
np.random.seed(2410)
exog = np.random.normal(size=(300, 4))
exog_re = np.random.normal(size=300)
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = exog_re * np.kron(np.random.normal(size=100), [1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mod1 = MixedLM(endog, exog, groups, exog_re)
# test the names
assert_(mod1.data.xnames == ["x1", "x2", "x3", "x4"])
assert_(mod1.data.exog_re_names == ["x_re1"])
assert_(mod1.data.exog_re_names_full == ["x_re1 RE"])
rslt1 = mod1.fit()
# Fit with a formula, passing groups as the actual values.
df = pd.DataFrame({"endog": endog})
for k in range(exog.shape[1]):
df["exog%d" % k] = exog[:, k]
df["exog_re"] = exog_re
fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
re_fml = "0 + exog_re"
mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml, groups=groups)
assert_(mod2.data.xnames == ["exog0", "exog1", "exog2", "exog3"])
assert_(mod2.data.exog_re_names == ["exog_re"])
assert_(mod2.data.exog_re_names_full == ["exog_re RE"])
rslt2 = mod2.fit()
assert_almost_equal(rslt1.params, rslt2.params)
# Fit with a formula, passing groups as the variable name.
df["groups"] = groups
mod3 = MixedLM.from_formula(fml,
df,
re_formula=re_fml,
groups="groups")
assert_(mod3.data.xnames == ["exog0", "exog1", "exog2", "exog3"])
assert_(mod3.data.exog_re_names == ["exog_re"])
assert_(mod3.data.exog_re_names_full == ["exog_re RE"])
rslt3 = mod3.fit(start_params=rslt2.params)
assert_allclose(rslt1.params, rslt3.params, rtol=1e-4)
# Check default variance structure with non-formula model
# creation, also use different exog_re that produces a zero
# estimated variance parameter.
exog_re = np.ones(len(endog), dtype=np.float64)
mod4 = MixedLM(endog, exog, groups, exog_re)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rslt4 = mod4.fit()
from statsmodels.formula.api import mixedlm
mod5 = mixedlm(fml, df, groups="groups")
assert_(mod5.data.exog_re_names == ["groups"])
assert_(mod5.data.exog_re_names_full == ["groups RE"])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rslt5 = mod5.fit()
assert_almost_equal(rslt4.params, rslt5.params)
def test_sparse(self):
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, '#1lab_results')
fname = os.path.join(rdir, 'pastes.csv')
# Dense
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit()
# Sparse
model2 = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
use_sparse=True,
data=data)
result2 = model2.fit()
assert_allclose(result.params, result2.params)
assert_allclose(result.bse, result2.bse)
def test_summary_col():
from statsmodels.iolib.summary2 import summary_col
ids = [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
# hard coded simulated y
# ids = np.asarray(ids)
# np.random.seed(123987)
# y = x + np.array([-1, 0, 1])[ids - 1] + 2 * np.random.randn(len(y))
y = np.array([
1.727, -1.037, 2.904, 3.569, 4.629, 5.736, 6.747, 7.020, 5.624, 10.155,
10.400, 17.164, 17.276, 14.988, 14.453
])
d = {'Y': y, 'X': x, 'IDS': ids}
d = pd.DataFrame(d)
# provide start_params to speed up convergence
sp1 = np.array([-1.26722599, 1.1617587, 0.19547518])
mod1 = MixedLM.from_formula('Y ~ X', d, groups=d['IDS'])
results1 = mod1.fit(start_params=sp1)
sp2 = np.array([3.48416861, 0.55287862, 1.38537901])
mod2 = MixedLM.from_formula('X ~ Y', d, groups=d['IDS'])
results2 = mod2.fit(start_params=sp2)
out = summary_col([results1, results2], stars=True)
s = ('\n=============================\n Y X \n'
'-----------------------------\nGroup Var 0.1955 1.3854 \n'
' (0.6032) (2.7377) \nIntercept -1.2672 3.4842* \n'
' (1.6546) (1.8882) \nX 1.1618*** \n'
' (0.1959) \nY 0.5529***\n'
' (0.2080) \n=============================\n'
'Standard errors in\nparentheses.\n* p<.1, ** p<.05, ***p<.01')
assert_equal(str(out), s)
def test_formulas(self):
np.random.seed(2410)
exog = np.random.normal(size=(300, 4))
exog_re = np.random.normal(size=300)
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = exog_re * np.kron(np.random.normal(size=100),
[1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mod1 = MixedLM(endog, exog, groups, exog_re)
# test the names
assert_(mod1.data.xnames == ["x1", "x2", "x3", "x4"])
assert_(mod1.data.exog_re_names == ["x_re1"])
assert_(mod1.data.exog_re_names_full == ["x_re1 RE"])
rslt1 = mod1.fit()
# Fit with a formula, passing groups as the actual values.
df = pd.DataFrame({"endog": endog})
for k in range(exog.shape[1]):
df["exog%d" % k] = exog[:, k]
df["exog_re"] = exog_re
fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
re_fml = "0 + exog_re"
mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml,
groups=groups)
assert_(mod2.data.xnames == ["exog0", "exog1", "exog2", "exog3"])
assert_(mod2.data.exog_re_names == ["exog_re"])
assert_(mod2.data.exog_re_names_full == ["exog_re RE"])
rslt2 = mod2.fit()
assert_almost_equal(rslt1.params, rslt2.params)
# Fit with a formula, passing groups as the variable name.
df["groups"] = groups
mod3 = MixedLM.from_formula(fml, df, re_formula=re_fml,
groups="groups")
assert_(mod3.data.xnames == ["exog0", "exog1", "exog2", "exog3"])
assert_(mod3.data.exog_re_names == ["exog_re"])
assert_(mod3.data.exog_re_names_full == ["exog_re RE"])
rslt3 = mod3.fit(start_params=rslt2.params)
assert_allclose(rslt1.params, rslt3.params, rtol=1e-4)
# Check default variance structure with non-formula model
# creation, also use different exog_re that produces a zero
# estimated variance parameter.
exog_re = np.ones(len(endog), dtype=np.float64)
mod4 = MixedLM(endog, exog, groups, exog_re)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rslt4 = mod4.fit()
from statsmodels.formula.api import mixedlm
mod5 = mixedlm(fml, df, groups="groups")
assert_(mod5.data.exog_re_names == ["groups"])
assert_(mod5.data.exog_re_names_full == ["groups RE"])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
rslt5 = mod5.fit()
assert_almost_equal(rslt4.params, rslt5.params)
def test_sparse(self):
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'pastes.csv')
# Dense
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula(
"strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit()
# Sparse
model2 = MixedLM.from_formula(
"strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
use_sparse=True,
data=data)
result2 = model2.fit()
assert_allclose(result.params, result2.params)
assert_allclose(result.bse, result2.bse)
def test_summary_col():
from statsmodels.iolib.summary2 import summary_col
ids = [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
# hard coded simulated y
# ids = np.asarray(ids)
# np.random.seed(123987)
# y = x + np.array([-1, 0, 1])[ids - 1] + 2 * np.random.randn(len(y))
y = np.array([
1.727, -1.037, 2.904, 3.569, 4.629, 5.736, 6.747, 7.020, 5.624, 10.155,
10.400, 17.164, 17.276, 14.988, 14.453
])
d = {'Y': y, 'X': x, 'IDS': ids}
d = pd.DataFrame(d)
# provide start_params to speed up convergence
sp1 = np.array([-1.26722599, 1.1617587, 0.19547518])
mod1 = MixedLM.from_formula('Y ~ X', d, groups=d['IDS'])
results1 = mod1.fit(start_params=sp1)
sp2 = np.array([3.48416861, 0.55287862, 1.38537901])
mod2 = MixedLM.from_formula('X ~ Y', d, groups=d['IDS'])
results2 = mod2.fit(start_params=sp2)
out = summary_col([results1, results2], stars=True)
s = ('\n=============================\n Y X \n'
'-----------------------------\nGroup Var 0.1955 1.3854 \n'
' (0.6032) (2.7377) \nIntercept -1.2672 3.4842* \n'
' (1.6546) (1.8882) \nX 1.1618*** \n'
' (0.1959) \nY 0.5529***\n'
' (0.2080) \n=============================\n'
'Standard errors in\nparentheses.\n* p<.1, ** p<.05, ***p<.01')
assert_equal(str(out), s)
def test_sparse(self):
import scipy
v = scipy.__version__.split(".")[1]
v = int(v)
if v < 16:
return
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, "results")
fname = os.path.join(rdir, "pastes.csv")
# Dense
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1", groups="batch", re_formula="1", vc_formula=vcf, data=data)
result = model.fit()
# Sparse
model2 = MixedLM.from_formula(
"strength ~ 1", groups="batch", re_formula="1", vc_formula=vcf, use_sparse=True, data=data
)
result2 = model2.fit()
assert_allclose(result.params, result2.params)
assert_allclose(result.bse, result2.bse)
def calcBetaLme(data_full, gain_full, loss_full, linear_full, quad_full, run_group, thrshd=None):
"""
function to calculate beta parameters.
