def test_from_formula():
mod = RecursiveLS.from_formula('cpi ~ m1', data=dta)
res = mod.fit()
# Test the RLS estimates against OLS estimates
mod_ols = OLS.from_formula('cpi ~ m1', data=dta)
res_ols = mod_ols.fit()
assert_allclose(res.params, res_ols.params)
def test_from_formula():
mod = RecursiveLS.from_formula('cpi ~ m1', data=dta)
res = mod.fit()
# Test the RLS estimates against OLS estimates
mod_ols = OLS.from_formula('cpi ~ m1', data=dta)
res_ols = mod_ols.fit()
assert_allclose(res.params, res_ols.params)
def test_escaped_variable_name():
# Rename 'cpi' column to 'CPI_'
data = macrodata.load().data
data.rename(columns={'cpi': 'CPI_'}, inplace=True)
mod = OLS.from_formula('CPI_ ~ 1 + np.log(realgdp)', data=data)
res = mod.fit()
assert 'CPI\\_' in res.summary().as_latex()
assert 'CPI_' in res.summary().as_text()
def test_from_formula():
with pytest.warns(ValueWarning, match="No frequency information"):
mod = RecursiveLS.from_formula('cpi ~ m1', data=dta)
res = mod.fit()
# Test the RLS estimates against OLS estimates
mod_ols = OLS.from_formula('cpi ~ m1', data=dta)
res_ols = mod_ols.fit()
assert_allclose(res.params, res_ols.params)
def test_from_formula():
with pytest.warns(ValueWarning, match="No frequency information"):
mod = RecursiveLS.from_formula('cpi ~ m1', data=dta)
res = mod.fit()
# Test the RLS estimates against OLS estimates
mod_ols = OLS.from_formula('cpi ~ m1', data=dta)
res_ols = mod_ols.fit()
assert_allclose(res.params, res_ols.params)
def setup_class(cls):
from .test_diagnostic import get_duncan_data
endog, exog, labels = get_duncan_data()
data = pd.DataFrame(np.column_stack((endog, exog)),
columns='y const var1 var2'.split(),
index=labels)
res0 = GLM.from_formula('y ~ const + var1 + var2 - 1', data).fit()
res1 = OLS.from_formula('y ~ const + var1 + var2 - 1', data).fit()
cls.infl1 = res1.get_influence()
cls.infl0 = res0.get_influence()
def setup_class(cls):
from .test_diagnostic import get_duncan_data
endog, exog, labels = get_duncan_data()
data = pd.DataFrame(np.column_stack((endog, exog)),
columns='y const var1 var2'.split(),
index=labels)
res0 = GLM.from_formula('y ~ const + var1 + var2 - 1', data).fit()
res1 = OLS.from_formula('y ~ const + var1 + var2 - 1', data).fit()
cls.infl1 = res1.get_influence()
cls.infl0 = res0.get_influence()
def setup_class(cls):
nobs = 30
np.random.seed(987128)
x = np.random.randn(nobs, 3)
y = x.sum(1) + np.random.randn(nobs)
index = ['obs%02d' % i for i in range(nobs)]
# add one extra column to check that it doesn't matter
cls.data = pd.DataFrame(np.round(np.column_stack((y, x)), 4),
columns='y var1 var2 var3'.split(),
index=index)
cls.res = OLS.from_formula('y ~ var1 + var2', data=cls.data).fit()
def setup_class(cls):
nobs = 30
np.random.seed(987128)
x = np.random.randn(nobs, 3)
y = x.sum(1) + np.random.randn(nobs)
index = ['obs%02d' % i for i in range(nobs)]
# add one extra column to check that it doesn't matter
cls.data = pd.DataFrame(np.round(np.column_stack((y, x)), 4),
columns='y var1 var2 var3'.split(),
index=index)
cls.res = OLS.from_formula('y ~ var1 + var2', data=cls.data).fit()
def test_missing_formula_predict():
# see 2171
nsample = 30
data = np.linspace(0, 10, nsample)
null = np.array([np.nan])
data = pandas.DataFrame({'x': np.concatenate((data, null))})
beta = np.array([1, 0.1])
e = np.random.normal(size=nsample + 1)
data['y'] = beta[0] + beta[1] * data['x'] + e
model = OLS.from_formula('y ~ x', data=data)
fit = model.fit()
fit.predict(exog=data[:-1])
def test_missing_formula_predict():
# see 2171
nsample = 30
data = pandas.DataFrame({'x': np.linspace(0, 10, nsample)})
null = pandas.DataFrame({'x': np.array([np.nan])})
data = pandas.concat([data, null])
beta = np.array([1, 0.1])
e = np.random.normal(size=nsample+1)
data['y'] = beta[0] + beta[1] * data['x'] + e
model = OLS.from_formula('y ~ x', data=data)
fit = model.fit()
pred = fit.predict(exog=data[:-1])
def env_corr(self, env_vars, coeff_plot=False, qq_plot=False):
"""
Determine correlations with environmental/non-discretionary variables
using a logit regression. Tobit will be implemented when available
upstream in statsmodels.
