simconf.mixture {excursions}

Simultaneous confidence regions for Gaussian mixture models

Description

simconf.mixture is used for calculating simultaneous confidence regions for Gaussian mixture models. The distribution for the process x is assumed to be

\pi(x) = \sum_{k=1}^K w_k N(\mu_k, Q_k^{-1}).

The function returns upper and lower bounds a and b such that P(a<x<b) = 1-\alpha.

Usage

simconf.mixture(
  alpha,
  mu,
  Q,
  w,
  ind,
  n.iter = 10000,
  vars,
  verbose = 0,
  max.threads = 0,
  seed = NULL,
  mix.samp = TRUE
)

Arguments

alpha

Error probability for the region.

mu

A list with the k expectation vectors \mu_k.

Q

A list with the k precision matrices Q_k.

w

A vector with the weights for each class in the mixture.

ind

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

n.iter

Number or iterations in the MC sampler that is used for approximating probabilities. The default value is 10000.

vars

A list with precomputed marginal variances for each class (optional).

verbose

Set to TRUE for verbose mode (optional).

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).

seed

Random seed (optional).

mix.samp

If TRUE, the MC integration is done by directly sampling the mixture, otherwise sequential integration is used.

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]

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.

See Also

simconf, simconf.inla, simconf.mc

Examples

n <- 11
K <- 3
mu <- Q <- list()
for (k in 1:K) {
  mu[[k]] <- k * 0.1 + seq(-5, 5, length = n)
  Q[[k]] <- Matrix(toeplitz(c(1, -0.1, rep(0, n - 2))))
}
## calculate the confidence region
conf <- simconf.mixture(0.05, mu, Q, w = rep(1 / 3, 3), max.threads = 2)

## Plot the region
plot(mu[[1]], type = "l")
lines(mu[[2]])
lines(mu[[3]])
lines(conf$a, col = 2)
lines(conf$b, col = 2)