Input: data from bold file, two list of gain, loss regressor values
dummy variable indicating the groups,
a threshold to idenfity the voxels inside the brain
Output: beta coefficient, the corresponding p-values, the convergence information
"""
T = data_full.shape[-1]
time_by_vox = np.reshape(data_full, (-1, T)).T
beta = np.empty([time_by_vox.shape[1],5])
fml = "bold ~ gain + loss"
for k in np.arange(0,time_by_vox.shape[1]):
## set a threshold to idenfity the voxels inside the brain
if thrshd != None:
if (np.mean(time_by_vox[:,k]) <= thrshd):
beta[k, :] = [0, 0, 0, 0, 0]
else:
dt = pd.DataFrame({'gain':gain_full,'loss':loss_full,'run_group':run_group,
'ldrift':linear_full,'qdrift':quad_full,'bold':time_by_vox[:,k]})
mod_lme = MixedLM.from_formula(fml, dt, groups=dt["run_group"])
lme_result = mod_lme.fit()
beta[k, :] = [lme_result.fe_params["gain"], lme_result.pvalues["gain"],
lme_result.fe_params["loss"], lme_result.pvalues["loss"], lme_result.converged]
else:
dt = pd.DataFrame({'gain':gain_full,'loss':loss_full,'run_group':run_group,
'ldrift':linear_full,'qdrift':quad_full,'bold':time_by_vox[:,k]})
mod_lme = MixedLM.from_formula(fml, dt, groups=dt["run_group"])
lme_result = mod_lme.fit()
beta[k, :] = [lme_result.fe_params["gain"], lme_result.pvalues["gain"],
lme_result.fe_params["loss"], lme_result.pvalues["loss"], lme_result.converged]
return beta
def test_sparse(self):
import scipy
v = scipy.__version__.split(".")[1]
v = int(v)
if v < 16:
return
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'pastes.csv')
# Dense
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit()
# Sparse
model2 = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
use_sparse=True,
data=data)
result2 = model2.fit()
assert_allclose(result.params, result2.params)
assert_allclose(result.bse, result2.bse)
def test_handle_missing():
np.random.seed(23423)
df = np.random.normal(size=(100, 6))
df = pd.DataFrame(df)
df.columns = ["y", "g", "x1", "z1", "c1", "c2"]
df["g"] = np.kron(np.arange(50), np.ones(2))
re = np.random.normal(size=(50, 4))
re = np.kron(re, np.ones((2, 1)))
df["y"] = re[:, 0] + re[:, 1] * df.z1 + re[:, 2] * df.c1
df["y"] += re[:, 3] * df.c2 + np.random.normal(size=100)
df.loc[1, "y"] = np.NaN
df.loc[2, "g"] = np.NaN
df.loc[3, "x1"] = np.NaN
df.loc[4, "z1"] = np.NaN
df.loc[5, "c1"] = np.NaN
df.loc[6, "c2"] = np.NaN
fml = "y ~ x1"
re_formula = "1 + z1"
vc_formula = {"a": "0 + c1", "b": "0 + c2"}
for include_re in False, True:
for include_vc in False, True:
kwargs = {}
dx = df.copy()
va = ["y", "g", "x1"]
if include_re:
kwargs["re_formula"] = re_formula
va.append("z1")
if include_vc:
kwargs["vc_formula"] = vc_formula
va.extend(["c1", "c2"])
dx = dx[va].dropna()
# Some of these models are severely misspecified with
# small n, so produce convergence warnings. Not relevant
# to what we are checking here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Drop missing externally
model1 = MixedLM.from_formula(fml,
groups="g",
data=dx,
**kwargs)
result1 = model1.fit()
# MixeLM handles missing
model2 = MixedLM.from_formula(fml,
groups="g",
data=df,
missing='drop',
**kwargs)
result2 = model2.fit()
assert_allclose(result1.params, result2.params)
assert_allclose(result1.bse, result2.bse)
assert_equal(len(result1.fittedvalues), result1.nobs)
def test_dietox_slopes(self):
# dietox data from geepack using random intercepts
#
# Fit in R using
#
# library(geepack)
# r = lmer(Weight ~ Time + (1 + Time | Pig), data=dietox)
# r = lmer(Weight ~ Time + (1 + Time | Pig), REML=FALSE, data=dietox)
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'dietox.csv')
# REML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
re_formula="1 + Time", data=data)
result = model.fit(method='powell')
# fixef(r)
assert_allclose(result.fe_params, np.r_[15.738650, 6.939014], rtol=1e-5)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse[0:2], np.r_[0.5501253, 0.0798254], rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 6.03745, rtol=1e-3)
# as.numeric(VarCorr(r)[[1]])
assert_allclose(result.cov_re.values.ravel(),
np.r_[19.4934552, 0.2938323, 0.2938323, 0.4160620],
rtol=1e-1)
# logLik(r)
assert_allclose(model.loglike(result.params_object), -2217.047, rtol=1e-5)
# ML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
re_formula="1 + Time", data=data)
result = model.fit(method='powell', reml=False)
# fixef(r)
assert_allclose(result.fe_params, np.r_[15.73863, 6.93902], rtol=1e-5)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse[0:2], np.r_[0.54629282, 0.07926954], rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 6.037441, rtol=1e-3)
# as.numeric(VarCorr(r)[[1]])
assert_allclose(result.cov_re.values.ravel(),
np.r_[19.190922, 0.293568, 0.293568, 0.409695], rtol=1e-2)
# logLik(r)
assert_allclose(model.loglike(result.params_object), -2215.753, rtol=1e-5)
def test_dietox_slopes(self):
# dietox data from geepack using random intercepts
#
# Fit in R using
#
# library(geepack)
# r = lmer(Weight ~ Time + (1 + Time | Pig), data=dietox)
# r = lmer(Weight ~ Time + (1 + Time | Pig), REML=FALSE, data=dietox)
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'dietox.csv')
# REML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
re_formula="1 + Time", data=data)
result = model.fit(method='powell')
# fixef(r)
assert_allclose(result.fe_params, np.r_[15.738650, 6.939014], rtol=1e-5)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse[0:2], np.r_[0.5501253, 0.0798254], rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 6.03745, rtol=1e-3)
# as.numeric(VarCorr(r)[[1]])
assert_allclose(result.cov_re.values.ravel(),
np.r_[19.4934552, 0.2938323, 0.2938323, 0.4160620],
rtol=1e-1)
# logLik(r)
assert_allclose(model.loglike(result.params_object), -2217.047, rtol=1e-5)
# ML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
re_formula="1 + Time", data=data)
result = model.fit(method='powell', reml=False)
# fixef(r)
assert_allclose(result.fe_params, np.r_[15.73863, 6.93902], rtol=1e-5)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse[0:2], np.r_[0.54629282, 0.07926954], rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 6.037441, rtol=1e-3)
# as.numeric(VarCorr(r)[[1]])
assert_allclose(result.cov_re.values.ravel(),
np.r_[19.190922, 0.293568, 0.293568, 0.409695], rtol=1e-2)
# logLik(r)
assert_allclose(model.loglike(result.params_object), -2215.753, rtol=1e-5)
def test_dietox(self):
# dietox data from geepack using random intercepts
#
# Fit in R using
#
# library(geepack)
# rm = lmer(Weight ~ Time + (1 | Pig), data=dietox)
# rm = lmer(Weight ~ Time + (1 | Pig), REML=FALSE, data=dietox)
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'dietox.csv')
# REML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
data=data)
result = model.fit()
# fixef(rm)
assert_allclose(result.fe_params, np.r_[15.723523, 6.942505], rtol=1e-5)
# sqrt(diag(vcov(rm)))
assert_allclose(result.bse[0:2], np.r_[0.78805374, 0.03338727], rtol=1e-5)
# attr(VarCorr(rm), "sc")^2
assert_allclose(result.scale, 11.36692, rtol=1e-5)
# VarCorr(rm)[[1]][[1]]
assert_allclose(result.cov_re, 40.39395, rtol=1e-5)
# logLik(rm)
assert_allclose(model.loglike(result.params_object), -2404.775, rtol=1e-5)
# ML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
data=data)
result = model.fit(reml=False)
# fixef(rm)
assert_allclose(result.fe_params, np.r_[15.723517, 6.942506], rtol=1e-5)
# sqrt(diag(vcov(rm)))
assert_allclose(result.bse[0:2], np.r_[0.7829397, 0.0333661], rtol=1e-5)
# attr(VarCorr(rm), "sc")^2
assert_allclose(result.scale, 11.35251, rtol=1e-5)
# VarCorr(rm)[[1]][[1]]
assert_allclose(result.cov_re, 39.82097, rtol=1e-5)
# logLik(rm)
assert_allclose(model.loglike(result.params_object), -2402.932, rtol=1e-5)
def test_dietox(self):
# dietox data from geepack using random intercepts
#
# Fit in R using
#
# library(geepack)
# rm = lmer(Weight ~ Time + (1 | Pig), data=dietox)
# rm = lmer(Weight ~ Time + (1 | Pig), REML=FALSE, data=dietox)
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'dietox.csv')
# REML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
data=data)
result = model.fit()
# fixef(rm)
assert_allclose(result.fe_params, np.r_[15.723523, 6.942505], rtol=1e-5)