Takes:
env_vars: A pandas dataframe of environmental variables
Returns:
corr_mod: the statsmodels' model instance containing the inputs
and results from the logit model.
Note that there can be no spaces in the variables' names.
"""
import matplotlib.pyplot as plt
from statsmodels.regression.linear_model import OLS
from statsmodels.graphics.gofplots import qqplot
from seaborn import coefplot
env_data = _to_dataframe(env_vars)
corr_data = env_data.join(self['Efficiency'])
corr_mod = OLS.from_formula(
"Efficiency ~ " + " + ".join(env_vars.columns), corr_data)
corr_res = corr_mod.fit()
#plot coeffs
if coeff_plot:
coefplot("Efficiency ~ " + " + ".join(env_vars.columns),
data=corr_data)
plt.xticks(rotation=45, ha='right')
plt.title('Regression coefficients and standard errors')
#plot qq of residuals
if qq_plot:
qqplot(corr_res.resid, line='s')
plt.title('Distribution of residuals')
print(corr_res.summary())
return corr_res
def env_corr(self, env_vars, coeff_plot=False, qq_plot=False):
"""
Determine correlations with environmental/non-discretionary variables
using a logit regression. Tobit will be implemented when available
upstream in statsmodels.
Takes:
env_vars: A pandas dataframe of environmental variables
Returns:
corr_mod: the statsmodels' model instance containing the inputs
and results from the logit model.
Note that there can be no spaces in the variables' names.
"""
import matplotlib.pyplot as plt
from statsmodels.regression.linear_model import OLS
from statsmodels.graphics.gofplots import qqplot
from seaborn import coefplot
env_data = _to_dataframe(env_vars)
corr_data = env_data.join(self['Efficiency'])
corr_mod = OLS.from_formula(
"Efficiency ~ " + " + ".join(env_vars.columns), corr_data)
corr_res = corr_mod.fit()
#plot coeffs
if coeff_plot:
coefplot("Efficiency ~ " + " + ".join(env_vars.columns),
data=corr_data)
plt.xticks(rotation=45, ha='right')
plt.title('Regression coefficients and standard errors')
#plot qq of residuals
if qq_plot:
qqplot(corr_res.resid, line='s')
plt.title('Distribution of residuals')
print(corr_res.summary())
return corr_res
def test_outlier_test():
endog, exog, labels = get_duncan_data()
ndarray_mod = OLS(endog, exog).fit()
rstudent = [
3.1345185839, -2.3970223990, 2.0438046359, -1.9309187757, 1.8870465798,
-1.7604905300, -1.7040324156, 1.6024285876, -1.4332485037,
-1.1044851583, 1.0688582315, 1.0185271840, -0.9024219332,
-0.9023876471, -0.8830953936, 0.8265782334, 0.8089220547, 0.7682770197,
0.7319491074, -0.6665962829, 0.5227352794, -0.5135016547, 0.5083881518,
0.4999224372, -0.4980818221, -0.4759717075, -0.4293565820,
-0.4114056499, -0.3779540862, 0.3556874030, 0.3409200462, 0.3062248646,
0.3038999429, -0.3030815773, -0.1873387893, 0.1738050251, 0.1424246593,
-0.1292266025, 0.1272066463, -0.0798902878, 0.0788467222, 0.0722556991,
0.0505098280, 0.0233215136, 0.0007112055
]
unadj_p = [
0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120,
0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623,
0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561,
0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039,
0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703,
0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263,
0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039,
0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197,
0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989
]
bonf_p = [
0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354,
4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133,
16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245, 20.1026416,
21.0763395, 22.8943818, 27.1787396, 27.4660532, 27.6257642, 27.8911043,
27.9489466, 28.6479487, 30.1460253, 30.7313018, 31.8336506, 32.5754218,
33.0707100, 34.2442399, 34.3233506, 34.3512109, 38.3543568, 38.8293308,
39.9348989, 40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391,
43.1982627, 44.1678127, 44.9746195
]
bonf_p = np.array(bonf_p)
bonf_p[bonf_p > 1] = 1
sorted_labels = [
"minister", "reporter", "contractor", "insurance.agent", "machinist",
"store.clerk", "conductor", "factory.owner", "mail.carrier",
"streetcar.motorman", "carpenter", "coal.miner", "bartender",
"bookkeeper", "soda.clerk", "chemist", "RR.engineer", "professor",
"electrician", "gas.stn.attendant", "auto.repairman", "watchman",
"banker", "machine.operator", "dentist", "waiter", "shoe.shiner",
"welfare.worker", "plumber", "physician", "pilot", "engineer",
"accountant", "lawyer", "undertaker", "barber", "store.manager",
"truck.driver", "cook", "janitor", "policeman", "architect", "teacher",
"taxi.driver", "author"
]
res2 = np.c_[rstudent, unadj_p, bonf_p]
res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True)
np.testing.assert_almost_equal(res.values, res2, 7)
np.testing.assert_equal(res.index.tolist(),
