def fit_cor(copula: Copula, data: np.ndarray, typ: str) -> np.ndarray:
"""
Constructs parameter matrix from matrix of Kendall's Taus or Spearman's Rho
Parameters
----------
copula: AbstractCopula
Copula instance
data: ndarray
Data to fit copula with
typ: {'irho', 'itau'}
The type of rank correlation measure to use. 'itau' uses Kendall's tau while 'irho' uses Spearman's rho
Returns
-------
ndarray
Parameter matrix is copula is elliptical. Otherwise, a vector
"""
indices = tri_indices(copula.dim, 1, 'lower')
if typ == 'itau':
tau = kendall_tau(data)[indices]
theta = copula.itau(tau)
elif typ == 'irho':
rho = spearman_rho(data)[indices]
theta = copula.irho(rho)
else:
raise ValueError("Correlation Inversion must be either 'itau' or 'irho'")
if is_elliptical(copula):
theta = near_psd(create_cov_matrix(theta))[indices]
return theta
def fit_cor(copula, data: np.ndarray, typ: str) -> np.ndarray:
"""
Constructs parameter matrix from matrix of Kendall's Taus or Spearman's Rho
Parameters
----------
copula: BaseCopula
Copula instance
data: ndarray
Data to fit copula with
typ: {'irho', 'itau'}
The type of rank correlation measure to use. 'itau' uses Kendall's tau while 'irho' uses Spearman's rho
Returns
-------
ndarray
Parameter matrix is copula is elliptical. Otherwise, a vector
"""
indices = tri_indices(copula.dim, 1, 'lower')
if typ == 'itau':
tau = kendall_tau(data)[indices]
theta = copula.itau(tau)
elif typ == 'irho':
rho = spearman_rho(data)[indices]
theta = copula.irho(rho)
else:
raise ValueError(
"Correlation Inversion must be either 'itau' or 'irho'")
if is_elliptical(copula):
theta = near_psd(create_cov_matrix(theta))[indices]
return theta
def sigma(self):
"""
The covariance matrix for the elliptical copula
:return: numpy array
Covariance matrix for elliptical copula
"""
return create_cov_matrix(self._rhos)
def sigma(self):
"""
The covariance matrix for the elliptical copula
:return: numpy array
Covariance matrix for elliptical copula
"""
return create_cov_matrix(self._rhos)