def setup_class(cls):
d = macrodata.load_pandas().data
# growth rates
d['gs_l_realinv'] = 400 * np.log(d['realinv']).diff()
d['gs_l_realgdp'] = 400 * np.log(d['realgdp']).diff()
d['lint'] = d['realint'].shift(1)
d['tbilrate'] = d['tbilrate'].shift(1)
d = d.dropna()
cls.d = d
endogg = d['gs_l_realinv']
exogg = add_constant(d[['gs_l_realgdp', 'lint']])
exogg2 = add_constant(d[['gs_l_realgdp', 'tbilrate']])
exogg3 = add_constant(d[['gs_l_realgdp']])
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
res_ols3 = OLS(endogg, exogg3).fit()
cls.res = res_ols
cls.res2 = res_ols2
cls.res3 = res_ols3
cls.endog = cls.res.model.endog
cls.exog = cls.res.model.exog
def setup_class(cls):
mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs)
cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs)
cls.res1b = mod1.fit(disp=False, **cls.fit_kwargs)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)
def test_regression_with_tuples(self):
i = pd.Series([1, 2, 3, 4] * 10, name="i")
y = pd.Series([1, 2, 3, 4, 5] * 8, name="y")
x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8] * 5, name="x")
df = pd.DataFrame(index=i.index)
df = df.join(i)
endo = df.join(y)
exo = df.join(x)
endo_groups = endo.groupby("i")
exo_groups = exo.groupby("i")
exo_Df = exo_groups.agg([np.sum, np.max])
endo_Df = endo_groups.agg([np.sum, np.max])
reg = OLS(exo_Df[[("x", "sum")]], endo_Df).fit()
interesting_lines = []
with warnings.catch_warnings():
# Catch ominormal warning, not interesting here
warnings.simplefilter("ignore")
for line in str(reg.summary()).splitlines():
if "('" in line:
interesting_lines.append(line[:38])
desired = [
"Dep. Variable: ('x', 'sum') ",
"('y', 'sum') 1.4595 0.209 ",
"('y', 'amax') 0.2432 0.035 "
]
assert sorted(desired) == sorted(interesting_lines)
def test_regularized_weights(self):
np.random.seed(1432)
exog1 = np.random.normal(size=(100, 3))
endog1 = exog1[:, 0] + exog1[:, 1] + np.random.normal(size=100)
exog2 = np.random.normal(size=(100, 3))
endog2 = exog2[:, 0] + exog2[:, 1] + np.random.normal(size=100)
exog_a = np.vstack((exog1, exog1, exog2))
endog_a = np.concatenate((endog1, endog1, endog2))
# Should be equivalent to exog_a, endog_a.
exog_b = np.vstack((exog1, exog2))
endog_b = np.concatenate((endog1, endog2))
wgts = np.ones(200)
wgts[0:100] = 2
sigma = np.diag(1 / wgts)
# TODO: parametrize?
for L1_wt in [0, 0.5, 1]:
for alpha in [0, 1]:
mod1 = OLS(endog_a, exog_a)
rslt1 = mod1.fit_regularized(L1_wt=L1_wt, alpha=alpha)
mod2 = WLS(endog_b, exog_b, weights=wgts)
rslt2 = mod2.fit_regularized(L1_wt=L1_wt, alpha=alpha)
mod3 = GLS(endog_b, exog_b, sigma=sigma)
rslt3 = mod3.fit_regularized(L1_wt=L1_wt, alpha=alpha)
assert_almost_equal(rslt1.params, rslt2.params, decimal=3)
assert_almost_equal(rslt1.params, rslt3.params, decimal=3)
def setup_class(cls):
mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs)
cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs)
cls.res1b = mod1.fit(cov_type='nw-panel', cov_kwds=cls.cov_kwds)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)
def setup_class(cls):
R = np.zeros(7)
R[4:6] = [1, -1]
data = datasets.longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
cls.Ttest1 = res1.t_test(R)
def setup_class(cls):
data = datasets.longley.load(as_pandas=False)
res1 = OLS(data.endog, data.exog).fit()
cls.res1 = res1
#cls.res2.wresid = res1.wresid # workaround hack
res_qr = OLS(data.endog, data.exog).fit(method="qr")
cls.res_qr = res_qr
def test_const_indicator():
