fit.lgcp: Function to fit a spatio-temporal log-Gaussian Cox process...

In cmjt/lgcpSPDE: Fitting and Simulation for log-Gaussian Cox processes using INLA/SPDE approach

Description

Function to fit a spatio-temporal log-Gaussian Cox process model with an intercept and covariates (optional)

Usage

fit.lgcp(mesh = NULL, mesh.pars = NULL, locs = NULL, temp = NULL,
  covariates = NULL, prior.rho = list(theta = list(prior = "pccor1", param =
  c(0, 0.9))), prior.range = c(400, 0.5), prior.sigma = c(1, 0.05),
  verbose = FALSE, control.inla = list(strategy = "gaussian", int.strategy =
  "eb"), control.fixed = list(prec.intercept = 0.001),
  return.attributes = FALSE, ns = NULL)

Arguments

mesh	a “mesh” object i.e. delauney triangulation of the domain, an object returned by make.mesh.
mesh.pars	a named vertor of mesh parameters, must contain cutoff length at which to cut off triangle edge lengths, min triangle edge length inside region, and max triangle edge length outside region.
locs	a matrix of observation locations, where each row corresponds to the observation.
temp	a numeric vector specifying a temporal index for each observation (starting at 1.....T).
covariates	a named data.frame of covariates
prior.rho	prior for the temporal correlation coefficient, by default a pcprior is used with param=c(0,0.9).
prior.range	pc prior for the range of the latent field (rnage0,Prange) (i.e., P(range < range0) = Prange NOTE should be changed to reflect range of the domain by default this is 400m
prior.sigma	pc prior for the sd of the latent field (sigma0,Psigma) by default c(1,0.05) i.e., prob sigma > 1 = 0.05
verbose	Logical if TRUE model fit is output to screen.
control.inla	a list to control model fitting (as per inla)
control.fixed	a list as per inla by default sets prior for precision intercept
response	a vector of response variable, each corresponds to the spatial locations in locs.

Value

A inla result object

cmjt/lgcpSPDE documentation built on July 25, 2019, 3:05 p.m.