excursions (version 2.5.8)

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

Description

An interface to the contourmap function for latent Gaussian models calculated using the INLA method.

Usage

contourmap.inla(
  result.inla,
  stack,
  name = NULL,
  tag = NULL,
  method = "QC",
  n.levels,
  type = c("standard", "pretty", "equalarea"),
  compute = list(F = TRUE, measures = NULL),
  alpha,
  F.limit,
  n.iter = 10000,
  verbose = FALSE,
  max.threads = 0,
  compressed = TRUE,
  seed = NULL,
  ind,
  ...
)

Value

contourmap.inla returns an object of class "excurobj" with the same elements as returned by contourmap.

Arguments

result.inla

Result object from INLA call.

stack

The stack object used in the INLA call.

name

The name of the component for which to do the calculation. This argument should only be used if a stack object is not provided, use the tag argument otherwise.

tag

The tag of the component in the stack for which to do the calculation. This argument should only be used if a stack object is provided, use the name argument otherwise.

method

Method for handeling the latent Gaussian structure. Currently only Empirical Bayes (EB) and Quantile corrections (QC) are supported.

n.levels

Number of levels in contour map.

type

Type of contour map. One of:

'standard'

Equidistant levels between smallest and largest value of the posterior mean (default).

'pretty'

Equally spaced 'round' values which cover the range of the values in the posterior mean.

'equalarea'

Levels such that different spatial regions are approximately equal in size.

compute

A list with quality indices to compute

'F':

TRUE/FALSE indicating whether the contour map function should be computed (default TRUE)

'measures':

A list with the quality measures to compute ("P0", "P1", "P2") or corresponding bounds based only on the marginal probabilities ("P0-bound", "P1-bound", "P2-bound")

alpha

Maximal error probability in contour map function (default=1)

F.limit

The limit value for the computation of the F function. F is set to NA for all nodes where F<1-F.limit. Default is F.limit = alpha.

n.iter

Number or iterations in the MC sampler that is used for calculating the quantities in compute. The default value is 10000.

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).

compressed

If INLA is run in compressed mode and a part of the linear predictor is to be used, then only add the relevant part. Otherwise the entire linear predictor is added internally (default TRUE).

seed

Random seed (optional).

ind

If only a part of a component should be used in the calculations, this argument specifies the indices for that part (optional).

...

Additional arguments to the contour map function. See the documentation for contourmap for details.

Author

David Bolin

Details

The INLA approximation of the quantity of interest is in general a weighted sum of Gaussian distributions with different parameters. If method = 'EB' is used, then the contour map is computed for the mean of the component in the weighted sum that has parameters with the highest likelihood. If on the other hand method='QC', then the contour map is computed for the posterior mean reported by INLA. If the EB method also is used in INLA, then this reported posterior mean is equal to the mean of the component with the highest likelihood. Therefore, method='EB' is appropriate if the EB method also is used in INLA, but method='QC' should be used in general.

The n.levels contours in the contour map are are placed according to the argument type. A number of quality measures can be computed based based on the specified contour map and the distribution of the component of interest. What should be computed is specified using the compute argument. For details on these quanties, see the reference below.

References

Bolin, D. and Lindgren, F. (2017) Quantifying the uncertainty of contour maps, Journal of Computational and Graphical Statistics, 26:3, 513-524.

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

contourmap, contourmap.mc, contourmap.colors

Examples

if (FALSE) {
if (require.nowarnings("INLA")) {
  # Generate mesh and SPDE model
  n.lattice <- 10 # increase for more interesting, but slower, examples
  x <- seq(from = 0, to = 10, length.out = n.lattice)
  lattice <- fmesher::fm_lattice_2d(x = x, y = x)
  mesh <- fmesher::fm_rcdt_2d_inla(lattice = lattice, extend = FALSE, refine = FALSE)
  spde <- inla.spde2.matern(mesh, alpha = 2)

  # Generate an artificial sample
  sigma2.e <- 0.01
  n.obs <- 100
  obs.loc <- cbind(
    runif(n.obs) * diff(range(x)) + min(x),
    runif(n.obs) * diff(range(x)) + min(x)
  )
  Q <- inla.spde2.precision(spde, theta = c(log(sqrt(0.5)), log(sqrt(1))))
  x <- inla.qsample(Q = Q)
  A <- fmesher::fm_basis(mesh = mesh, loc = obs.loc)
  Y <- as.vector(A %*% x + rnorm(n.obs) * sqrt(sigma2.e))

  ## Estimate the parameters using INLA
  mesh.index <- inla.spde.make.index(name = "field", n.spde = spde$n.spde)
  ef <- list(c(mesh.index, list(Intercept = 1)))

  s.obs <- inla.stack(data = list(y = Y), A = list(A), effects = ef, tag = "obs")
  s.pre <- inla.stack(data = list(y = NA), A = list(1), effects = ef, tag = "pred")
  stack <- inla.stack(s.obs, s.pre)
  formula <- y ~ -1 + Intercept + f(field, model = spde)
  result <- inla(
    formula = formula, family = "normal", data = inla.stack.data(stack),
    control.predictor = list(
      A = inla.stack.A(stack),
      compute = TRUE
    ),
    control.compute = list(
      config = TRUE,
      return.marginals.predictor = TRUE
    ),
    num.threads = 1
  )

  ## Calculate contour map with two levels
  map <- contourmap.inla(result,
    stack = stack, tag = "pred",
    n.levels = 2, alpha = 0.1, F.limit = 0.1,
    max.threads = 1
  )

  ## Plot the results
  cols <- contourmap.colors(map,
    col = heat.colors(100, 1),
    credible.col = grey(0.5, 1)
  )
  image(matrix(map$M[mesh$idx$lattice], n.lattice, n.lattice), col = cols)
}
}