simconf.mixture {excursions} | R Documentation |
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 |
Q |
A list with the |
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 davidbolin@gmail.com
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)