np.random.seed(12345)
X = np.random.randint(0, 3, size=30)
X = categorical(X, drop=True)
y = np.dot(X, [1., 2., 3.]) + np.random.normal(size=30)
modc = OLS(y, add_constant(X[:, 1:], prepend=True)).fit()
mod = OLS(y, X, hasconst=True).fit()
assert_almost_equal(modc.rsquared, mod.rsquared, 12)
def setup_class(cls):
mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs)
cls.res1 = mod1.fit(**cls.fit_kwargs)
mod2 = OLS(endog, exog)
# check kernel as string
kwds2 = {'kernel': 'uniform', 'maxlags': 2}
cls.res2 = mod2.fit(cov_type=cls.cov_type, cov_kwds=kwds2)
def test_summary_as_latex():
# GH#734
dta = datasets.longley.load_pandas()
X = dta.exog
X["constant"] = 1
y = dta.endog
with warnings.catch_warnings(record=True):
res = OLS(y, X).fit()
table = res.summary().as_latex()
# replace the date and time
table = re.sub(r"(?<=\n\\textbf\{Date:\} &).+?&",
r" Sun, 07 Apr 2013 &", table)
table = re.sub(r"(?<=\n\\textbf\{Time:\} &).+?&",
r" 13:46:07 &", table)
expected = textwrap.dedent("""
\\begin{center}
\\begin{tabular}{lclc}
\\toprule
\\textbf{Dep. Variable:} & TOTEMP & \\textbf{ R-squared: } & 0.995 \\\\
\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.992 \\\\
\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 330.3 \\\\
\\textbf{Date:} & Sun, 07 Apr 2013 & \\textbf{ Prob (F-statistic):} & 4.98e-10 \\\\
\\textbf{Time:} & 13:46:07 & \\textbf{ Log-Likelihood: } & -109.62 \\\\
\\textbf{No. Observations:} & 16 & \\textbf{ AIC: } & 233.2 \\\\
\\textbf{Df Residuals:} & 9 & \\textbf{ BIC: } & 238.6 \\\\
\\textbf{Df Model:} & 6 & \\textbf{ } & \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lcccccc}
& \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$>$$|$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\
\\midrule
\\textbf{GNPDEFL} & 15.0619 & 84.915 & 0.177 & 0.863 & -177.029 & 207.153 \\\\
\\textbf{GNP} & -0.0358 & 0.033 & -1.070 & 0.313 & -0.112 & 0.040 \\\\
\\textbf{UNEMP} & -2.0202 & 0.488 & -4.136 & 0.003 & -3.125 & -0.915 \\\\
\\textbf{ARMED} & -1.0332 & 0.214 & -4.822 & 0.001 & -1.518 & -0.549 \\\\
\\textbf{POP} & -0.0511 & 0.226 & -0.226 & 0.826 & -0.563 & 0.460 \\\\
\\textbf{YEAR} & 1829.1515 & 455.478 & 4.016 & 0.003 & 798.788 & 2859.515 \\\\
\\textbf{constant} & -3.482e+06 & 8.9e+05 & -3.911 & 0.004 & -5.5e+06 & -1.47e+06 \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lclc}
\\textbf{Omnibus:} & 0.749 & \\textbf{ Durbin-Watson: } & 2.559 \\\\
\\textbf{Prob(Omnibus):} & 0.688 & \\textbf{ Jarque-Bera (JB): } & 0.684 \\\\
\\textbf{Skew:} & 0.420 & \\textbf{ Prob(JB): } & 0.710 \\\\
\\textbf{Kurtosis:} & 2.434 & \\textbf{ Cond. No. } & 4.86e+09 \\\\
\\bottomrule
\\end{tabular}
%\\caption{OLS Regression Results}
\\end{center}
Warnings: \\newline
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline
[2] The condition number is large, 4.86e+09. This might indicate that there are \\newline
strong multicollinearity or other numerical problems.""").strip() # noqa:E501
assert_equal(table, expected)
def setup_class(cls):
# check kernel specified as string
mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs)
cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(disp=False,
cov_type=cls.cov_type,
cov_kwds={'maxlags': 2})
def test_conf_int_single_regressor():
# GH#706 single-regressor model (i.e. no intercept) with 1D exog
# should get passed to DataFrame for conf_int
y = pd.Series(np.random.randn(10))
x = pd.Series(np.ones(10))
res = OLS(y, x).fit()
conf_int = res.conf_int()
