rspde.result: rSPDE result extraction from INLA estimation results

In rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations

rSPDE result extraction from INLA estimation results

Description

Extract field and parameter values and distributions for an rspde effect from an inla result object.

Usage

rspde.result(
  inla,
  name,
  rspde,
  compute.summary = TRUE,
  parameterization = "detect",
  n_samples = 5000,
  n_density = 1024
)

Arguments

inla	An inla object obtained from a call to inla().
name	A character string with the name of the rSPDE effect in the inla formula.
rspde	The inla_rspde object used for the effect in the inla formula.
compute.summary	Should the summary be computed?
parameterization	If 'detect', the parameterization from the model will be used. Otherwise, the options are 'spde', 'matern' and 'matern2'.
n_samples	The number of samples to be used if parameterization is different from the one used to fit the model.
n_density	The number of equally spaced points to estimate the density.

Value

If the model was fitted with matern parameterization (the default), it returns a list containing:

marginals.range	Marginal densities for the range parameter
marginals.log.range	Marginal densities for log(range)
marginals.std.dev	Marginal densities for std. deviation
marginals.log.std.dev	Marginal densities for log(std. deviation)
marginals.values	Marginal densities for the field values
summary.log.range	Summary statistics for log(range)
summary.log.std.dev	Summary statistics for log(std. deviation)
summary.values	Summary statistics for the field values

If compute.summary is TRUE, then the list will also contain

summary.kappa	Summary statistics for kappa
summary.tau	Summary statistics for tau

If the model was fitted with the spde parameterization, it returns a list containing:

marginals.kappa	Marginal densities for kappa
marginals.log.kappa	Marginal densities for log(kappa)
marginals.log.tau	Marginal densities for log(tau)
marginals.tau	Marginal densities for tau
marginals.values	Marginal densities for the field values
summary.log.kappa	Summary statistics for log(kappa)
summary.log.tau	Summary statistics for log(tau)
summary.values	Summary statistics for the field values

If compute.summary is TRUE, then the list will also contain

summary.kappa	Summary statistics for kappa
summary.tau	Summary statistics for tau

For both cases, if nu was estimated, then the list will also contain

marginals.nu

Marginal densities for nu

If nu was estimated and a beta prior was used, then the list will also contain

marginals.logit.nu	Marginal densities for logit(nu)
summary.logit.nu	Marginal densities for logit(nu)

If nu was estimated and a truncated lognormal prior was used, then the list will also contain

marginals.log.nu	Marginal densities for log(nu)
summary.log.nu	Marginal densities for log(nu)

If nu was estimated and compute.summary is TRUE, then the list will also contain

summary.nu

Summary statistics for nu

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.