Summary for posteriors of field parameters for an inla_rspde model from a rspde_result object
Source: R/inla_rspde.R
summary.rspde_result.RdSummary for posteriors of rSPDE field parameters in their original scales.
Usage
# S3 method for class 'rspde_result'
summary(object, digits = 6, ...)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)
}
#> Warning: the mean or mode of nu is very close to nu.upper.bound, please consider increasing nu.upper.bound, and refitting the model.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> tau 0.0131116 0.0320314 3.96669e-11 0.00106948 0.101728 3.96669e-11
#> kappa 25.7241000 12.9526000 1.26258e+01 21.90800000 60.807200 1.59920e+01
#> nu 1.5088100 0.4535200 5.68331e-01 1.65194000 1.997740 1.99992e+00
# devel.tag
# }