sorted_labels) # pylint: disable-msg=E1103
data = pd.DataFrame(np.column_stack((endog, exog)),
columns='y const var1 var2'.split(),
index=labels)
# check `order` with pandas bug in #3971
res_pd = OLS.from_formula('y ~ const + var1 + var2 - 0', data).fit()
res_outl2 = oi.outlier_test(res_pd, method='b', order=True)
assert_almost_equal(res_outl2.values, res2, 7)
assert_equal(res_outl2.index.tolist(), sorted_labels)
res_outl1 = res_pd.outlier_test(method='b')
res_outl1 = res_outl1.sort_values(['unadj_p'], ascending=True)
assert_almost_equal(res_outl1.values, res2, 7)
assert_equal(res_outl1.index.tolist(), sorted_labels)
assert_array_equal(res_outl2.index, res_outl1.index)
# additional keywords in method
res_outl3 = res_pd.outlier_test(method='b', order=True)
assert_equal(res_outl3.index.tolist(), sorted_labels)
res_outl4 = res_pd.outlier_test(method='b', order=True, cutoff=0.15)
assert_equal(res_outl4.index.tolist(), sorted_labels[:1])
def test_outlier_test():
endog, exog, labels = get_duncan_data()
ndarray_mod = OLS(endog, exog).fit()
rstudent = [3.1345185839, -2.3970223990, 2.0438046359, -1.9309187757,
1.8870465798, -1.7604905300, -1.7040324156, 1.6024285876,
-1.4332485037, -1.1044851583, 1.0688582315, 1.0185271840,
-0.9024219332, -0.9023876471, -0.8830953936, 0.8265782334,
0.8089220547, 0.7682770197, 0.7319491074, -0.6665962829,
0.5227352794, -0.5135016547, 0.5083881518, 0.4999224372,
-0.4980818221, -0.4759717075, -0.4293565820, -0.4114056499,
-0.3779540862, 0.3556874030, 0.3409200462, 0.3062248646,
0.3038999429, -0.3030815773, -0.1873387893, 0.1738050251,
0.1424246593, -0.1292266025, 0.1272066463, -0.0798902878,
0.0788467222, 0.0722556991, 0.0505098280, 0.0233215136,
0.0007112055]
unadj_p = [0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120,
0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623,
0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561,
0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039,
0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703,
0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263,
0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039,
0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197,
0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989]
bonf_p = [0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354,
4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133,
16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245,
20.1026416, 21.0763395, 22.8943818, 27.1787396, 27.4660532,
27.6257642, 27.8911043, 27.9489466, 28.6479487, 30.1460253,
30.7313018, 31.8336506, 32.5754218, 33.0707100, 34.2442399,
34.3233506, 34.3512109, 38.3543568, 38.8293308, 39.9348989,
40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391,
43.1982627, 44.1678127, 44.9746195]
bonf_p = np.array(bonf_p)
bonf_p[bonf_p > 1] = 1
sorted_labels = ["minister", "reporter", "contractor", "insurance.agent",
"machinist", "store.clerk", "conductor", "factory.owner",
"mail.carrier", "streetcar.motorman", "carpenter", "coal.miner",
"bartender", "bookkeeper", "soda.clerk", "chemist", "RR.engineer",
"professor", "electrician", "gas.stn.attendant", "auto.repairman",
"watchman", "banker", "machine.operator", "dentist", "waiter",
"shoe.shiner", "welfare.worker", "plumber", "physician", "pilot",
"engineer", "accountant", "lawyer", "undertaker", "barber",
"store.manager", "truck.driver", "cook", "janitor", "policeman",
"architect", "teacher", "taxi.driver", "author"]
res2 = np.c_[rstudent, unadj_p, bonf_p]
res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True)
np.testing.assert_almost_equal(res.values, res2, 7)
np.testing.assert_equal(res.index.tolist(), sorted_labels) # pylint: disable-msg=E1103
data = pd.DataFrame(np.column_stack((endog, exog)),
columns='y const var1 var2'.split(),
index=labels)
# check `order` with pandas bug in #3971
res_pd = OLS.from_formula('y ~ const + var1 + var2 - 0', data).fit()
res_outl2 = oi.outlier_test(res_pd, method='b', order=True)
assert_almost_equal(res_outl2.values, res2, 7)
assert_equal(res_outl2.index.tolist(), sorted_labels)
res_outl1 = res_pd.outlier_test(method='b')
res_outl1 = res_outl1.sort_values(['unadj_p'], ascending=True)
assert_almost_equal(res_outl1.values, res2, 7)
assert_equal(res_outl1.index.tolist(), sorted_labels)
assert_array_equal(res_outl2.index, res_outl1.index)
# additional keywords in method
res_outl3 = res_pd.outlier_test(method='b', order=True)
assert_equal(res_outl3.index.tolist(), sorted_labels)
res_outl4 = res_pd.outlier_test(method='b', order=True, cutoff=0.15)
assert_equal(res_outl4.index.tolist(), sorted_labels[:1])
def setup_class(cls):
formula_outcome = 'bweight ~ prenatal1_ + mmarried_ + mage + fbaby_'
mod = OLS.from_formula(formula_outcome, dta_cat)
tind = np.asarray(dta_cat['mbsmoke_'])
cls.teff = TreatmentEffect(mod, tind, results_select=res_probit)
