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

singl_log_plik

R Documentation

Evaluate log-lik

Evaluate the log-likelihood for a given set of parameters - New parametrization + profile likelihood

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

`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.