inla.spde.result: SPDE result extraction from INLA estimation results

In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Description

Exctract field and parameter values and distributions for an inla.spde SPDE effect from an inla result object.

Usage

inla.spde.result(...)

## S3 method for class 'inla.spde1'
inla.spde.result(inla, name, spde, do.transform = TRUE, ...)
## S3 method for class 'inla.spde2'
inla.spde.result(inla, name, spde, do.transform = TRUE, ...)

## Direct function call for class 'inla.spde1':
inla.spde1.result(inla, name, spde, do.transform = TRUE, ...)
## Direct function call for class 'inla.spde2':
inla.spde2.result(inla, name, spde, do.transform = TRUE, ...)

Arguments

inla	An inla object obtained from a call to inla
name	A character string with the name of the SPDE effect in the inla formula.
spde	The inla.spde object used for the effect in the inla formula. (Note: this could have been stored in the inla output, but isn't.) Usually the result of a call to inla.spde2.matern.
do.transform	If TRUE, also calculate marginals transformed to user-scale. Setting to FALSE is useful for large non-stationary models, as transforming many marginal densities is time-consuming.
...	Further arguments passed to and from other methods.

Value

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

Author(s)

Finn Lindgren [e-mail]

See Also

inla.spde.models, inla.spde2.matern

Examples

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")

andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.