Evaluate the log-likelihood for a given set of parameters - New
parametrization + profile likelihood
theta |
a vector of size 2 containing the parameters associated
with the model. These parameters are \nu and \phi ,
respectively.
|
.dt |
a numeric vector containing the variable Y .
|
dists |
a list of size three. The first containing the distance
matrices associated with the regions where Y was measured, the
second for the distance matrices associated with X , and the last
containing the cross-distance matrices.
|
npix |
a integer vector containing the number of pixels within
each polygon. (Ordered by the id variables for the polygons).
|
model |
a character indicating which covariance function to
use. Possible values are c("matern", "pexp", "gaussian",
"spherical", "cs", "gw", "tapmat") .
|
nu |
\nu parameter. Not necessary if mode is
"gaussian" or "spherical"
|
tr |
\theta_r taper range.
|
kappa |
\kappa \in \{0, \ldots, 3 \} parameter for the GW cov
function.
|
mu2 |
the smoothness parameter \mu for the GW function.
|
apply_exp |
a logical indicating whether the exponential
transformation should be applied to variance parameters. This
facilitates the optimization process.
|
Internal use.