def _f_stat_raw(self):
"""Returns the raw f-stat value."""
from scipy.stats import f
cols = self._x.columns
if self._nw_lags is None:
F = self._r2_raw / (self._r2_raw - self._r2_adj_raw)
q = len(cols)
if 'intercept' in cols:
q -= 1
shape = q, self.df_resid
p_value = 1 - f.cdf(F, shape[0], shape[1])
return F, shape, p_value
k = len(cols)
R = np.eye(k)
r = np.zeros((k, 1))
try:
intercept = cols.get_loc('intercept')
R = np.concatenate((R[0:intercept], R[intercept + 1:]))
r = np.concatenate((r[0:intercept], r[intercept + 1:]))
except KeyError:
# no intercept
pass
return math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
self._nobs, self.df)
def _f_stat_raw(self):
"""Returns the raw f-stat value."""
from scipy.stats import f
cols = self._x.columns
if self._nw_lags is None:
F = self._r2_raw / (self._r2_raw - self._r2_adj_raw)
q = len(cols)
if 'intercept' in cols:
q -= 1
shape = q, self.df_resid
p_value = 1 - f.cdf(F, shape[0], shape[1])
return F, shape, p_value
k = len(cols)
R = np.eye(k)
r = np.zeros((k, 1))
try:
intercept = cols.get_loc('intercept')
R = np.concatenate((R[0: intercept], R[intercept + 1:]))
r = np.concatenate((r[0: intercept], r[intercept + 1:]))
except KeyError:
# no intercept
pass
return math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
self._nobs, self.df)
def f_test(self, hypothesis):
"""Runs the F test, given a joint hypothesis. The hypothesis is
represented by a collection of equations, in the form
A*x_1+B*x_2=C
You must provide the coefficients even if they're 1. No spaces.
The equations can be passed as either a single string or a
list of strings.
Examples
--------
o = ols(...)
o.f_test('1*x1+2*x2=0,1*x3=0')
o.f_test(['1*x1+2*x2=0','1*x3=0'])
"""
x_names = self._x.columns
R = []
r = []
if isinstance(hypothesis, str):
eqs = hypothesis.split(',')
elif isinstance(hypothesis, list):
eqs = hypothesis
else: # pragma: no cover
raise Exception('hypothesis must be either string or list')
for equation in eqs:
row = np.zeros(len(x_names))
lhs, rhs = equation.split('=')
for s in lhs.split('+'):
ss = s.split('*')
coeff = float(ss[0])
x_name = ss[1]
if x_name not in x_names:
raise Exception('no coefficient named %s' % x_name)
idx = x_names.get_loc(x_name)
row[idx] = coeff
rhs = float(rhs)
R.append(row)
r.append(rhs)
R = np.array(R)
q = len(r)
r = np.array(r).reshape(q, 1)
result = math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
self._nobs, self.df)
return f_stat_to_dict(result)
def f_test(self, hypothesis):
"""Runs the F test, given a joint hypothesis. The hypothesis is
represented by a collection of equations, in the form
A*x_1+B*x_2=C
You must provide the coefficients even if they're 1. No spaces.
The equations can be passed as either a single string or a
list of strings.
Examples
--------
o = ols(...)
o.f_test('1*x1+2*x2=0,1*x3=0')
o.f_test(['1*x1+2*x2=0','1*x3=0'])
"""
x_names = self._x.columns
R = []
r = []
if isinstance(hypothesis, str):
eqs = hypothesis.split(',')
elif isinstance(hypothesis, list):
eqs = hypothesis
else: # pragma: no cover
raise Exception('hypothesis must be either string or list')
for equation in eqs:
row = np.zeros(len(x_names))
lhs, rhs = equation.split('=')
for s in lhs.split('+'):
ss = s.split('*')
coeff = float(ss[0])
x_name = ss[1]
if x_name not in x_names:
raise Exception('no coefficient named %s' % x_name)
idx = x_names.get_loc(x_name)
row[idx] = coeff
rhs = float(rhs)
R.append(row)
r.append(rhs)
R = np.array(R)
q = len(r)
r = np.array(r).reshape(q, 1)
result = math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
self._nobs, self.df)
return f_stat_to_dict(result)
def get_result(beta, vcov, n, d):
return math.calc_F(R, r, beta, vcov, n, d)
def get_result(beta, vcov, n, d):
return math.calc_F(R, r, beta, vcov, n, d)