# sqrt(diag(vcov(rm)))
assert_allclose(result.bse[0:2], np.r_[0.78805374, 0.03338727], rtol=1e-5)
# attr(VarCorr(rm), "sc")^2
assert_allclose(result.scale, 11.36692, rtol=1e-5)
# VarCorr(rm)[[1]][[1]]
assert_allclose(result.cov_re, 40.39395, rtol=1e-5)
# logLik(rm)
assert_allclose(model.loglike(result.params_object), -2404.775, rtol=1e-5)
# ML
data = pd.read_csv(fname)
model = MixedLM.from_formula("Weight ~ Time", groups="Pig",
data=data)
result = model.fit(reml=False)
# fixef(rm)
assert_allclose(result.fe_params, np.r_[15.723517, 6.942506], rtol=1e-5)
# sqrt(diag(vcov(rm)))
assert_allclose(result.bse[0:2], np.r_[0.7829397, 0.0333661], rtol=1e-5)
# attr(VarCorr(rm), "sc")^2
assert_allclose(result.scale, 11.35251, rtol=1e-5)
# VarCorr(rm)[[1]][[1]]
assert_allclose(result.cov_re, 39.82097, rtol=1e-5)
# logLik(rm)
assert_allclose(model.loglike(result.params_object), -2402.932, rtol=1e-5)
def test_pastes_vcomp(self):
"""
pastes data from lme4
Fit in R using formula:
strength ~ (1|batch) + (1|batch:cask)
"""
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'pastes.csv')
# REML
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit()
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
assert_allclose(result.bse.iloc[0], 0.6769, rtol=1e-3)
assert_allclose(result.cov_re.iloc[0, 0], 1.657, rtol=1e-3)
assert_allclose(result.scale, 0.678, rtol=1e-3)
assert_allclose(result.llf, -123.49, rtol=1e-1)
assert_equal(result.aic, np.nan) # don't provide aic/bic with REML
assert_equal(result.bic, np.nan)
resid = np.r_[0.17133538, -0.02866462, -1.08662875, 1.11337125,
-0.12093607]
assert_allclose(result.resid[0:5], resid, rtol=1e-3)
fit = np.r_[62.62866, 62.62866, 61.18663, 61.18663, 62.82094]
assert_allclose(result.fittedvalues[0:5], fit, rtol=1e-4)
# ML
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit(reml=False)
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
assert_allclose(result.bse.iloc[0], 0.642, rtol=1e-3)
assert_allclose(result.cov_re.iloc[0, 0], 1.199, rtol=1e-3)
assert_allclose(result.scale, 0.67799, rtol=1e-3)
assert_allclose(result.llf, -123.997, rtol=1e-1)
assert_allclose(result.aic, 255.9944, rtol=1e-3)
assert_allclose(result.bic, 264.3718, rtol=1e-3)
def test_handle_missing():
np.random.seed(23423)
df = np.random.normal(size=(100, 6))
df = pd.DataFrame(df)
df.columns = ["y", "g", "x1", "z1", "c1", "c2"]
df["g"] = np.kron(np.arange(50), np.ones(2))
re = np.random.normal(size=(50, 4))
re = np.kron(re, np.ones((2, 1)))
df["y"] = re[:, 0] + re[:, 1] * df.z1 + re[:, 2] * df.c1
df["y"] += re[:, 3] * df.c2 + np.random.normal(size=100)
df.loc[1, "y"] = np.NaN
df.loc[2, "g"] = np.NaN
df.loc[3, "x1"] = np.NaN
df.loc[4, "z1"] = np.NaN
df.loc[5, "c1"] = np.NaN
df.loc[6, "c2"] = np.NaN
fml = "y ~ x1"
re_formula = "1 + z1"
vc_formula = {"a": "0 + c1", "b": "0 + c2"}
for include_re in False, True:
for include_vc in False, True:
kwargs = {}
dx = df.copy()
va = ["y", "g", "x1"]
if include_re:
kwargs["re_formula"] = re_formula
va.append("z1")
if include_vc:
kwargs["vc_formula"] = vc_formula
va.extend(["c1", "c2"])
dx = dx[va].dropna()
# Some of these models are severely misspecified with
# small n, so produce convergence warnings. Not relevant
# to what we are checking here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Drop missing externally
model1 = MixedLM.from_formula(
fml, groups="g", data=dx, **kwargs)
result1 = model1.fit()
# MixeLM handles missing
model2 = MixedLM.from_formula(
fml, groups="g", data=df, missing='drop', **kwargs)
result2 = model2.fit()
assert_allclose(result1.params, result2.params)
assert_allclose(result1.bse, result2.bse)
assert_equal(len(result1.fittedvalues), result1.nobs)
def test_pastes_vcomp(self):
"""
pastes data from lme4
Fit in R using formula:
strength ~ (1|batch) + (1|batch:cask)
"""
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'pastes.csv')
# REML
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1", groups="batch",
re_formula="1", vc_formula=vcf,
data=data)
result = model.fit()
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
assert_allclose(result.bse.iloc[0], 0.6769, rtol=1e-3)
assert_allclose(result.cov_re.iloc[0, 0], 1.657, rtol=1e-3)
assert_allclose(result.scale, 0.678, rtol=1e-3)
assert_allclose(result.llf, -123.49, rtol=1e-1)
assert_equal(result.aic, np.nan) # don't provide aic/bic with REML
assert_equal(result.bic, np.nan)
resid = np.r_[0.17133538, -0.02866462, -
1.08662875, 1.11337125, -0.12093607]
assert_allclose(result.resid[0:5], resid, rtol=1e-3)
fit = np.r_[62.62866, 62.62866, 61.18663, 61.18663, 62.82094]
assert_allclose(result.fittedvalues[0:5], fit, rtol=1e-4)
# ML
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
model = MixedLM.from_formula("strength ~ 1", groups="batch",
re_formula="1", vc_formula=vcf,
data=data)
result = model.fit(reml=False)
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
assert_allclose(result.bse.iloc[0], 0.642, rtol=1e-3)
assert_allclose(result.cov_re.iloc[0, 0], 1.199, rtol=1e-3)
assert_allclose(result.scale, 0.67799, rtol=1e-3)
assert_allclose(result.llf, -123.997, rtol=1e-1)
assert_allclose(result.aic, 255.9944, rtol=1e-3)
assert_allclose(result.bic, 264.3718, rtol=1e-3)
def test_vcomp_3(self):
# Test a model with vcomp but no other random effects, using formulas.
import scipy
v = scipy.__version__.split(".")[1]
v = int(v)
if v < 16:
return
np.random.seed(4279)
x1 = np.random.normal(size=400)
groups = np.kron(np.arange(100), np.ones(4))
slopes = np.random.normal(size=100)
slopes = np.kron(slopes, np.ones(4)) * x1
y = slopes + np.random.normal(size=400)
vc_fml = {"a": "0 + x1"}
df = pd.DataFrame({"y": y, "x1": x1, "groups": groups})
model = MixedLM.from_formula("y ~ 1", groups="groups", vc_formula=vc_fml, data=df)
result = model.fit()
result.summary()
assert_allclose(result.resid.iloc[0:4], np.r_[-1.180753, 0.279966, 0.578576, -0.667916], rtol=1e-3)
assert_allclose(result.fittedvalues.iloc[0:4], np.r_[-0.101549, 0.028613, -0.224621, -0.126295], rtol=1e-3)
def calcBetaLme(data_full, gain_full, loss_full, linear_full, quad_full,
run_group, thrshd):
"""
function to calculate beta parameters.
Input: data from bold file, two list of gain, loss regressor values
dummy variable indicating the groups,
a threshold to idenfity the voxels inside the brain
Output: beta coefficient, the corresponding p-values, the convergence information
"""
T = data_full.shape[-1]
time_by_vox = np.reshape(data_full, (-1, T)).T
beta = np.empty([time_by_vox.shape[1], 5])
fml = "bold ~ gain + loss"
for k in np.arange(0, time_by_vox.shape[1]):
## set a threshold to idenfity the voxels inside the brain
if (np.mean(time_by_vox[:, k]) <= 400):
beta[k, :] = [0, 0, 0, 0, 0]
else:
dt = pd.DataFrame({
'gain': gain_full,
'loss': loss_full,
'run_group': run_group,
'ldrift': linear_full,
'qdrift': quad_full,
'bold': time_by_vox[:, k]
})
mod_lme = MixedLM.from_formula(fml, dt, groups=dt["run_group"])
lme_result = mod_lme.fit()
beta[k, :] = [
lme_result.fe_params["gain"], lme_result.pvalues["gain"],
lme_result.fe_params["loss"], lme_result.pvalues["loss"],
lme_result.converged
]
return beta
def test_formulas(self):
np.random.seed(2410)
exog = np.random.normal(size=(300, 4))
exog_re = np.random.normal(size=300)
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = exog_re * np.kron(np.random.normal(size=100), [1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mod1 = MixedLM(endog, exog, groups, exog_re)
rslt1 = mod1.fit()
df = pd.DataFrame({"endog": endog})
for k in range(exog.shape[1]):
df["exog%d" % k] = exog[:, k]
df["exog_re"] = exog_re
fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
re_fml = "0 + exog_re"
mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml, groups=groups)
rslt2 = mod2.fit()
assert_almost_equal(rslt1.params, rslt2.params)
# Check default variance structure, with formula.api
exog_re = np.ones(len(endog), dtype=np.float64)
mod3 = MixedLM(endog, exog, groups, exog_re)
rslt3 = mod3.fit()
from statsmodels.formula.api import mixedlm
mod4 = mixedlm(fml, df, groups=groups)
rslt4 = mod4.fit()
assert_almost_equal(rslt3.params, rslt4.params)
def test_vcomp_3(self):
