From a matrix of locations and covariance parameters of the form (variance, range, nugget, <nonstat variance parameters>), return the square matrix of all pairwise covariances.
exponential_nonstat_var(covparms, Z)d_exponential_nonstat_var(covparms, Z)
A matrix with n rows and n columns, with the i,j entry
containing the covariance between observations at locs[i,] and
locs[j,].
A vector with covariance parameters
in the form (variance, range, nugget, <nonstat variance parameters>).
The number of nonstationary variance parameters should equal p.
A matrix with n rows and 2 columns for spatial
locations + p columns describing spatial basis functions.
Each row of locs gives a point in R^2 (two dimensions only!) + the value
of p spatial basis functions.
d_exponential_nonstat_var(): Derivatives with respect to parameters
This covariance function multiplies the isotropic exponential covariance
by a nonstationary variance function. The form of the covariance is
$$ C(x,y) = exp( \phi(x) + \phi(y) ) M(x,y) $$
where M(x,y) is the isotropic exponential covariance, and
$$ \phi(x) = c_1 \phi_1(x) + ... + c_p \phi_p(x) $$
where \(\phi_1,...,\phi_p\) are the spatial basis functions
contained in the last p columns of Z, and
\(c_1,...,c_p\) are the nonstationary variance parameters.