assert conf_int.shape == (1, 2)
assert isinstance(conf_int, pd.DataFrame)
def setup_class(cls):
mod1 = cls.model_cls(endog, exog, **cls.mod_kwargs)
cls.res1 = mod1.fit(disp=False, **cls.fit_kwargs)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(disp=False, **cls.fit_kwargs)
# for debugging
cls.res3 = mod2.fit(cov_type=cls.cov_type, cov_kwds={'maxlags': 2})
def setup_class(cls):
data = datasets.longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
R2 = [[0, 1, -1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, -1, 0]]
cls.Ftest1 = res1.f_test(R2)
hyp = 'x2 = x3, x5 = x6'
cls.NewFtest1 = res1.f_test(hyp)
def setup_class(cls):
data = datasets.longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
R = np.array([[0, 1, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0]])
q = np.array([0, 0, 0, 1, 0])
cls.Ftest1 = res1.f_test((R, q))
def test_outlier_influence_funcs(reset_randomstate):
x = add_constant(np.random.randn(10, 2))
y = x.sum(1) + np.random.randn(10)
res = OLS(y, x).fit()
out_05 = oi.summary_table(res)
# GH#3344 : Check alpha has an effect
out_01 = oi.summary_table(res, alpha=0.01)
assert np.all(out_01[1][:, 6] <= out_05[1][:, 6])
assert np.all(out_01[1][:, 7] >= out_05[1][:, 7])
res2 = OLS(y, x[:, 0]).fit()
oi.summary_table(res2, alpha=0.05)
infl = res2.get_influence()
infl.summary_table()
def setup(self):
model = OLS(self.res1.model.endog, self.res1.model.exog)
res_ols = model.fit(cov_type='cluster',
cov_kwds=dict(groups=self.groups,
use_correction=False,
use_t=False,
df_correction=True))
self.res3 = self.res1
self.res1 = res_ols
self.bse_robust = res_ols.bse
self.cov_robust = res_ols.cov_params()
cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
se1 = sw.se_cov(cov1)
self.bse_robust2 = se1
self.cov_robust2 = cov1
def setup_class(cls):
np.random.seed(54321)
cls.endog_n_ = np.random.uniform(0, 20, size=30)
cls.endog_n_one = cls.endog_n_[:, None]
cls.exog_n_ = np.random.uniform(0, 20, size=30)
cls.exog_n_one = cls.exog_n_[:, None]
cls.degen_exog = cls.exog_n_one[:-1]
cls.mod1 = OLS(cls.endog_n_one, cls.exog_n_one)
cls.mod1.df_model += 1
cls.res1 = cls.mod1.fit()
# Note that these are created for every subclass..
# A little extra overhead probably
cls.mod2 = OLS(cls.endog_n_one, cls.exog_n_one)
cls.mod2.df_model += 1
cls.res2 = cls.mod2.fit()
def test_missing(self):
data = datasets.longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
data.endog[[3, 7, 14]] = np.nan
mod = OLS(data.endog, data.exog, missing='drop')
assert mod.endog.shape[0] == 13
assert mod.exog.shape[0] == 13
def test_influence_wrapped():
d = macrodata.load_pandas().data
# growth rates
gs_l_realinv = 400 * np.log(d['realinv']).diff().dropna()
gs_l_realgdp = 400 * np.log(d['realgdp']).diff().dropna()
lint = d['realint'][:-1]
# re-index these because they won't conform to lint
gs_l_realgdp.index = lint.index
gs_l_realinv.index = lint.index
data = dict(const=np.ones_like(lint), lint=lint, lrealgdp=gs_l_realgdp)
# order is important
exog = pd.DataFrame(data, columns=['const', 'lrealgdp', 'lint'])
res = OLS(gs_l_realinv, exog).fit()
# basic
# already tested
# cov.scaled and cov.unscaled have already been tested
# TODO: check that above is correct;
# comment is (roughly) copied from upstream
infl = oi.OLSInfluence(res)
# smoke test just to make sure it works, results separately tested
df = infl.summary_frame()
assert isinstance(df, pd.DataFrame)
# this test is slow
path = os.path.join(cur_dir, "results", "influence_lsdiag_R.json")