# Test a model with vcomp but no other random effects, using formulas.
import scipy
v = scipy.__version__.split(".")[1]
v = int(v)
if v < 16:
return
np.random.seed(4279)
x1 = np.random.normal(size=400)
groups = np.kron(np.arange(100), np.ones(4))
slopes = np.random.normal(size=100)
slopes = np.kron(slopes, np.ones(4)) * x1
y = slopes + np.random.normal(size=400)
vc_fml = {"a": "0 + x1"}
df = pd.DataFrame({"y": y, "x1": x1, "groups": groups})
model = MixedLM.from_formula("y ~ 1",
groups="groups",
vc_formula=vc_fml,
data=df)
result = model.fit()
result.summary()
assert_allclose(result.resid.iloc[0:4],
np.r_[-1.180753, 0.279966, 0.578576, -0.667916],
rtol=1e-3)
assert_allclose(result.fittedvalues.iloc[0:4],
np.r_[-0.101549, 0.028613, -0.224621, -0.126295],
rtol=1e-3)
def lmemodel(data, metadata,
fixedEffects = ['Tissue of Origin'],
randomEffects=['High Confidence Donor ID (HCDID)']):
"""Performs a mixed effect linear model"""
df = metadata[fixedEffects].copy()
df = pd.concat([df, metadata[randomEffects]], axis=1 )
#Change the parameters to be compatible with patsy formulas
fixedEffects = [c.translate(string.maketrans(' ()', '___')) for c in fixedEffects]
randomEffects = [c.translate(string.maketrans(' ()', '___')) for c in randomEffects]
df.columns = [c.translate(string.maketrans(' ()', '___')) for c in df.columns]
model_string = 'gene ~ '+' + '.join(fixedEffects)
results = []
for i in range(data.shape[0]):
#Add the dependent variable to the dataframe
df['gene'] = data.irow(i)
#################
df['High_Confidence_Donor_ID__HCDID_'] = stats.binom.rvs(1, .4, size=69)
print df.shape, model_string
return df
df = df.dropna()
print df.shape
#################
#compute new model
mod = MixedLM.from_formula(model_string, df, groups = df[randomEffects])
return mod
#df.boxplot(by=fixedEffects)
#mod = sm.ols(model_fit, df)
#results.append(mod.fit())
return results
def test_vcomp_2(self):
# Simulated data comparison to R
np.random.seed(6241)
n = 1600
exog = np.random.normal(size=(n, 2))
groups = np.kron(np.arange(n / 16), np.ones(16))
# Build up the random error vector
errors = 0
# The random effects
exog_re = np.random.normal(size=(n, 2))
slopes = np.random.normal(size=(n // 16, 2))
slopes = np.kron(slopes, np.ones((16, 1))) * exog_re
errors += slopes.sum(1)
# First variance component
subgroups1 = np.kron(np.arange(n / 4), np.ones(4))
errors += np.kron(2 * np.random.normal(size=n // 4), np.ones(4))
# Second variance component
subgroups2 = np.kron(np.arange(n / 2), np.ones(2))
errors += np.kron(2 * np.random.normal(size=n // 2), np.ones(2))
# iid errors
errors += np.random.normal(size=n)
endog = exog.sum(1) + errors
df = pd.DataFrame(index=range(n))
df["y"] = endog
df["groups"] = groups
df["x1"] = exog[:, 0]
df["x2"] = exog[:, 1]
df["z1"] = exog_re[:, 0]
df["z2"] = exog_re[:, 1]
df["v1"] = subgroups1
df["v2"] = subgroups2
# Equivalent model in R:
# df.to_csv("tst.csv")
# model = lmer(y ~ x1 + x2 + (0 + z1 + z2 | groups) + (1 | v1) + (1 |
# v2), df)
vcf = {"a": "0 + C(v1)", "b": "0 + C(v2)"}
model1 = MixedLM.from_formula("y ~ x1 + x2", groups=groups,
re_formula="0+z1+z2",
vc_formula=vcf, data=df)
result1 = model1.fit()
# Compare to R
assert_allclose(result1.fe_params, [
0.16527, 0.99911, 0.96217], rtol=1e-4)
assert_allclose(result1.cov_re, [
[1.244, 0.146], [0.146, 1.371]], rtol=1e-3)
assert_allclose(result1.vcomp, [4.024, 3.997], rtol=1e-3)
assert_allclose(result1.bse.iloc[0:3], [
0.12610, 0.03938, 0.03848], rtol=1e-3)
def test_vcomp_formula(self):
np.random.seed(6241)
n = 800
exog = np.random.normal(size=(n, 2))
exog[:, 0] = 1
ex_vc = []
groups = np.kron(np.arange(n / 4), np.ones(4))
errors = 0
exog_re = np.random.normal(size=(n, 2))
slopes = np.random.normal(size=(n // 4, 2))
slopes = np.kron(slopes, np.ones((4, 1))) * exog_re
errors += slopes.sum(1)
ex_vc = np.random.normal(size=(n, 4))
slopes = np.random.normal(size=(n // 4, 4))
slopes[:, 2:] *= 2
slopes = np.kron(slopes, np.ones((4, 1))) * ex_vc
errors += slopes.sum(1)
errors += np.random.normal(size=n)
endog = exog.sum(1) + errors
exog_vc = {"a": {}, "b": {}}
for k, group in enumerate(range(int(n / 4))):
ix = np.flatnonzero(groups == group)
exog_vc["a"][group] = ex_vc[ix, 0:2]
exog_vc["b"][group] = ex_vc[ix, 2:]
with pytest.warns(UserWarning, match="Using deprecated variance"):
model1 = MixedLM(endog,
exog,
groups,
exog_re=exog_re,
exog_vc=exog_vc)
result1 = model1.fit()
df = pd.DataFrame(exog[:, 1:], columns=["x1"])
df["y"] = endog
df["re1"] = exog_re[:, 0]
df["re2"] = exog_re[:, 1]
df["vc1"] = ex_vc[:, 0]
df["vc2"] = ex_vc[:, 1]
df["vc3"] = ex_vc[:, 2]
df["vc4"] = ex_vc[:, 3]
vc_formula = {"a": "0 + vc1 + vc2", "b": "0 + vc3 + vc4"}
model2 = MixedLM.from_formula("y ~ x1",
groups=groups,
re_formula="0 + re1 + re2",
vc_formula=vc_formula,
data=df)
result2 = model2.fit()
assert_allclose(result1.fe_params, result2.fe_params, rtol=1e-8)
assert_allclose(result1.cov_re, result2.cov_re, rtol=1e-8)
assert_allclose(result1.vcomp, result2.vcomp, rtol=1e-8)
assert_allclose(result1.params, result2.params, rtol=1e-8)
assert_allclose(result1.bse, result2.bse, rtol=1e-8)
def test_formulas(self):
np.random.seed(2410)
exog = np.random.normal(size=(300,4))
exog_re = np.random.normal(size=300)
groups = np.kron(np.arange(100), [1,1,1])
g_errors = exog_re * np.kron(np.random.normal(size=100),
[1,1,1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mod1 = MixedLM(endog, exog, groups, exog_re)
rslt1 = mod1.fit()
# Fit with a formula, passing groups as the actual values.
df = pd.DataFrame({"endog": endog})
for k in range(exog.shape[1]):
df["exog%d" % k] = exog[:,k]
df["exog_re"] = exog_re
fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
re_fml = "0 + exog_re"
mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml,
groups=groups)
rslt2 = mod2.fit()
assert_almost_equal(rslt1.params, rslt2.params)
# Fit with a formula, passing groups as the variable name.
df["groups"] = groups
mod3 = MixedLM.from_formula(fml, df, re_formula=re_fml,
groups="groups")
rslt3 = mod3.fit(start_params=rslt2.params)
assert_allclose(rslt1.params, rslt3.params, rtol=1e-4)
# Check default variance structure with non-formula model
# creation.
exog_re = np.ones(len(endog), dtype=np.float64)
mod4 = MixedLM(endog, exog, groups, exog_re)
rslt4 = mod4.fit(start_params=rslt2.params)
from statsmodels.formula.api import mixedlm
mod5 = mixedlm(fml, df, groups="groups")
rslt5 = mod5.fit(start_params=rslt2.params)
assert_almost_equal(rslt4.params, rslt5.params)
def test_formulas(self):
np.random.seed(2410)
exog = np.random.normal(size=(300, 4))
exog_re = np.random.normal(size=300)
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = exog_re * np.kron(np.random.normal(size=100), [1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
mod1 = MixedLM(endog, exog, groups, exog_re)
rslt1 = mod1.fit()
# Fit with a formula, passing groups as the actual values.
df = pd.DataFrame({"endog": endog})
for k in range(exog.shape[1]):
df["exog%d" % k] = exog[:, k]
df["exog_re"] = exog_re
fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
re_fml = "0 + exog_re"
mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml, groups=groups)
rslt2 = mod2.fit()
assert_almost_equal(rslt1.params, rslt2.params)
# Fit with a formula, passing groups as the variable name.
df["groups"] = groups
mod3 = MixedLM.from_formula(fml,
df,
re_formula=re_fml,
groups="groups")
rslt3 = mod3.fit(start_params=rslt2.params)
assert_almost_equal(rslt1.params, rslt3.params, decimal=5)
# Check default variance structure with formula.api
exog_re = np.ones(len(endog), dtype=np.float64)
mod4 = MixedLM(endog, exog, groups, exog_re)
rslt4 = mod4.fit(start_params=rslt2.params)
from statsmodels.formula.api import mixedlm
mod5 = mixedlm(fml, df, groups="groups")
rslt5 = mod5.fit(start_params=rslt2.params)
assert_almost_equal(rslt4.params, rslt5.params)
def test_mixed_lm_wrapper():
# a bit more complicated model to test
np.random.seed(2410)
exog = np.random.normal(size=(300, 4))
exog_re = np.random.normal(size=300)
groups = np.kron(np.arange(100), [1, 1, 1])
g_errors = exog_re * np.kron(np.random.normal(size=100),
[1, 1, 1])
endog = exog.sum(1) + g_errors + np.random.normal(size=300)
