def __init__(self, y, x, w):
# 1a. OLS --> \tilde{betas}
ols = OLS.BaseOLS(y=y, x=x)
self.n, self.k = ols.x.shape
self.x = ols.x
self.y = ols.y
# 1b. GMM --> \tilde{\lambda1}
moments = _momentsGM_Error(w, ols.u)
lambda1 = optim_moments(moments)
# 2a. OLS -->\hat{betas}
xs = get_spFilter(w, lambda1, self.x)
ys = get_spFilter(w, lambda1, self.y)
ols2 = OLS.BaseOLS(y=ys, x=xs)
# Output
self.predy = spdot(self.x, ols2.betas)
self.u = y - self.predy
self.betas = np.vstack((ols2.betas, np.array([[lambda1]])))
self.sig2 = ols2.sig2n
self.e_filtered = self.u - lambda1 * w * self.u
self.vm = self.sig2 * ols2.xtxi
se_betas = np.sqrt(self.vm.diagonal())
self._cache = {}
def vif(reg):
"""
Calculates the variance inflation factor for each independent variable.
For the ease of indexing the results, the constant is currently
included. This should be omitted when reporting the results to the
output text. [Greene2003]_
Parameters
----------
reg : regression object
output instance from a regression model
Returns
-------
vif_result : list of tuples
each tuple includes the vif and the tolerance, the
order of the variables corresponds to their order in
the reg.x matrix
Examples
--------
>>> import numpy as np
>>> import pysal
>>> import diagnostics
>>> from ols import OLS
Read the DBF associated with the Columbus data.
>>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
Create the dependent variable vector.
>>> y = np.array(db.by_col("CRIME"))
>>> y = np.reshape(y, (49,1))
Create the matrix of independent variables.
>>> X = []
>>> X.append(db.by_col("INC"))
>>> X.append(db.by_col("HOVAL"))
>>> X = np.array(X).T
Run an OLS regression.
>>> reg = OLS(y,X)
Calculate the variance inflation factor (VIF).
>>> testresult = diagnostics.vif(reg)
Select the tuple for the income variable.
>>> incvif = testresult[1]
Print the VIF for income.
>>> print("%12.12f"%incvif[0])
1.333117497189
Print the tolerance for income.
>>> print("%12.12f"%incvif[1])
0.750121427487
Repeat for the home value variable.
>>> hovalvif = testresult[2]
>>> print("%12.12f"%hovalvif[0])
1.333117497189
>>> print("%12.12f"%hovalvif[1])
0.750121427487
"""
X = reg.x
n, k = X.shape
vif_result = []
for j in range(k):
Z = X.copy()
Z = np.delete(Z, j, 1)
y = X[:, j]
import ols as OLS
aux = OLS.BaseOLS(y, Z)
mean_y = aux.mean_y
utu = aux.utu
ss_tot = sum((y - mean_y)**2)
if ss_tot == 0:
resj = pysal.MISSINGVALUE
else:
r2aux = 1 - utu / ss_tot
tolj = 1 - r2aux
vifj = 1 / tolj
resj = (vifj, tolj)
vif_result.append(resj)
return vif_result
def white(reg):
"""
Calculates the White test to check for heteroscedasticity. [White1980]_
Parameters
----------
reg : regression object
output instance from a regression model
Returns
-------
white_result : dictionary
contains the statistic (white), degrees of freedom
(df) and the associated p-value (pvalue) for the
White test.
white : float
scalar value for the White test statistic.
df : integer
degrees of freedom associated with the test
pvalue : float
p-value associated with the statistic (chi^2
distributed with k df)
Notes
-----
x attribute in the reg object must have a constant term included. This is
standard for spreg.OLS so no testing done to confirm constant.
Examples
--------
>>> import numpy as np
>>> import pysal
>>> import diagnostics
>>> from ols import OLS
Read the DBF associated with the Columbus data.
>>> db = pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
Create the dependent variable vector.
>>> y = np.array(db.by_col("CRIME"))
>>> y = np.reshape(y, (49,1))
Create the matrix of independent variables.
>>> X = []
>>> X.append(db.by_col("INC"))
>>> X.append(db.by_col("HOVAL"))
>>> X = np.array(X).T
Run an OLS regression.
>>> reg = OLS(y,X)
Calculate the White test for heteroscedasticity.
>>> testresult = diagnostics.white(reg)
Print the degrees of freedom for the test.
