singl_log_lik: Evaluate log-lik
In lcgodoy/smile: Spatial Misalignment: Interpolation, Linkage, and Estimation

singl_log_lik

R Documentation

Evaluate log-lik

Evaluate the log-likelihood for a given set of parameters

singl_log_lik(
  theta,
  .dt,
  dists,
  npix,
  model,
  nu = NULL,
  tr = NULL,
  kappa = 1,
  mu2 = 1.5,
  apply_exp = FALSE
)

`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.