# Fit with a formula, passing groups as the actual values.
df = pd.DataFrame({"endog": endog})
for k in range(exog.shape[1]):
df["exog%d" % k] = exog[:, k]
df["exog_re"] = exog_re
fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
re_fml = "~ exog_re"
mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml,
groups=groups)
result = mod2.fit()
result.summary()
xnames = ["exog0", "exog1", "exog2", "exog3"]
re_names = ["Intercept", "exog_re"]
re_names_full = ["Intercept RE", "Intercept RE x exog_re RE",
"exog_re RE"]
assert_(mod2.data.xnames == xnames)
assert_(mod2.data.exog_re_names == re_names)
assert_(mod2.data.exog_re_names_full == re_names_full)
params = result.params
assert_(params.index.tolist() == xnames + re_names_full)
bse = result.bse
assert_(bse.index.tolist() == xnames + re_names_full)
tvalues = result.tvalues
assert_(tvalues.index.tolist() == xnames + re_names_full)
cov_params = result.cov_params()
assert_(cov_params.index.tolist() == xnames + re_names_full)
assert_(cov_params.columns.tolist() == xnames + re_names_full)
fe = result.fe_params
assert_(fe.index.tolist() == xnames)
bse_fe = result.bse_fe
assert_(bse_fe.index.tolist() == xnames)
cov_re = result.cov_re
assert_(cov_re.index.tolist() == re_names)
assert_(cov_re.columns.tolist() == re_names)
cov_re_u = result.cov_re_unscaled
assert_(cov_re_u.index.tolist() == re_names)
assert_(cov_re_u.columns.tolist() == re_names)
bse_re = result.bse_re
assert_(bse_re.index.tolist() == re_names_full)
def test_vcomp_formula(self):
np.random.seed(6241)
n = 800
exog = np.random.normal(size=(n, 2))
exog[:, 0] = 1
ex_vc = []
groups = np.kron(np.arange(n / 4), np.ones(4))
errors = 0
exog_re = np.random.normal(size=(n, 2))
slopes = np.random.normal(size=(n // 4, 2))
slopes = np.kron(slopes, np.ones((4, 1))) * exog_re
errors += slopes.sum(1)
ex_vc = np.random.normal(size=(n, 4))
slopes = np.random.normal(size=(n // 4, 4))
slopes[:, 2:] *= 2
slopes = np.kron(slopes, np.ones((4, 1))) * ex_vc
errors += slopes.sum(1)
errors += np.random.normal(size=n)
endog = exog.sum(1) + errors
exog_vc = {"a": {}, "b": {}}
for k, group in enumerate(range(int(n / 4))):
ix = np.flatnonzero(groups == group)
exog_vc["a"][group] = ex_vc[ix, 0:2]
exog_vc["b"][group] = ex_vc[ix, 2:]
model1 = MixedLM(endog, exog, groups, exog_re=exog_re, exog_vc=exog_vc)
result1 = model1.fit()
df = pd.DataFrame(exog[:, 1:], columns=["x1"])
df["y"] = endog
df["re1"] = exog_re[:, 0]
df["re2"] = exog_re[:, 1]
df["vc1"] = ex_vc[:, 0]
df["vc2"] = ex_vc[:, 1]
df["vc3"] = ex_vc[:, 2]
df["vc4"] = ex_vc[:, 3]
vc_formula = {"a": "0 + vc1 + vc2", "b": "0 + vc3 + vc4"}
model2 = MixedLM.from_formula(
"y ~ x1",
groups=groups,
re_formula="0 + re1 + re2",
vc_formula=vc_formula,
data=df)
result2 = model2.fit()
assert_allclose(result1.fe_params, result2.fe_params, rtol=1e-8)
assert_allclose(result1.cov_re, result2.cov_re, rtol=1e-8)
assert_allclose(result1.vcomp, result2.vcomp, rtol=1e-8)
assert_allclose(result1.params, result2.params, rtol=1e-8)
assert_allclose(result1.bse, result2.bse, rtol=1e-8)
def mass_uv_mixedlmm(formula, data, uv_data, group_id, re_formula=None):
mods = []
for d_idx in range(uv_data.shape[1]):
print("{} of {}".format(d_idx, uv_data.shape[1]), end="\r")
data_temp = data.copy()
data_temp["Brain"] = uv_data[:, d_idx]
model = MixedLM.from_formula(formula, data_temp, groups=group_id)
try:
mod_fit = model.fit()
except:
mods.append(None)
continue
mods.append(mod_fit)
return mods
def mass_uv_mixedlmm(formula, data, uv_data, group_id, re_formula=None):
mods = [[] for source_idx in range(uv_data.shape[1])]
for source_idx in range(uv_data.shape[1]):
for dest_idx in range(uv_data.shape[2]):
if all(uv_data[:, source_idx, dest_idx] == 0):
mods[source_idx].append(None)
continue
#print("Source {}, Destination {}".format(source_idx, dest_idx), end="\r")
print("Source {}, Destination {}".format(source_idx, dest_idx))
data_temp = data.copy()
data_temp["Brain"] = uv_data[:, source_idx, dest_idx]
model = MixedLM.from_formula(formula, data_temp, groups=group_id)
mod_fit = model.fit()
mods[source_idx].append(mod_fit)
return mods
def mass_uv_mixedlmm(formula, data, uv_data, group_id, re_formula=None, exclude=[]):
tvals = []
coeffs = []
for d_idx in range(uv_data.shape[1]):
if d_idx in exclude:
tvals.append(0)
coeffs.append(0)
continue
data_temp = data.copy()
data_temp["Brain"] = uv_data[:,d_idx]
model = MixedLM.from_formula(formula, data_temp, groups=group_id)
mod_fit = model.fit()
tvals.append(mod_fit.tvalues.get(indep_var))
coeffs.append(mod_fit.params.get(indep_var))
tvals, coeffs = np.array(tvals), np.array(coeffs)
return tvals, coeffs
def test_singular():
# Issue #7051
np.random.seed(3423)
n = 100
data = np.random.randn(n, 2)
df = pd.DataFrame(data, columns=['Y', 'X'])
df['class'] = pd.Series([i % 3 for i in df.index], index=df.index)
with pytest.warns(Warning) as wrn:
md = MixedLM.from_formula("Y ~ X", df, groups=df['class'])
mdf = md.fit()
mdf.summary()
if not wrn:
pytest.fail("warning expected")
def fit_func(rdf):
md = MixedLM.from_formula("supply_hours ~ 1 + delta_weeks",
groups='block_dow',
re_formula='1 + delta_weeks',
data=rdf.fillna({'supply_hours': 0.}))
mdf = md.fit()
index = mdf.random_effects.keys()
data = {
'supply_hours': (mdf.params['Intercept'] +
[mdf.random_effects[i]['Intercept'] for i in index]),
'block_dow':
index
}
result = pd.DataFrame(data).set_index('block_dow')
return result
def lmemodel(data,
metadata,
fixedEffects=['Tissue of Origin'],
randomEffects=['High Confidence Donor ID (HCDID)']):
"""Performs a mixed effect linear model"""
df = metadata[fixedEffects].copy()
df = pd.concat([df, metadata[randomEffects]], axis=1)
#Change the parameters to be compatible with patsy formulas
fixedEffects = [
c.translate(string.maketrans(' ()', '___')) for c in fixedEffects
]
randomEffects = [
c.translate(string.maketrans(' ()', '___')) for c in randomEffects
]
df.columns = [
c.translate(string.maketrans(' ()', '___')) for c in df.columns
]
model_string = 'gene ~ ' + ' + '.join(fixedEffects)
results = []
for i in range(data.shape[0]):
#Add the dependent variable to the dataframe
df['gene'] = data.irow(i)
#################
df['High_Confidence_Donor_ID__HCDID_'] = stats.binom.rvs(1,
.4,
size=69)
print df.shape, model_string
return df
df = df.dropna()
print df.shape
#################
#compute new model
mod = MixedLM.from_formula(model_string, df, groups=df[randomEffects])
return mod
#df.boxplot(by=fixedEffects)
#mod = sm.ols(model_fit, df)
#results.append(mod.fit())
return results
"""
try:
from statsmodels.regression.mixed_linear_model import MixedLM
raw_df = se_df.copy()
9b50c5aedf52 · D1691991
raw_df['delta_weeks'] = (pd.to_datetime(raw_df['week_of']) - pd.to_datetime(recommendation_week)).dt.days / 7
17a83f0a52b1 · D1438409
def fit_func(group):
ea0c134be68e · D1540393
try:
md = MixedLM.from_formula("{} ~ 1 + delta_weeks".format(metric),
groups='block_dow',
re_formula='1 + delta_weeks',
data=group.fillna({metric: 0.})
)
mdf = md.fit()
index = mdf.random_effects.keys()
data = {
metric: (mdf.params['Intercept'] + [mdf.random_effects[i]['Intercept'] for i in index]),
'block_dow': index,
}
return pd.DataFrame(data).set_index('block_dow')
except np.linalg.linalg.LinAlgError as err:
logging.warning(err)
dca42ed79c75 · D2335295
cnx_col_inds = list(np.where(cnx_masks[ROI_idx,])[0])
for col_idx in cnx_col_inds:
this_point = this_epo[ROI_idx,col_idx].copy()
outname = label_names[col_idx]
