SpatialModelEstimator: SpatialModelEstimator

In Craig44/spatialsurvey: Gives access to a range of Design and Model-based estimators for two dimensional systematic surveys, particulraly systemtic spatial surveys

SpatialModelEstimator

Description

SpatialModelEstimator

Usage

SpatialModelEstimator(
  spatial_df,
  formula,
  mesh,
  extrapolation_grid,
  family,
  link,
  bias_correct = F,
  control = list(eval.max = 10000, iter.max = 10000),
  convergence = list(grad_tol = 0.001),
  start_par_list = NULL,
  trace_level = "none"
)

Arguments

spatial_df	data.frame
formula	class(formula) e.g. formula = y ~ 1
mesh	an inla.mesh object that has been created before this function is applied
extrapolation_grid	data.frame we expect columns names "x", "y", "area"
family	0 = Poisson, 1 = Negative Binomial, 2 = Gaussian, 3 = Gamma
link	link function 0 = log, 1 = logit, 2 = probit, 3 = inverse, 4 = identity
bias_correct	<bool> whether bias correction is needed in deriving expectations and standard errors for
control	list of settings for nlminb
convergence	list of convergence setting to check
start_par_list	list of inital values for optimisaton, if you have conversion issues can be useful to look at alternative starting values
trace_level	'none' don't print any information, 'low' print steps in the function 'medium' print gradients of TMB optimisation, 'high' print parameter candidates as well as gradients during oprimisation.

Details

Return Estimates and objects from a model based GMRF approach

Value

: list of estimated objects and data objects

Craig44/spatialsurvey documentation built on June 28, 2022, 8:35 p.m.