Evaluate the log-likelihood for a given set of parameters - New parametrization + profile likelihood
singl_log_plik(
theta,
.dt,
dists,
npix,
model,
nu = NULL,
kappa = 1,
mu2 = 1.5,
apply_exp = FALSE
)a scalar representing -log.lik.
a vector of size 2 containing the parameters associated
with the model. These parameters are \(\nu\) and \(\phi\),
respectively.
a numeric vector containing the variable \(Y\).
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.
a integer vector containing the number of pixels within
each polygon. (Ordered by the id variables for the polygons).
a character indicating which covariance function to
use. Possible values are c("matern", "pexp", "gaussian",
"spherical", "cs", "gw", "tapmat").
\(\nu\) parameter. Not necessary if mode is
"gaussian" or "spherical"
\(\kappa \in \{0, \ldots, 3 \}\) parameter for the GW cov function.
the smoothness parameter \(\mu\) for the GW function.
a logical indicating whether the exponential
transformation should be applied to variance parameters. This
facilitates the optimization process.
Internal use.