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

## 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?

## 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

```
#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 <- 0.01
range <- 0.2
nu <- 0.8
kappa <- sqrt(8 * nu) / range
op <- matern.operators(
mesh = mesh_2d, nu = nu,
kappa = kappa, sigma = sigma, m = 2
)
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)),
inla.mode = "experimental"
)
result <- rspde.result(rspde_fit, "field", rspde_model)
summary(result)
plot(result)
}
#> Error in inla.inlaprogram.has.crashed() :
#> The inla-program exited with an error. Unless you interupted it yourself, please rerun with verbose=TRUE and check the output carefully.
#> If this does not help, please contact the developers at <help@r-inla.org>.
#>
#> *** inla.core.safe: inla.program has crashed: rerun to get better initial values. try=1/2
#>
#> *** inla.core.safe: rerun with improved initial values
#devel.tag
```