with open(path, 'r') as fp:
lsdiag = json.load(fp)
c0, c1 = infl.cooks_distance # TODO: what's c1, it's pvalues? -ss
# NOTE: we get a hard-cored 5 decimals with pandas testing
assert_almost_equal(c0, lsdiag['cooks'], 14)
assert_almost_equal(infl.hat_matrix_diag, (lsdiag['hat']), 14)
assert_almost_equal(infl.resid_studentized_internal,
lsdiag['std.res'], 14)
# slow
dffits, dffth = infl.dffits
assert_almost_equal(dffits, lsdiag['dfits'], 14)
assert_almost_equal(infl.resid_studentized_external,
lsdiag['stud.res'], 14)
fn = os.path.join(cur_dir, "results", "influence_measures_R.csv")
infl_r = pd.read_csv(fn, index_col=0)
# not used yet:
# fn = os.path.join(cur_dir, "results", "influence_measures_bool_R.csv")
# conv = lambda s: 1 if s == 'TRUE' else 0
#infl_bool_r = pd.read_csv(fn, index_col=0,
# converters=dict(zip(lrange(7), [conv]*7)))
infl_r2 = np.asarray(infl_r)
# TODO: finish wrapping this stuff
assert_almost_equal(infl.dfbetas, infl_r2[:, :3], decimal=13)
assert_almost_equal(infl.cov_ratio, infl_r2[:, 4], decimal=14)
def test_formula_missing_cat():
# GH#805
dta = datasets.grunfeld.load_pandas().data
dta.loc[dta.index[0], 'firm'] = np.nan
formula = 'value ~ invest + capital + firm + year'
mod = OLS.from_formula(formula=formula, data=dta.dropna())
res = mod.fit()
mod2 = OLS.from_formula(formula=formula, data=dta)
res2 = mod2.fit()
assert_almost_equal(res.params.values,
res2.params.values)
with pytest.raises(PatsyError):
OLS.from_formula(formula, data=dta, missing='raise')
def setup_class(cls):
d2 = macrodata.load_pandas().data
g_gdp = 400 * np.diff(np.log(d2['realgdp'].values))
g_inv = 400 * np.diff(np.log(d2['realinv'].values))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1].values],
prepend=False)
cls.res1 = OLS(g_inv, exogg).fit()
def setup_class(cls):
# TODO: Test HAC method
X = np.random.randn(100, 3)
b = np.ones((3, 1))
e = np.random.randn(100, 1)
y = np.dot(X, b) + e
# Cases?
# Homoskedastic
# HC0
cls.res1_full = OLS(y, X).fit()
cls.res1_restricted = OLS(y, X[:, 0]).fit()
cls.res2_full = cls.res1_full.get_robustcov_results('HC0')
cls.res2_restricted = cls.res1_restricted.get_robustcov_results('HC0')
cls.X = X
cls.Y = y
def setup_class(cls):
data = datasets.longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
cls.res1 = OLS(data.endog, data.exog).fit()
R = np.identity(7)
cls.Ttest = cls.res1.t_test(R)
hyp = 'x1 = 0, x2 = 0, x3 = 0, x4 = 0, x5 = 0, x6 = 0, const = 0'
cls.NewTTest = cls.res1.t_test(hyp)
def test_recursive_residuals(self):
reccumres_standardize = np.array([
-2.151, -3.748, -3.114, -3.096, -1.865, -2.230, -1.194, -3.500,
-3.638, -4.447, -4.602, -4.631, -3.999, -4.830, -5.429, -5.435,
-6.554, -8.093, -8.567, -7.532, -7.079, -8.468, -9.320, -12.256,
-11.932, -11.454, -11.690, -11.318, -12.665, -12.842, -11.693,
-10.803, -12.113, -12.109, -13.002, -11.897, -10.787, -10.159,
-9.038, -9.007, -8.634, -7.552, -7.153, -6.447, -5.183, -3.794,
-3.511, -3.979, -3.236, -3.793, -3.699, -5.056, -5.724, -4.888,
-4.309, -3.688, -3.918, -3.735, -3.452, -2.086, -6.520, -7.959,
-6.760, -6.855, -6.032, -4.405, -4.123, -4.075, -3.235, -3.115,
-3.131, -2.986, -1.813, -4.824, -4.424, -4.796, -4.000, -3.390,
-4.485, -4.669, -4.560, -3.834, -5.507, -3.792, -2.427, -1.756,
-0.354, 1.150, 0.586, 0.643, 1.773, -0.830, -0.388, 0.517, 0.819,
2.240, 3.791, 3.187, 3.409, 2.431, 0.668, 0.957, -0.928, 0.327,
-0.285, -0.625, -2.316, -1.986, -0.744, -1.396, -1.728, -0.646,
