How to use spreg - 10 common examples
To help you get started, we’ve selected a few spreg examples, based on popular ways it is used in public projects.
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pysal / spglm / spglm / glm.py View on Github 
def normalized_cov_params(self):
return la.inv(spdot(self.w.T, self.w))def _compute_betas(y, x):
"""
compute MLE coefficients using iwls routine
Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
Geographically weighted regression: the analysis of spatially varying relationships.
"""
xT = x.T
xtx = spdot(xT, x)
xtx_inv = la.inv(xtx)
xtx_inv = sp.csr_matrix(xtx_inv)
xTy = spdot(xT, y, array_out=False)
betas = spdot(xtx_inv, xTy)
return betasdef _compute_betas(y, x):
"""
compute MLE coefficients using iwls routine
Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
Geographically weighted regression: the analysis of spatially varying relationships.
"""
xT = x.T
xtx = spdot(xT, x)
xtx_inv = la.inv(xtx)
xtx_inv = sp.csr_matrix(xtx_inv)
xTy = spdot(xT, y, array_out=False)
betas = spdot(xtx_inv, xTy)
return betasdef _compute_betas(y, x):
"""
compute MLE coefficients using iwls routine
Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
Geographically weighted regression: the analysis of spatially varying relationships.
"""
xT = x.T
xtx = spdot(xT, x)
xtx_inv = la.inv(xtx)
xtx_inv = sp.csr_matrix(xtx_inv)
xTy = spdot(xT, y, array_out=False)
betas = spdot(xtx_inv, xTy)
return betaswhile diff > tol and n_iter < max_iter:
n_iter += 1
w = family.weights(mu)
z = v + (family.link.deriv(mu) * (y - mu))
w = np.sqrt(w)
if not isinstance(x, np.ndarray):
w = sp.csr_matrix(w)
z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False)
if wi is None:
n_betas = _compute_betas(wz, wx)
else:
n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
v = spdot(x, n_betas)
mu = family.fitted(v)
if isinstance(family, Poisson):
mu = mu * offset
diff = min(abs(n_betas - betas))
betas = n_betas
if wi is None:
return betas, mu, wx, n_iter
else:
return betas, mu, v, w, z, xtx_inv_xt, n_iterdef __init__(self, y, X, family=family.Gaussian(), offset=None, y_fix = None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
if offset is None:
self.offset = np.ones(shape=(self.n,1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n,1))
else:
self.y_fix = y_fix
self.fit_params = {}def __init__(self, y, X, family=family.Gaussian(), offset=None, y_fix = None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
if offset is None:
self.offset = np.ones(shape=(self.n,1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n,1))
else:
self.y_fix = y_fixdef __init__(self, y, X, family=family.Gaussian(), offset=None, y_fix = None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
if offset is None:
self.offset = np.ones(shape=(self.n,1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n,1))
else:
self.y_fix = y_fix
self.fit_params = {}v = family.predict(y_off)
mu = family.starting_mu(y)
else:
mu = family.starting_mu(y)
v = family.predict(mu)
while diff > tol and n_iter < max_iter:
n_iter += 1
w = family.weights(mu)
z = v + (family.link.deriv(mu) * (y - mu))
w = np.sqrt(w)
if not isinstance(x, np.ndarray):
w = sp.csr_matrix(w)
z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False)
if wi is None:
n_betas = _compute_betas(wz, wx)
else:
n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
v = spdot(x, n_betas)
mu = family.fitted(v)
if isinstance(family, Poisson):
mu = mu * offset
diff = min(abs(n_betas - betas))
betas = n_betas
if wi is None:
return betas, mu, wx, n_iter
else:y_off = family.starting_mu(y_off)
v = family.predict(y_off)
mu = family.starting_mu(y)
else:
mu = family.starting_mu(y)
v = family.predict(mu)
while diff > tol and n_iter < max_iter:
n_iter += 1
w = family.weights(mu)
z = v + (family.link.deriv(mu) * (y - mu))
w = np.sqrt(w)
if not isinstance(x, np.ndarray):
w = sp.csr_matrix(w)
z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False)
if wi is None:
n_betas = _compute_betas(wz, wx)
else:
n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
v = spdot(x, n_betas)
mu = family.fitted(v)
if isinstance(family, Poisson):
mu = mu * offset
diff = min(abs(n_betas - betas))
betas = n_betas
if wi is None:
return betas, mu, wx, n_iterspreg
PySAL Spatial Econometric Regression in Python
