rSPDE inlabru mapper
Usage
bru_get_mapper.inla_rspde(model, ...)
ibm_n.bru_mapper_inla_rspde(mapper, ...)
ibm_values.bru_mapper_inla_rspde(mapper, ...)
ibm_jacobian.bru_mapper_inla_rspde(mapper, input, ...)
Examples
# \donttest{
# devel version
if (requireNamespace("INLA", quietly = TRUE) &&
requireNamespace("inlabru", quietly = TRUE)) {
library(INLA)
library(inlabru)
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
y <- as.vector(y)
data_df <- data.frame(
y = y, x1 = loc_2d_mesh[, 1],
x2 = loc_2d_mesh[, 2]
)
coordinates(data_df) <- c("x1", "x2")
rspde_model <- rspde.matern(
mesh = mesh_2d,
nu_upper_bound = 2
)
cmp <- y ~ Intercept(1) +
field(coordinates, model = rspde_model)
rspde_fit <- bru(cmp, data = data_df)
summary(rspde_fit)
}
#> Loading required package: sp
#> This is INLA_24.06.27 built 2024-06-27 02:36:04 UTC.
#> - See www.r-inla.org/contact-us for how to get help.
#> - List available models/likelihoods/etc with inla.list.models()
#> - Use inla.doc(<NAME>) to access documentation
#> Loading required package: fmesher
#> inlabru version: 2.11.1
#> INLA version: 24.06.27
#> Components:
#> Intercept: main = linear(1), group = exchangeable(1L), replicate = iid(1L)
#> field: main = cgeneric(coordinates), group = exchangeable(1L), replicate = iid(1L)
#> Likelihoods:
#> Family: 'gaussian'
#> Data class: 'SpatialPointsDataFrame'
#> Predictor: y ~ .
#> Time used:
#> Pre = 0.506, Running = 7.92, Post = 1.08, Total = 9.51
#> Fixed effects:
#> mean sd 0.025quant 0.5quant 0.975quant mode kld
#> Intercept 0.286 0.181 -0.073 0.286 0.65 0.286 0
#>
#> Random effects:
#> Name Model
#> field CGeneric
#>
#> Model hyperparameters:
#> mean sd 0.025quant 0.5quant
#> Precision for the Gaussian observations 168.36 87.289 49.96 151.18
#> Theta1 for field -3.32 0.465 -4.24 -3.31
#> Theta2 for field 2.81 0.246 2.35 2.81
#> Theta3 for field -1.37 0.194 -1.75 -1.37
#> 0.975quant mode
#> Precision for the Gaussian observations 383.772 118.07
#> Theta1 for field -2.407 -3.31
#> Theta2 for field 3.314 2.79
#> Theta3 for field -0.987 -1.37
#>
#> Deviance Information Criterion (DIC) ...............: -122.06
#> Deviance Information Criterion (DIC, saturated) ....: 199.47
#> Effective number of parameters .....................: 96.72
#>
#> Watanabe-Akaike information criterion (WAIC) ...: -144.18
#> Effective number of parameters .................: 55.83
#>
#> Marginal log-Likelihood: -109.91
#> is computed
#> Posterior summaries for the linear predictor and the fitted values are computed
#> (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
#>
# devel.tag
# }