outhemi = "lh" if "lh" in outname else "rh"
data_dict["Brain"].append(this_point)
data_dict["Subj"].append(sub)
data_dict["Block"].append(cond)
data_dict["ROI"].append(ROI)
data_dict["OutRegion"].append(outname)
data_dict["Hemi"].append(outhemi)
data_dict["RT"].append(epo.metadata["RT"].iloc[epo_idx])
group_id.append(sub_idx)
dm = pd.DataFrame.from_dict(data_dict)
group_id = np.array(group_id)
formula = "RT ~ Brain*Block + Brain*Block*C(ROI, Treatment('L3969-lh'))"
formula = "Brain ~ RT*Block"
mfs = []
for ROI in ROIs:
this_dm = dm.copy()
this_dm = this_dm[this_dm["ROI"]==ROI]
this_group_id = group_id[(dm["ROI"]==ROI)]
mod = MixedLM.from_formula(formula, this_dm, groups=this_group_id)
mfs.append(mod.fit(reml=False))
formula = "RT ~ Block"
mod_rt = MixedLM.from_formula(formula, dm, groups=group_id)
mf_rt = mod_rt.fit()
def test_get_distribution():
np.random.seed(234)
n = 100
n_groups = 10
fe_params = np.r_[1, -2]
cov_re = np.asarray([[1, 0.5], [0.5, 2]])
vcomp = np.r_[0.5**2, 1.5**2]
scale = 1.5
exog_fe = np.random.normal(size=(n, 2))
exog_re = np.random.normal(size=(n, 2))
exog_vca = np.random.normal(size=(n, 2))
exog_vcb = np.random.normal(size=(n, 2))
groups = np.repeat(np.arange(n_groups, dtype=np.int), n / n_groups)
ey = np.dot(exog_fe, fe_params)
u = np.random.normal(size=(n_groups, 2))
u = np.dot(u, np.linalg.cholesky(cov_re).T)
u1 = np.sqrt(vcomp[0]) * np.random.normal(size=(n_groups, 2))
u2 = np.sqrt(vcomp[1]) * np.random.normal(size=(n_groups, 2))
y = ey + (u[groups, :] * exog_re).sum(1)
y += (u1[groups, :] * exog_vca).sum(1)
y += (u2[groups, :] * exog_vcb).sum(1)
y += np.sqrt(scale) * np.random.normal(size=n)
df = pd.DataFrame({
"y": y,
"x1": exog_fe[:, 0],
"x2": exog_fe[:, 1],
"z0": exog_re[:, 0],
"z1": exog_re[:, 1],
"grp": groups
})
df["z2"] = exog_vca[:, 0]
df["z3"] = exog_vca[:, 1]
df["z4"] = exog_vcb[:, 0]
df["z5"] = exog_vcb[:, 1]
vcf = {"a": "0 + z2 + z3", "b": "0 + z4 + z5"}
m = MixedLM.from_formula("y ~ 0 + x1 + x2",
groups="grp",
re_formula="0 + z0 + z1",
vc_formula=vcf,
data=df)
# Build a params vector that is comparable to
# MixedLMResults.params
import statsmodels
mp = statsmodels.regression.mixed_linear_model.MixedLMParams
po = mp.from_components(fe_params=fe_params, cov_re=cov_re, vcomp=vcomp)
pa = po.get_packed(has_fe=True, use_sqrt=False)
pa[len(fe_params):] /= scale
# Get a realization
dist = m.get_distribution(pa, scale, None)
yr = dist.rvs(0)
# Check the overall variance
v = (np.dot(exog_re, cov_re) * exog_re).sum(1).mean()
v += vcomp[0] * (exog_vca**2).sum(1).mean()
v += vcomp[1] * (exog_vcb**2).sum(1).mean()
v += scale
assert_allclose(np.var(yr - ey), v, rtol=1e-2, atol=1e-4)
def test_pastes_vcomp(self):
# pastes data from lme4
#
# Fit in R using:
#
# r = lmer(strength ~ (1|batch) + (1|batch:cask), data=data)
# r = lmer(strength ~ (1|batch) + (1|batch:cask), data=data,
# reml=FALSE)
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'pastes.csv')
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
# REML
model = MixedLM.from_formula(
"strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit()
# fixef(r)
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse.iloc[0], 0.6769, rtol=1e-3)
# VarCorr(r)$batch[[1]]
assert_allclose(result.cov_re.iloc[0, 0], 1.657, rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 0.678, rtol=1e-3)
# logLik(r)
assert_allclose(result.llf, -123.49, rtol=1e-1)
# don't provide aic/bic with REML
assert_equal(result.aic, np.nan)
assert_equal(result.bic, np.nan)
# resid(r)[1:5]
resid = np.r_[0.17133538, -0.02866462, -1.08662875, 1.11337125,
-0.12093607]
assert_allclose(result.resid[0:5], resid, rtol=1e-3)
# predict(r)[1:5]
fit = np.r_[62.62866, 62.62866, 61.18663, 61.18663, 62.82094]
assert_allclose(result.fittedvalues[0:5], fit, rtol=1e-4)
# ML
model = MixedLM.from_formula(
"strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit(reml=False)
# fixef(r)
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse.iloc[0], 0.642, rtol=1e-3)
# VarCorr(r)$batch[[1]]
assert_allclose(result.cov_re.iloc[0, 0], 1.199, rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 0.67799, rtol=1e-3)
# logLik(r)
assert_allclose(result.llf, -123.997, rtol=1e-1)
# AIC(r)
assert_allclose(result.aic, 255.9944, rtol=1e-3)
# BIC(r)
assert_allclose(result.bic, 264.3718, rtol=1e-3)
# How to estimate multilevel GLM in statsmodels package
# We only show example for gaussian model, because gamma model is not implemented in the package
# see: https://www.statsmodels.org/devel/mixed_glm.html
from pandas import read_csv
from statsmodels.regression.mixed_linear_model import MixedLM
if __name__ == '__main__':
# Requires to set the working directory to the project directory
gaussian_data = read_csv("./data/Gaussian_identity_data.csv")
model = MixedLM.from_formula("y ~ x1 + x2 + x3", data=gaussian_data, groups=gaussian_data["group_index"])
model_result = model.fit()
# Fixed effects
print(model_result.summary())
# Random effects
print(model_result.random_effects)
# Dispersion parameter
print(model_result.scale)
def test_random_effects_getters():
# Simulation-based test to make sure that the BLUPs and actual
# random effects line up.
np.random.seed(34234)
ng = 500 # number of groups
m = 10 # group size
y, x, z, v0, v1, g, b, c0, c1 = [], [], [], [], [], [], [], [], []
for i in range(ng):
# Fixed effects
xx = np.random.normal(size=(m, 2))
yy = xx[:, 0] + 0.5 * np.random.normal(size=m)
# Random effects (re_formula)
zz = np.random.normal(size=(m, 2))
bb = np.random.normal(size=2)
bb[0] *= 3
bb[1] *= 1
yy += np.dot(zz, bb).flat
b.append(bb)
# First variance component
vv0 = np.kron(np.r_[0, 1], np.ones(m // 2)).astype(np.int)
cc0 = np.random.normal(size=2)
yy += cc0[vv0]
v0.append(vv0)
c0.append(cc0)
# Second variance component
vv1 = np.kron(np.ones(m // 2), np.r_[0, 1]).astype(np.int)
cc1 = np.random.normal(size=2)
yy += cc1[vv1]
v1.append(vv1)
c1.append(cc1)
y.append(yy)
x.append(xx)
z.append(zz)
g.append(["g%d" % i] * m)
y = np.concatenate(y)
x = np.concatenate(x)
z = np.concatenate(z)
v0 = np.concatenate(v0)
v1 = np.concatenate(v1)
g = np.concatenate(g)
df = pd.DataFrame({
"y": y,
"x0": x[:, 0],
"x1": x[:, 1],
"z0": z[:, 0],
"z1": z[:, 1],
"v0": v0,
"v1": v1,
"g": g
})
b = np.asarray(b)
c0 = np.asarray(c0)
c1 = np.asarray(c1)
cc = np.concatenate((c0, c1), axis=1)
model = MixedLM.from_formula("y ~ x0 + x1",
re_formula="~0 + z0 + z1",
vc_formula={
"v0": "~0+C(v0)",
"v1": "0+C(v1)"
},
groups="g",
data=df)
result = model.fit()
ref = result.random_effects
b0 = [ref["g%d" % k][0:2] for k in range(ng)]
b0 = np.asarray(b0)
assert (np.corrcoef(b0[:, 0], b[:, 0])[0, 1] > 0.8)
assert (np.corrcoef(b0[:, 1], b[:, 1])[0, 1] > 0.8)
cf0 = [ref["g%d" % k][2:6] for k in range(ng)]
cf0 = np.asarray(cf0)
for k in range(4):
assert (np.corrcoef(cf0[:, k], cc[:, k])[0, 1] > 0.8)
# Smoke test for predictive covariances
refc = result.random_effects_cov
for g in refc.keys():
p = ref[g].size
assert (refc[g].shape == (p, p))
def big_ass_matrix(df, y, x, group = None, short = True) :
independent = combinatorial(x, short)
models = {}
p = {}
aic = {}
r2 = {}
best = {}
dfs = {}
bestdf = {}
for dependent in y :
print "Regressing for %s" % dependent
for covariate in independent :
if group is None :
subset = delayer([covariate, dependent])
df2 = df[subset].dropna()
df2["Intercept"] = np.ones(len(df2))
dfs.setdefault(dependent, []).append(df2)
ols = sm.GLS(endog=df2[dependent], exog=df2[delayer([covariate, "Intercept"])]).fit()
