simconf.inla
is used for calculating simultaneous confidence regions
for latent Gaussian models estimated using INLA
.
Usage
simconf.inla(
result.inla,
stack,
name = NULL,
tag = NULL,
ind = NULL,
alpha,
method = "NI",
n.iter = 10000,
verbose = FALSE,
link = FALSE,
max.threads = 0,
compressed = TRUE,
seed = NULL,
inla.sample = TRUE
)
Arguments
- result.inla
Result object from INLA call.
- stack
The stack object used in the INLA call.
- name
The name of the component for which to do the calculation. This argument should only be used if a stack object is not provided, use the tag argument otherwise.
- tag
The tag of the component in the stack for which to do the calculation. This argument should only be used if a stack object is provided, use the name argument otherwise.
- ind
If only a part of a component should be used in the calculations, this argument specifies the indices for that part.
- alpha
Error probability for the region.
- method
Method for handling the latent Gaussian structure:
- 'EB'
Empirical Bayes (Gaussian approximation of posterior).
- 'NI'
Numerical integration (Calculation based on the Gaussian mixture approximation of the posterior, as calculated by INLA).
- n.iter
Number or iterations in the MC sampler that is used for approximating probabilities. The default value is 10000.
- verbose
Set to TRUE for verbose mode (optional).
- link
Transform output to the scale of the data using the link function as defined in the model estimated with INLA (default FALSE).
- max.threads
Decides the number of threads the program can use. Set to 0 for using the maximum number of threads allowed by the system (default).
- compressed
If INLA is run in compressed mode and a part of the linear predictor is to be used, then only add the relevant part. Otherwise the entire linear predictor is added internally (default TRUE).
- seed
Random seed (optional).
- inla.sample
Set to TRUE if inla.posterior.sample should be used for the MC integration.
Value
An object of class "excurobj" with elements
- a
The lower bound.
- b
The upper bound.
- a.marginal
The lower bound for pointwise confidence bands.
- b.marginal
The upper bound for pointwise confidence bands.
Details
See simconf()
for details.
Note
This function requires the INLA
package, which is not a CRAN package.
See https://www.r-inla.org/download-install for easy installation instructions.
References
Bolin et al. (2015) Statistical prediction of global sea level from global temperature, Statistica Sinica, vol 25, pp 351-367.
Bolin, D. and Lindgren, F. (2018), Calculating Probabilistic Excursion Sets and Related Quantities Using excursions, Journal of Statistical Software, vol 86, no 1, pp 1-20.
Author
David Bolin davidbolin@gmail.com
Examples
if (FALSE) { # \dontrun{
if (require.nowarnings("INLA")) {
n <- 10
x <- seq(0, 6, length.out = n)
y <- sin(x) + rnorm(n)
mu <- 1:n
result <- inla(y ~ 1 + f(mu, model = "rw2"),
data = list(y = y, mu = mu), verbose = FALSE,
control.compute = list(
config = TRUE,
return.marginals.predictor = TRUE
),
num.threads = "1:1"
)
res <- simconf.inla(
result, name = "mu", alpha = 0.05,
max.threads = 1, num.threads = "1:1")
plot(result$summary.random$mu$mean, ylim = c(-2, 2))
lines(res$a)
lines(res$b)
lines(res$a.marginal, col = "2")
lines(res$b.marginal, col = "2")
}
} # }