>>> print testresult['df']
5
Print the test statistic.
>>> print("%1.3f"%testresult['wh'])
19.946
Print the associated p-value.
>>> print("%1.4f"%testresult['pvalue'])
0.0013
"""
e = reg.u**2
k = int(reg.k)
n = int(reg.n)
y = reg.y
X = reg.x
#constant = constant_check(X)
# Check for constant, if none add one, see Greene 2003, pg. 222
# if constant == False:
# X = np.hstack((np.ones((n,1)),X))
# Check for multicollinearity in the X matrix
ci = condition_index(reg)
if ci > 30:
white_result = "Not computed due to multicollinearity."
return white_result
# Compute cross-products and squares of the regression variables
if type(X).__name__ == 'ndarray':
A = np.zeros((n, (k * (k + 1)) // 2))
elif type(X).__name__ == 'csc_matrix' or type(X).__name__ == 'csr_matrix':
# this is probably inefficient
A = SP.lil_matrix((n, (k * (k + 1)) // 2))
else:
raise Exception, "unknown X type, %s" % type(X).__name__
counter = 0
for i in range(k):
for j in range(i, k):
v = spmultiply(X[:, i], X[:, j], False)
A[:, counter] = v
counter += 1
# Append the original variables
A = sphstack(X, A) # note: this also converts a LIL to CSR
n, k = A.shape
# Check to identify any duplicate or constant columns in A
omitcolumn = []
for i in range(k):
current = A[:, i]
# remove all constant terms (will add a constant back later)
if spmax(current) == spmin(current):
omitcolumn.append(i)
pass
# do not allow duplicates
for j in range(k):
check = A[:, j]
if i < j:
test = abs(current - check).sum()
if test == 0:
omitcolumn.append(j)
uniqueomit = set(omitcolumn)
omitcolumn = list(uniqueomit)
# Now the identified columns must be removed
if type(A).__name__ == 'ndarray':
A = np.delete(A, omitcolumn, 1)
elif type(A).__name__ == 'csc_matrix' or type(A).__name__ == 'csr_matrix':
# this is probably inefficient
keepcolumn = range(k)
for i in omitcolumn:
keepcolumn.remove(i)
A = A[:, keepcolumn]
else:
raise Exception, "unknown A type, %s" % type(X).__name__
A = sphstack(np.ones((A.shape[0], 1)), A) # add a constant back in
n, k = A.shape
# Conduct the auxiliary regression and calculate the statistic
import ols as OLS
aux_reg = OLS.BaseOLS(e, A)
aux_r2 = r2(aux_reg)
wh = aux_r2 * n
df = k - 1
pvalue = chisqprob(wh, df)
white_result = {'df': df, 'wh': wh, 'pvalue': pvalue}
return white_result
if len(name_y) > 1 and isinstance(name_y, list):
name_y = ''.join([i for i in name_y[0] if not i.isdigit()])
if len(name_y) == 1 and isinstance(name_y, list):
name_y = name_y[0]
if name_x:
if len(name_x) != k*T and len(name_x) != k:
raise Exception("Names of columns in X must have exactly either k or k*t elements.")
if len(name_x) > k:
name_bigx = []
for i in range(k):
name_bigx.append(''.join([j for j in name_x[i*T] if not j.isdigit()]))
name_x = name_bigx
return bigy, bigx, name_y, name_x
ols = OLS.BaseOLS(y=y, x=x)
x, y, n, k, xtx = ols.x, ols.y, ols.n, ols.k, ols.xtx
N = w.n
T = y.shape[0]//N
moments, trace_w2 = _moments_kkp(w.sparse, ols.u, 0)
lambda1, sig_v = optim_moments(moments, all_par=True)
Tw = SP.kron(SP.identity(T),w.sparse)
ub = Tw.dot(ols.u)
ulu = ols.u - lambda1*ub
Q1 = SP.kron(np.ones((T,T))/T,SP.identity(N))
sig_1 = float(np.dot(ulu.T,Q1.dot(ulu))/N)
#print('initial_lamb_sig:',lambda1,sig_v,sig_1)
#print('theta:', 1 - np.sqrt(sig_v)/ np.sqrt(sig_1))
Xi_a = SP.diags([(sig_v*sig_v)/(T-1),sig_1*sig_1])
if full_weights:
Tau = _get_Tau(w.sparse,trace_w2)