models.setdefault(dependent, []).append(ols)
p.setdefault(dependent, []).append(ols.pvalues[:-1].values)
aic.setdefault(dependent, []).append(ols.aic)
r2.setdefault(dependent, []).append(ols.rsquared)
else :
subset = delayer([covariate, dependent, group])
df2 = df[subset].dropna()
dfs.setdefault(dependent, []).append(df2)
ols = MixedLM.from_formula(rstr(y=dependent, x=covariate), data=df2, groups=df2[group]).fit()
models.setdefault(dependent, []).append(ols)
aic.setdefault(dependent, []).append(2 * (ols.k_fe + 1) - 2 * ols.llf)
p.setdefault(dependent, []).append(ols.pvalues[1:-1].values)
r2.setdefault(dependent, []).append(mmR2(df2, ols))
bestAIC = np.min(aic[dependent])
for i, val in enumerate(models[dependent]) :
if aic[dependent][i] < 2 + bestAIC :
if np.sum(p[dependent][i] > 0.05) == 0 :
if group is None :
best.setdefault(dependent, []).append(val)
bestdf.setdefault(dependent, []).append(dfs[dependent][i])
else :
if val.random_effects.abs().mean()[0] > 0.01 :
best.setdefault(dependent, []).append(val)
bestdf.setdefault(dependent, []).append(dfs[dependent][i])
if best.has_key(dependent) :
for i, model in enumerate(best[dependent]) :
if not os.path.exists("regressions/%s" % dependent) :
os.mkdir("regressions/%s" % dependent)
if not os.path.exists("../talk/figures/regressions/%s" % dependent) :
os.mkdir("../talk/figures/regressions/%s" % dependent)
if group is None :
dfx = bestdf[dependent][i]
plt.scatter(model.fittedvalues.values, dfx[model.model.endog_names].values, c=seaborn.color_palette("deep", 8)[0])
plt.plot(dfx[model.model.endog_names].values, dfx[model.model.endog_names].values, c=seaborn.color_palette("deep", 8)[2])
plt.ylabel(model.model.endog_names)
yl = model.model.exog_names[:]
yl.remove("Intercept")
plt.xlabel("Estimate using " + ", ".join(yl))
plt.title(rstr(dependent, model.model.exog_names).replace(" + Intercept", ""))
#plt.title(r"$R^2$ = %.02f" % model.rsquared)
st = ("$R^2$ = %.03f\n\n"% model.rsquared)
for coefnum, coef in enumerate(yl) :
st += ("%s" % coef)
st += (" : %.03f\n" % model.params[coef])
st += ("$p$ = %.01e\n\n" % model.pvalues[coefnum])
#plt.suptitle(st)
plt.text(0.01, .99, st, va="top", ha="left")
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.savefig("regressions/%s/lm-%d.pdf" % (dependent, i))
plt.savefig("../talk/figures/regressions/%s/lm-%d.png" % (dependent, i), dpi=300, jpeg_quality=90)
plt.close()
else :
dfx = bestdf[dependent][i]
y, yhat = mmPredict(model.model.data.frame, model)
plt.scatter(yhat, y, c=seaborn.color_palette("deep", 8)[0])
plt.plot(y, y, c=seaborn.color_palette("deep", 8)[2])
plt.ylabel(model.model.endog_names)
yl = model.model.exog_names[:]
yl.remove("Intercept")
plt.xlabel("Estimate using " + ", ".join(yl))
plt.title(rstr(dependent, model.model.exog_names).replace("Intercept + ", ""))
#plt.title(r"$R^2$ = %.02f" % mmR2(dfx, model))
st = ("$R^2$ = %.03f\n\n" % mmR2(dfx, model))
for coefnum, coef in enumerate(yl) :
st += coef
st += " : %.03f\n" % model.fe_params[1+coefnum]
st += "$p$ = %.01e\n\n" % model.pvalues[coef]
st += ("Avg. abs. RE coef. : %.03f" % model.random_effects.abs().mean())
plt.text(0.01, .99, st, va="top", ha="left")
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.savefig("regressions/%s/mm_%d.pdf" % (dependent, i))
plt.savefig("../talk/figures/regressions/%s/mm_%d.png" % (dependent, i), dpi=300, jpeg_quality=90)
plt.close()
return best, (models, p, r2, aic)
def test_pastes_vcomp(self):
# pastes data from lme4
#
# Fit in R using:
#
# r = lmer(strength ~ (1|batch) + (1|batch:cask), data=data)
# r = lmer(strength ~ (1|batch) + (1|batch:cask), data=data,
# reml=FALSE)
cur_dir = os.path.dirname(os.path.abspath(__file__))
rdir = os.path.join(cur_dir, 'results')
fname = os.path.join(rdir, 'pastes.csv')
data = pd.read_csv(fname)
vcf = {"cask": "0 + cask"}
# REML
model = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit()
# fixef(r)
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse.iloc[0], 0.6769, rtol=1e-3)
# VarCorr(r)$batch[[1]]
assert_allclose(result.cov_re.iloc[0, 0], 1.657, rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 0.678, rtol=1e-3)
# logLik(r)
assert_allclose(result.llf, -123.49, rtol=1e-1)
# do not provide aic/bic with REML
assert_equal(result.aic, np.nan)
assert_equal(result.bic, np.nan)
# resid(r)[1:5]
resid = np.r_[0.17133538, -0.02866462, -1.08662875, 1.11337125,
-0.12093607]
assert_allclose(result.resid[0:5], resid, rtol=1e-3)
# predict(r)[1:5]
fit = np.r_[62.62866, 62.62866, 61.18663, 61.18663, 62.82094]
assert_allclose(result.fittedvalues[0:5], fit, rtol=1e-4)
# ML
model = MixedLM.from_formula("strength ~ 1",
groups="batch",
re_formula="1",
vc_formula=vcf,
data=data)
result = model.fit(reml=False)
# fixef(r)
assert_allclose(result.fe_params.iloc[0], 60.0533, rtol=1e-3)
# sqrt(diag(vcov(r)))
assert_allclose(result.bse.iloc[0], 0.642, rtol=1e-3)
# VarCorr(r)$batch[[1]]
assert_allclose(result.cov_re.iloc[0, 0], 1.199, rtol=1e-3)
# attr(VarCorr(r), "sc")^2
assert_allclose(result.scale, 0.67799, rtol=1e-3)
# logLik(r)
assert_allclose(result.llf, -123.997, rtol=1e-1)
# AIC(r)
assert_allclose(result.aic, 255.9944, rtol=1e-3)
# BIC(r)
assert_allclose(result.bic, 264.3718, rtol=1e-3)
continue
subj, block, trial = match.group(1), match.group(2), match.group(3)
print(filename)
stc = mne.read_source_estimate("{}/stcs/{}".format(proc_dir,filename))
stc = morphs["ATT_"+subj].apply(stc)
ev_str = "Subj=='ATT_{}' and Block=='{}' and TrialIdx=={}".format(subj, block, int(trial))
row_idx = np.where(np.array(df.eval(ev_str)))[0]
temp_data = mne.extract_label_time_course(stc,labels,fs_src,mode="mean")
temp_data = temp_data.mean(axis=1)
for lab_idx, ln in enumerate(stats_label_names):
df.at[row_idx,ln] = temp_data[lab_idx]
df = df.astype({ln:np.float64 for ln in stats_label_names})
for ln_idx, ln in enumerate(stats_label_names):
formula = "{} ~ 1".format(ln)
model = MixedLM.from_formula(formula, df, groups=df["Subj"])
mod_fit = model.fit(reml=False)
mod_fit.save("{}{}/null_reg70_lmm_byresp_{}.pickle".format(lmm_dir,band,ln_idx))
formula = "{} ~ RT + Block".format(ln)
re_formula = "1 + RT"
model = MixedLM.from_formula(formula, df, groups=df["Subj"],
re_formula=re_formula)
mod_fit = model.fit(reml=False)
mod_fit.save("{}{}/simple_reg70_lmm_byresp_{}.pickle".format(lmm_dir,band,ln_idx))
formula = "{} ~ RT*C(Block, Treatment('audio'))".format(ln)
re_formula = "1 + RT"
model = MixedLM.from_formula(formula, df, groups=df["Subj"],
re_formula=re_formula)
mod_fit = model.fit(reml=False)
#sel_inds = (df["Block"]==cond) & (df["Subj"]==sub)
dPTE_slices = {}
for k, v in pairs_info.items():
temp_inds = list(zip(*v["inds"]))
dPTE_slices[k] = dPTE[:, temp_inds[0],
temp_inds[1]].mean(axis=1)
if avg_trials:
dm = dm.append(df[sel_inds])
#dm = dm.append({"Subj":sub,"Block":cond},ignore_index=True)
for k, v in dPTE_slices.items():
data[k].append(v.mean())
group_id.append(sub_idx)
else:
for epo_idx in range(len(dPTE)):
dm = dm.append(df[sel_inds])
#dm = dm.append({"Subj":sub,"Block":cond},ignore_index=True)
for k, v in dPTE_slices.items():
data[k].append(v[epo_idx, ])
group_id.append(sub_idx)
formula = "Brain ~ Laut + Angenehm + C(Block, Treatment('audio')) + Wav"
#formula = "Brain ~ C(Block, Treatment('rest'))"
mod_fits = {}
for k, v in data.items():
dm_temp = dm.copy()
dm_temp["Brain"] = v
model = MixedLM.from_formula(formula, dm_temp, groups=group_id)
mod_fits[k] = model.fit()
def test_random_effects_getters():
