likfit.dist: Fit of gaussian field. Modified from function likfit in geoR.
In statnmap/GeoDist: Constrained distance calculation and associated geotools

Fit of gaussian field. Modified from function likfit in geoR.

likfit.dist(
  geodata,
  coords = geodata$coords,
  data = geodata$data,
  trend = "cte",
  ini.cov.pars,
  fix.nugget = FALSE,
  nugget = 0,
  fix.kappa = TRUE,
  kappa = 0.5,
  fix.lambda = TRUE,
  lambda = 1,
  fix.psiA = TRUE,
  psiA = 0,
  fix.psiR = TRUE,
  psiR = 1,
  cov.model,
  dist.mat,
  realisations,
  lik.method = "ML",
  components = TRUE,
  nospatial = TRUE,
  limits = pars.limits(),
  print.pars = FALSE,
  messages,
  ...
)

`geodata`	a list containing elements `coords` and `data` as described next. Typically an object of the class `"geodata"`. If not provided the arguments `coords` and `data` must be provided instead.
`coords`	an n x 2 matrix where each row has the 2-D coordinates of the n data locations. By default it takes the component `coords` of the argument `geodata`, if provided.
`data`	a vector with n data values. By default it takes the component `data` of the argument `geodata`, if provided.
`trend`	specifies the mean part of the model. See documentation of `trend.spatial` for further details. Defaults to `"cte"`.
`ini.cov.pars`	initial values for the covariance parameters: sigma^2 (partial sill) and phi (range parameter). Typically a vector with two components. However a matrix can be used to provide several initial values. See `DETAILS` below.
`fix.nugget`	logical, indicating whether the parameter tau^2 (nugget variance) should be regarded as fixed (`fix.nugget = TRUE`) or should be estimated (`fix.nugget = FALSE`). Defaults to `FALSE`.
`nugget`	value of the nugget parameter. Regarded as a fixed value if `fix.nugget = TRUE` otherwise as the initial value for the minimisation algorithm. Defaults to zero.
`fix.kappa`	logical, indicating whether the extra parameter kappa should be regarded as fixed (`fix.kappa = TRUE`) or should be estimated (`fix.kappa = FALSE`). Defaults to `TRUE`.
`kappa`	value of the extra parameter kappa. Regarded as a fixed value if `fix.kappa = TRUE` otherwise as the initial value for the minimisation algorithm. Defaults to 0.5. This parameter is valid only if the covariance function is one of: `"matern"`, `"powered.exponential"`, `"cauchy"` or `"gneiting.matern"`. For more details on covariance functions see documentation for `cov.spatial`.
`fix.lambda`	logical, indicating whether the Box-Cox transformation parameter lambda should be regarded as fixed (`fix.lambda = TRUE`) or should be be estimated (`fix.lambda = FALSE`). Defaults to `TRUE`.
`lambda`	value of the Box-Cox transformation parameter lambda. Regarded as a fixed value if `fix.lambda = TRUE` otherwise as the initial value for the minimisation algorithm. Defaults to 1. Two particular cases are lambda = 1 indicating no transformation and lambda = 0 indicating log-transformation.
`fix.psiA`	logical, indicating whether the anisotropy angle parameter psi_R should be regarded as fixed (`fix.psiA = TRUE`) or should be estimated (`fix.psiA = FALSE`). Defaults to `TRUE`.
`psiA`	value (in radians) for the anisotropy angle parameter psi_A. Regarded as a fixed value if `fix.psiA = TRUE` otherwise as the initial value for the minimisation algorithm. Defaults to 0. See `coords.aniso` for further details on anisotropy correction.
`fix.psiR`	logical, indicating whether the anisotropy ratio parameter psi_R should be regarded as fixed (`fix.psiR = TRUE`) or should be estimated (`fix.psiR = FALSE`). Defaults to `TRUE`.
`psiR`	value, always greater than 1, for the anisotropy ratio parameter psi_R. Regarded as a fixed value if `fix.psiR = TRUE` otherwise as the initial value for the minimisation algorithm. Defaults to 1. See `coords.aniso` for further details on anisotropy correction.
`cov.model`	a string specifying the model for the correlation function. For further details see documentation for `cov.spatial`. Reads values from an `variomodel` object passed to `ini.cov.pars` if any, otherwise defaults to the exponential model.
`dist.mat`	Square matrix of distances between data points
`realisations`	optional. Logical or a vector indicating the number of replication for each datum. For further information see `DETAILS` below and documentation for `as.geodata`.
`lik.method`	(formely method.lik) options are `"ML"` for maximum likelihood and `"REML"` for restricted maximum likelihood. Defaults to `"ML"`.
`components`	an n x 3 data-frame with fitted values for the three model components: trend, spatial and residuals. See the section `DETAILS` below for the model specification.
`nospatial`	logical. If `TRUE` parameter estimates for the model without spatial component are included in the output.
`limits`	values defining lower and upper limits for the model parameters used in the numerical minimisation. The auxiliary function `pars.limits` is called to set the limits. See also Limits in DETAILS below.
`print.pars`	logical. If `TRUE` the parameters and the value of the negative log-likelihood (up to a constant) are printed each time the function to be minimised is called.
`messages`	logical. Indicates whether status messages should be printed on the screen (or output device) while the function is running.
`...`	additional parameters to be passed to the minimisation function. Typically arguments of the type `control()` which controls the behavior of the minimisation algorithm. For further details see documentation for the minimisation function `optim`.