singl_log_lik {smile} | R Documentation |
Evaluate log-lik
Description
Evaluate the log-likelihood for a given set of parameters
Usage
singl_log_lik(
theta,
.dt,
dists,
npix,
model,
nu = NULL,
tr = NULL,
kappa = 1,
mu2 = 1.5,
apply_exp = FALSE
)
Arguments
theta |
a numeric vector of size 4 (\mu, \sigma^2, \alpha,
\phi ) containing the parameters associated with the model.
|
.dt |
a numeric vector containing the variable Y .
|
dists |
a list of size distance matrices at the point level.
|
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 model 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.
|
Details
Internal use.
Value
a scalar representing -log.lik
.
[Package
smile version 1.0.5
Index]