inla.spde.result {INLA} | R Documentation |
Exctract field and parameter values and distributions for an
inla.spde
SPDE effect from an inla result object.
inla.spde.result(...) inla.spde1.result(inla, name, spde, do.transform = TRUE, ...) ## S3 method for class 'inla.spde1' inla.spde.result(inla, name, spde, do.transform = TRUE, ...) inla.spde2.result(inla, name, spde, do.transform = TRUE, ...) ## S3 method for class 'inla.spde2' inla.spde.result(inla, name, spde, do.transform = TRUE, ...)
... |
Further arguments passed to and from other methods. |
inla |
An |
name |
A character string with the name of the SPDE effect in the inla formula. |
spde |
The |
do.transform |
If |
For inla.spde2
models, a list, where the nominal range and
variance are defined as the values that would have been obtained with a
stationary model and no boundary effects:
marginals.kappa |
Marginal densities for kappa |
marginals.log.kappa |
Marginal densities for log(kappa) |
marginals.log.range.nominal |
Marginal densities for log(range) |
marginals.log.tau |
Marginal densities for log(tau) |
marginals.log.variance.nominal |
Marginal densities for log(variance) |
marginals.range.nominal |
Marginal densities for range |
marginals.tau |
Marginal densities for tau |
marginals.theta
|
Marginal densities for the theta parameters |
marginals.values
|
Marginal densities for the field values |
marginals.variance.nominal
|
Marginal densities for variance |
summary.hyperpar |
The SPDE related part of the inla hyperpar output summary |
summary.log.kappa |
Summary statistics for log(kappa) |
summary.log.range.nominal |
Summary statistics for log(range) |
summary.log.tau |
Summary statistics for log(tau) |
summary.log.variance.nominal |
Summary statistics for log(kappa) |
summary.theta |
Summary statistics for the theta parameters |
summary.values |
Summary statistics for the field values |
Finn Lindgren finn.lindgren@gmail.com
inla.spde.models()
, inla.spde2.matern()
loc <- matrix(runif(100 * 2), 100, 2) mesh <- inla.mesh.create.helper(points.domain = loc, max.edge = c(0.1, 0.5)) spde <- inla.spde2.matern(mesh) index <- inla.spde.make.index("spatial", mesh$n, n.repl = 2) spatial.A <- inla.spde.make.A(mesh, loc, index = rep(1:nrow(loc), 2), repl = rep(1:2, each = nrow(loc)) ) ## Toy example with no spatial correlation (range=zero) y <- 10 + rnorm(100 * 2) stack <- inla.stack( data = list(y = y), A = list(spatial.A), effects = list(c(index, list(intercept = 1))), tag = "tag" ) data <- inla.stack.data(stack, spde = spde) formula <- y ~ -1 + intercept + f(spatial, model = spde, replicate = spatial.repl ) result <- inla(formula, family = "gaussian", data = data, control.predictor = list(A = inla.stack.A(stack)) ) spde.result <- inla.spde.result(result, "spatial", spde) plot(spde.result$marginals.range.nominal[[1]], type = "l")