rspde.make.index: rSPDE model index vector generation

In rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations

rSPDE model index vector generation

Description

Generates a list of named index vectors for an rSPDE model.

Usage

rspde.make.index(
  name,
  n.spde = NULL,
  n.group = 1,
  n.repl = 1,
  mesh = NULL,
  rspde.order = 2,
  nu = NULL,
  dim = NULL
)

Arguments

name	A character string with the base name of the effect.
n.spde	The number of basis functions in the mesh model.
n.group	The size of the group model.
n.repl	The total number of replicates.
mesh	An inla.mesh, an inla.mesh.1d object or a metric_graph object.
rspde.order	The order of the rational approximation
nu	If NULL, then the model will assume that nu will be estimated. If nu is fixed, you should provide the value of nu.
dim	the dimension of the domain. Should only be provided if mesh is not provided.

Value

A list of named index vectors.

name	Indices into the vector of latent variables
name.group	'group' indices
name.repl	Indices for replicates

Examples

 #tryCatch version
tryCatch({
if (requireNamespace("INLA", quietly = TRUE)){
library(INLA)

set.seed(123)

m <- 100
loc_2d_mesh <- matrix(runif(m * 2), m, 2)
mesh_2d <- inla.mesh.2d(
  loc = loc_2d_mesh,
  cutoff = 0.05,
  max.edge = c(0.1, 0.5)
)
sigma <- 1
range <- 0.2
nu <- 0.8
kappa <- sqrt(8 * nu) / range
op <- matern.operators(
  mesh = mesh_2d, nu = nu,
  range = range, sigma = sigma, m = 2,
  parameterization = "matern"
)
u <- simulate(op)
A <- inla.spde.make.A(
  mesh = mesh_2d,
  loc = loc_2d_mesh
)
sigma.e <- 0.1
y <- A %*% u + rnorm(m) * sigma.e
Abar <- rspde.make.A(mesh = mesh_2d, loc = loc_2d_mesh)
mesh.index <- rspde.make.index(name = "field", mesh = mesh_2d)
st.dat <- inla.stack(
  data = list(y = as.vector(y)),
  A = Abar,
  effects = mesh.index
)
rspde_model <- rspde.matern(
  mesh = mesh_2d,
  nu.upper.bound = 2
)
f <- y ~ -1 + f(field, model = rspde_model)
rspde_fit <- inla(f,
  data = inla.stack.data(st.dat),
  family = "gaussian",
  control.predictor =
    list(A = inla.stack.A(st.dat))
)
result <- rspde.result(rspde_fit, "field", rspde_model)
summary(result)
}
#stable.tryCatch
}, error = function(e){print("Could not run the example")})

rSPDE documentation built on Nov. 6, 2023, 1:06 a.m.