def test_zero_resid():
# smoke and regression tests
X = np.array([[1, 0], [0, 1], [0, 2.1], [0, 3.1]], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
res = QuantReg(y, X).fit(0.5, bandwidth='chamberlain') #'bofinger')
res.summary()
assert_allclose(res.params, np.array([0.0, 0.96774163]), rtol=1e-4, atol=1e-20)
assert_allclose(res.bse, np.array([0.0447576, 0.01154867]), rtol=1e-4, atol=1e-20)
assert_allclose(res.resid, np.array([0.0, 3.22583680e-02, -3.22574272e-02,
9.40732912e-07]), rtol=1e-4, atol=1e-20)
X = np.array([[1, 0], [0.1, 1], [0, 2.1], [0, 3.1]], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
res = QuantReg(y, X).fit(0.5, bandwidth='chamberlain')
res.summary()
assert_allclose(res.params, np.array([9.99982796e-08, 9.67741630e-01]),
rtol=1e-4, atol=1e-20)
assert_allclose(res.bse, np.array([0.04455029, 0.01155251]), rtol=1e-4, atol=1e-20)
assert_allclose(res.resid, np.array([-9.99982796e-08, 3.22583598e-02,
-3.22574234e-02, 9.46361860e-07]), rtol=1e-4, atol=1e-20)
def test_alpha_summary():
X = np.array([[1, 0], [0, 1], [0, 2.1], [0, 3.1]], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
res = QuantReg(y, X).fit(0.5, bandwidth='chamberlain', use_t=True)
summ_20 = res.summary(alpha=.2)
assert '[0.025 0.975]' not in str(summ_20)
assert '[0.1 0.9]' in str(summ_20)
def test_use_t_summary():
X = np.array([[1, 0], [0, 1], [0, 2.1], [0, 3.1]], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
res = QuantReg(y, X).fit(0.5, bandwidth='chamberlain', use_t=True)
summ = res.summary()
assert 'P>|t|' in str(summ)
assert 'P>|z|' not in str(summ)
def test_alpha_summary():
X = np.array([[1, 0], [0, 1], [0, 2.1], [0, 3.1]], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
res = QuantReg(y, X).fit(0.5, bandwidth='chamberlain', use_t=True)
summ_20 = res.summary(alpha=.2)
assert '[0.025 0.975]' not in str(summ_20)
assert '[0.1 0.9]' in str(summ_20)
def test_use_t_summary():
X = np.array([[1, 0], [0, 1], [0, 2.1], [0, 3.1]], dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
res = QuantReg(y, X).fit(0.5, bandwidth='chamberlain', use_t=True)
summ = res.summary()
assert 'P>|t|' in str(summ)
assert 'P>|z|' not in str(summ)
import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.regression.quantile_regression import QuantReg
import matplotlib.pyplot as plt
data = pd.read_csv('ice.csv')
x = data[['temp', 'street']]
x = sm.add_constant(x)
y = data['ice']
est = QuantReg(y, x).fit(q=0.99999)
print(est.summary())
