BasicResultExtraction: Transform covariates to new grid

In PointProcess/SealPupProduction-JRSSC-code:

Description

Transform a covariate grid of class 'im' to a new grid of covariate values, along with only those values being within a specified counting domain. Note: This function should also be used if covariates are not present to simplify later computations.

Usage

BasicResultExtraction(
  pp.res,
  inlaPrepList,
  projgrid,
  use.covariates = TRUE,
  covariate.fitting = "quadratic",
  additional.iid.term = TRUE,
  covariateValues,
  logicalGridPointsInsideCountingDomain,
  nxy,
  extraNonlinear.covGridList
)

Arguments

pp.res	inla object (being the output of running the inla function)
inlaPrepList	list of data provided to the inla function (being the output from either PrepareINLAFuncContLikApprox or PrepareINLAFunc)
projgrid	inla.mesh.projector object (being the output from running inla.mesh.projector)
use.covariates	Logical, indicating whether covariates are used or not (see description!)
covariate.fitting	String, indicating how to model covariates. "linear", quadratic (default) or "linAndLog", or FALSE for no covariates
additional.iid.term	Logical, indicating whether to include an additional iid (Gaussian) term in the latent field specification. FALSE is default
covariateValues,	Matrix giving the covariate values on the grid
logicalGridPointsInsideCountingDomain	Logical vector, indicating which of the grid elements are within the counting domain
nxy	Numberic vector of size 2, giving the dimension in x- and y-direction for the grid
extraNonlinear.covGridList	List with additional projection object and such for nonlinear covariate effect, when applicable

Value

List with several results requiring minimal interaction with inla object

PointProcess/SealPupProduction-JRSSC-code documentation built on Jan. 27, 2020, 10:06 p.m.