excursions (version 2.0.6)

contourmap: Contour maps and contour map quality measures for latent Gaussian models

Description

contourmap is used for calculating contour maps and quality measures for contour maps for latent Gaussian models.

Usage

contourmap(mu,
           Q,
           vars,
           n.levels,
           ind,
           levels,
           type = c("standard",
                    "equalarea",
                    "P0-optimal",
                    "P1-optimal",
                    "P2-optimal"),
           compute = list(F=TRUE, measures = NULL),
           use.marginals=TRUE,
           alpha,
           F.limit,
           n.iter=10000,
           verbose=FALSE,
           max.threads=0,
           seed=NULL)

Arguments

mu

Expectation vector

Q

Precision matrix

vars

Precomputed marginal variances (optional)

n.levels

Number of levels in contour map

ind

Indices of the nodes that should be analyzed (optional)

levels

Levels to use in contour map

type

Type of contour map. One of:

• 'standard'

{Equidistant levels between min(mu) and max(mu) (default)} 'equalarea'{Levels such that different spatial regions are approximately equal in size} 'P0-optimal'{

Value

• contourmap returns an object of class "excurobj". This is a list that can contains the following arguments:
• u
  Contour levels
• n.levels
  The number of contours used
• u.e
  The values associated with the level sets G_k
• G
  A vector which shows which of the level sets G_k each node belongs to.
• map
  Representation of the contour map with map[i]=u.e[k] if i is in G_k
• F
  The contour map function (if computed)
• M
  Contour avoiding sets (if F is computed). $M=-1$ for all non-significant nodes and $M=k$ for nodes that belong to $M_k$.
• P0/P1/P2
  Calculated quality measures (if computed)
• P0bound/P1bound/P2bound
  Calculated upper bounds quality measures (if computed)
• meta
  A list containing various information about the calculation.

item

• compute
• 'measures':
• use.marginals
• alpha
• F.limit
• n.iter
• verbose
• max.threads
• seed

itemize

• 'F':

code

alpha

References

Bolin, D. and Lindgren, F. (2014) Quantifying the uncertainty of contour maps, ArXiv preprint

Examples

n = 10
  Q = Matrix(toeplitz(c(1, -0.5, rep(0, n-2))))
  mu = seq(-5, 5, length=n)
  lp <- contourmap(mu,Q,n.levels = 2, compute=list(F=FALSE, measures = c("P1","P2")),max.threads=1)