excursions (version 2.4.1)

simconf.mc: Simultaneous confidence regions using Monte Carlo samples

Description

simconf.mc is used for calculating simultaneous confidence regions based on Monte Carlo samples. The function returns upper and lower bounds \(a\) and \(b\) such that \(P(a<x<b) = 1-alpha\).

Usage

simconf.mc(samples, alpha, ind, verbose = FALSE)

Arguments

samples

Matrix with model Monte Carlo samples. Each column contains a sample of the model.

alpha

Error probability for the region.

ind

Indices of the nodes that should be analyzed (optional).

verbose

Set to TRUE for verbose mode (optional).

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.

See Also

simconf, simconf.inla

Examples

# NOT RUN {
## Create mean and a tridiagonal precision matrix
n = 11
mu.x = seq(-5, 5, length=n)
Q.x = Matrix(toeplitz(c(1, -0.1, rep(0, n-2))))
## Sample the model 100 times (increase for better estimate)
X = mu.x + solve(chol(Q.x),matrix(rnorm(n=n*100),nrow=n,ncol=100))
## calculate the confidence region
conf = simconf.mc(X,0.2)
## Plot the region
plot(mu.x, type="l", ylim=c(-10, 10),
     main='Mean (black) and confidence region (red)')
lines(conf$a, col=2)
lines(conf$b, col=2)
# }