# Simulation-based test to make sure that the BLUPs and actual
# random effects line up.
np.random.seed(34234)
ng = 500 # number of groups
m = 10 # group size
y, x, z, v0, v1, g, b, c0, c1 = [], [], [], [], [], [], [], [], []
for i in range(ng):
# Fixed effects
xx = np.random.normal(size=(m, 2))
yy = xx[:, 0] + 0.5 * np.random.normal(size=m)
# Random effects (re_formula)
zz = np.random.normal(size=(m, 2))
bb = np.random.normal(size=2)
bb[0] *= 3
bb[1] *= 1
yy += np.dot(zz, bb).flat
b.append(bb)
# First variance component
vv0 = np.kron(np.r_[0, 1], np.ones(m // 2)).astype(np.int)
cc0 = np.random.normal(size=2)
yy += cc0[vv0]
v0.append(vv0)
c0.append(cc0)
# Second variance component
vv1 = np.kron(np.ones(m // 2), np.r_[0, 1]).astype(np.int)
cc1 = np.random.normal(size=2)
yy += cc1[vv1]
v1.append(vv1)
c1.append(cc1)
y.append(yy)
x.append(xx)
z.append(zz)
g.append(["g%d" % i] * m)
y = np.concatenate(y)
x = np.concatenate(x)
z = np.concatenate(z)
v0 = np.concatenate(v0)
v1 = np.concatenate(v1)
g = np.concatenate(g)
df = pd.DataFrame({
"y": y,
"x0": x[:, 0],
"x1": x[:, 1],
"z0": z[:, 0],
"z1": z[:, 1],
"v0": v0,
"v1": v1,
"g": g
})
b = np.asarray(b)
c0 = np.asarray(c0)
c1 = np.asarray(c1)
cc = np.concatenate((c0, c1), axis=1)
model = MixedLM.from_formula(
"y ~ x0 + x1",
re_formula="~0 + z0 + z1",
vc_formula={
"v0": "~0+C(v0)",
"v1": "0+C(v1)"
},
groups="g",
data=df)
result = model.fit()
ref = result.random_effects
b0 = [ref["g%d" % k][0:2] for k in range(ng)]
b0 = np.asarray(b0)
assert (np.corrcoef(b0[:, 0], b[:, 0])[0, 1] > 0.8)
assert (np.corrcoef(b0[:, 1], b[:, 1])[0, 1] > 0.8)
cf0 = [ref["g%d" % k][2:6] for k in range(ng)]
cf0 = np.asarray(cf0)
for k in range(4):
assert (np.corrcoef(cf0[:, k], cc[:, k])[0, 1] > 0.8)
# Smoke test for predictive covariances
refc = result.random_effects_cov
for g in refc.keys():
p = ref[g].size
assert (refc[g].shape == (p, p))
axes = [ax for sublist in axes for ax in sublist]
for block_idx,block in enumerate(blocks):
angs = []
for wav in wavs:
angs.append(df_ang.loc[df_laut["Wav"]==wav]["Angenehm"][df_laut["Block"]==block].values)
angs = np.array(angs)
angs_mean = np.mean(angs,axis=1)
print(angs_mean)
sem = stats.sem(angs,axis=1)
plt.sca(axes[block_idx])
plt.bar(np.arange(len(wavs)),angs_mean,yerr=sem,tick_label=wavs)
plt.title(block)
groups = df_laut["Subj"]
formula = "Laut ~ Block*Wav"
laut_model = MixedLM.from_formula(formula, df_laut, groups=groups)
laut_mf = laut_model.fit()
print(laut_mf.summary())
groups = df_ang["Subj"]
formula = "Angenehm ~ Block*Wav"
ang_model = MixedLM.from_formula(formula, df_ang, groups=groups)
ang_mf = ang_model.fit()
print(ang_mf.summary())
font = {'weight' : 'bold',
'size' : 38}
matplotlib.rc('font', **font)
fig, axes = plt.subplots(1, 2, figsize=(38.4, 21.6))
angs_block = []
def test_get_distribution():
np.random.seed(234)
n = 100
n_groups = 10
fe_params = np.r_[1, -2]
cov_re = np.asarray([[1, 0.5], [0.5, 2]])
vcomp = np.r_[0.5**2, 1.5**2]
scale = 1.5
exog_fe = np.random.normal(size=(n, 2))
exog_re = np.random.normal(size=(n, 2))
exog_vca = np.random.normal(size=(n, 2))
exog_vcb = np.random.normal(size=(n, 2))
groups = np.repeat(np.arange(n_groups, dtype=np.int),
n / n_groups)
ey = np.dot(exog_fe, fe_params)
u = np.random.normal(size=(n_groups, 2))
u = np.dot(u, np.linalg.cholesky(cov_re).T)
u1 = np.sqrt(vcomp[0]) * np.random.normal(size=(n_groups, 2))
u2 = np.sqrt(vcomp[1]) * np.random.normal(size=(n_groups, 2))
y = ey + (u[groups, :] * exog_re).sum(1)
y += (u1[groups, :] * exog_vca).sum(1)
y += (u2[groups, :] * exog_vcb).sum(1)
y += np.sqrt(scale) * np.random.normal(size=n)
df = pd.DataFrame({"y": y, "x1": exog_fe[:, 0], "x2": exog_fe[:, 1],
"z0": exog_re[:, 0], "z1": exog_re[:, 1],
"grp": groups})
df["z2"] = exog_vca[:, 0]
df["z3"] = exog_vca[:, 1]
df["z4"] = exog_vcb[:, 0]
df["z5"] = exog_vcb[:, 1]
vcf = {"a": "0 + z2 + z3", "b": "0 + z4 + z5"}
m = MixedLM.from_formula("y ~ 0 + x1 + x2", groups="grp",
re_formula="0 + z0 + z1",
vc_formula=vcf, data=df)
# Build a params vector that is comparable to
# MixedLMResults.params
import statsmodels
mp = statsmodels.regression.mixed_linear_model.MixedLMParams
po = mp.from_components(fe_params=fe_params, cov_re=cov_re,
vcomp=vcomp)
pa = po.get_packed(has_fe=True, use_sqrt=False)
pa[len(fe_params):] /= scale
# Get a realization
dist = m.get_distribution(pa, scale, None)
yr = dist.rvs(0)
# Check the overall variance
v = (np.dot(exog_re, cov_re) * exog_re).sum(1).mean()
v += vcomp[0] * (exog_vca**2).sum(1).mean()
v += vcomp[1] * (exog_vcb**2).sum(1).mean()
v += scale
assert_allclose(np.var(yr - ey), v, rtol=1e-2, atol=1e-4)
if v["from"][0] == "all":
from_inds = np.arange(mat_n)
else:
from_inds = np.array([label_names.index(x) for x in v["from"]])
from_mat = these_data[:, from_inds, ]
from_mat = np.nanmean(from_mat, axis=1)
if v["to"][0] == "all":
to_inds = np.arange(mat_n)
else:
to_inds = np.array([label_names.index(x) for x in v["to"]])
to_mat = from_mat[:, to_inds]
quant = np.nanmean(to_mat, axis=1)
df["Brain"] = quant
model = MixedLM.from_formula(formula, df, groups=group_id)
mod_fit = model.fit(reml=False)
print(mod_fit.summary())
stat_cond = "C(Block, Treatment('rest'))[T.task]"
CIs = mod_fit.conf_int()
mod_ests[k] = {
"Rest": mod_fit.params["Intercept"],
"Task": mod_fit.params[stat_cond],
"Rest_CIs": np.array([CIs[0]["Intercept"], CIs[1]["Intercept"]]),
"Task_CIs": np.array([CIs[0][stat_cond], CIs[1][stat_cond]])
}
fig, ax = dpte_bar(mod_ests)
else:
these_data = data.copy()
triu_inds, tril_inds = np.triu_indices(mat_n, k=1), np.tril_indices(mat_n,
k=-1)
print " -------- 2" best_lm_hist, stuff_lm_hist = big_ass_matrix(df=sheep, y=histcols, x=imagecols, group=None, short=5) print " -------- 3" best_mm_phys, stuff_mm_phys = big_ass_matrix(df=sheep, y=pcols, x=imagecols, group="AgeAtDeath", short=5) print " -------- 4" best_mm_hist, stuff_mm_hist = big_ass_matrix(df=sheep, y=histcols, x=imagecols, group="AgeAtDeath", short=5) # <codecell> y = "BDHyperplasia" x = ["Inflammation", "Scale", "Directionality"] dfx = sheep[delayer([x, y, "AgeAtDeath"])].dropna() model = MixedLM.from_formula(rstr(y, x), data=dfx, groups="AgeAtDeath").fit() #model = sm.GLS(endog=dfx.Portal_inflammation, exog=dfx[["FociSize", "AgeAtDeath"]]).fit() dfx = sheep[["BDHyperplasia", "Inflammation", "AgeAtDeath"]].dropna() model2 = MixedLM.from_formula(rstr(y, ["Inflammation"]), data=dfx, groups="AgeAtDeath").fit() dfx = sheep[["BDHyperplasia", "FociSize", "AgeAtDeath"]].dropna() model3 = MixedLM.from_formula(rstr(y, ["FociSize"]), data=dfx, groups="AgeAtDeath").fit() # <codecell> ss = "E" s = np.array([sheep[sheep.AgeAtDeath == model.random_effects.index.values[i]][ss].iloc[0] for i in range(len(model.random_effects.index.values))]) s -= s.min() s /= s.max()