-2.602, -2.741, -2.289, -2.897, -1.934, -2.532, -3.175, -2.806,
-3.099, -2.658, -2.487, -2.515, -2.224, -2.416, -1.141, 0.650,
-0.947, 0.725, 0.439, 0.885, 2.419, 2.642, 2.745, 3.506, 4.491,
5.377, 4.624, 5.523, 6.488, 6.097, 5.390, 6.299, 6.656, 6.735,
8.151, 7.260, 7.846, 8.771, 8.400, 8.717, 9.916, 9.008, 8.910,
8.294, 8.982, 8.540, 8.395, 7.782, 7.794, 8.142, 8.362, 8.400,
7.850, 7.643, 8.228, 6.408, 7.218, 7.699, 7.895, 8.725, 8.938,
8.781, 8.350, 9.136, 9.056, 10.365, 10.495, 10.704, 10.784,
10.275, 10.389, 11.586, 11.033, 11.335, 11.661, 10.522, 10.392,
10.521, 10.126, 9.428, 9.734, 8.954, 9.949, 10.595, 8.016, 6.636,
6.975])
rr = diagnostic.recursive_olsresiduals(self.res, skip=3, alpha=0.95)
np.testing.assert_equal(np.round(rr[5][1:], 3),
reccumres_standardize) # extra zero in front
assert_almost_equal(rr[3][4:], np.diff(reccumres_standardize), 3)
assert_almost_equal(rr[4][3:].std(ddof=1), 10.7242, decimal=4)
# regression number, visually checked with graph from gretl
ub0 = np.array([13.37318571, 13.50758959, 13.64199346, 13.77639734,
13.91080121])
ub1 = np.array([39.44753774, 39.58194162, 39.7163455, 39.85074937,
39.98515325])
lb, ub = rr[6]
assert_almost_equal(ub[:5], ub0, decimal=7)
assert_almost_equal(lb[:5], -ub0, decimal=7)
assert_almost_equal(ub[-5:], ub1, decimal=7)
assert_almost_equal(lb[-5:], -ub1, decimal=7)
# test a few values with explicit OLS
endog = self.res.model.endog
exog = self.res.model.exog
params = []
ypred = []
for i in range(3, 10):
resi = OLS(endog[:i], exog[:i]).fit()
ypred.append(resi.model.predict(resi.params, exog[i]))
params.append(resi.params)
assert_almost_equal(rr[2][3:10], ypred, decimal=12)
assert_almost_equal(rr[0][3:10], endog[3:10] - ypred, decimal=12)
assert_almost_equal(rr[1][2:9], params, decimal=12)
def test_norm_resid_zero_variance(self):
with warnings.catch_warnings(record=True):
y = self.res1.model.endog
res = OLS(y, y).fit()
assert_allclose(res.scale, 0,
atol=1e-20)
assert_allclose(res.wresid,
res.resid_pearson,
atol=5e-11)
def test_rsquared_adj_overfit(self):
# Test that if df_resid = 0, rsquared_adj = 0.
# This is a regression test for user issue:
# GH#868
with warnings.catch_warnings(record=True):
x = np.random.randn(5)
y = np.random.randn(5, 6)
results = OLS(x, y).fit()
rsquared_adj = results.rsquared_adj
assert_equal(rsquared_adj, np.nan)
def setup_class(cls):
super(TestGLS_large_data, cls).setup_class()
nobs = 1000
y = np.random.randn(nobs, 1)
X = np.random.randn(nobs, 20)
sigma = np.ones_like(y)
cls.gls_res = GLS(y, X, sigma=sigma).fit()
cls.gls_res_scalar = GLS(y, X, sigma=1).fit()
cls.gls_res_none = GLS(y, X).fit()
cls.ols_res = OLS(y, X).fit()
def setup_class(cls):
nobs, k_exog = 100, 5
np.random.seed(987125)
x = np.random.randn(nobs, k_exog - 1)
x = add_constant(x)
y_true = x.sum(1) / 2
y = y_true + 2 * np.random.randn(nobs)
cls.endog = y
cls.exog = x
cls.idx_p_uc = np.array(cls.idx_uc)
cls.exogc = xc = x[:, cls.idx_uc]
mod_ols_c = OLS(y - 0.5 * x[:, 1], xc)
mod_ols_c.exog_names[:] = ['const', 'x2', 'x3', 'x4']
cls.mod2 = mod_ols_c
cls.res2 = cls.mod2.fit(**cls.fit_kwargs)
cls.init()
def setup_class(cls):
data = datasets.longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
ols_res = OLS(data.endog, data.exog).fit()
gls_res = GLS(data.endog, data.exog).fit()
gls_res_scalar = GLS(data.endog, data.exog, sigma=1)
cls.endog = data.endog
cls.exog = data.exog
cls.res1 = gls_res
cls.res2 = ols_res
cls.res3 = gls_res_scalar # TODO: Do something with this?
