Skip to contents

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

# \donttest{
# devel version
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)
}
#>             mean        sd  0.025quant   0.5quant 0.975quant       mode
#> tau    0.0250738 0.0109131  0.00800786  0.0238808   0.049406  0.0198685
#> kappa 16.9027000 3.4415000 11.36340000 16.4716000  24.822200 15.5774000
#> nu     0.9380740 0.1240890  0.73075800  0.9247460   1.209600  0.8777050
# devel.tag
# }