simconf.mc: Simultaneous confidence regions using Monte Carlo samples

In finnlindgren/excursions: Excursion Sets and Contour Credibility Regions for Random Fields

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).

Details

See simconf for details.

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.

Author(s)

David Bolin [e-mail]

See Also

simconf, simconf.inla

Examples

## 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)

finnlindgren/excursions documentation built on Jan. 17, 2021, 12:20 a.m.