R: Posterior predictive density on the simplex, for...

posterior.predictive3D {BMAmevt}

R Documentation

Posterior predictive density on the simplex, for three-dimensional extreme value models.

Description

Computes an approximation of the predictive density based on a posterior parameters sample. Only allowed in the three-dimensional case.

Usage

posterior.predictive3D(
  post.sample,
  densityGrid,
  from = post.sample$Nbin + 1,
  to = post.sample$Nsim,
  thin = 40,
  npoints = 40,
  eps = 10^(-3),
  equi = T,
  displ = T,
  ...
)

Arguments

`post.sample`	A posterior sample as returned by `posteriorMCMC`
`densityGrid`	A function returning a `npointsnpoints` matrix, representing a discretized version of the spectral density on the two dimensional simplex. The function should be compatible with `dgridplot`. In particular, it must use `discretize` to produce the discretization grid. It must be of type `function(par, npoints, eps, equi, displ,invisible, ... )`. See Details* below.
`from`	Integer or `NULL`. If `NULL`, the default value is used. Otherwise, should be greater than `post.sample$Nbin`. Indicates the index where the averaging process should start. Default to `post.sample$Nbin +1`
`to`	Integer or `NULL`. If `NULL`, the default value is used. Otherwise, must be lower than `Nsim+1`. Indicates where the averaging process should stop. Default to `post.sample$Nsim`.
`thin`	Thinning interval.
`npoints`	The number of grid nodes on the squared grid containing the desired triangle.
`eps`	Positive number: minimum distance from any node inside the simplex to the simplex boundary
`equi`	logical. Is the simplex represented as an equilateral triangle (if `TRUE`) or a right triangle (if `FALSE`) ?
`displ`	logical. Should a plot be produced ?
`...`	Additional graphical parameters and arguments to be passed to `contour` and `image`.

Details

The posterior predictive density is approximated by averaging the densities produced by the function densityGrid(par, npoints, eps, equi, displ,invisible, ...) for par in a subset of the parameters sample stored in post.sample. The arguments of densityGrid must be

par: A vector containing the parameters.
npoints, eps, equi: Discretization parameters to be passed to dgridplot.
displ: logical. Should a plot be produced ?
invisible: logical. Should the result be returned as invisible ?
... additional arguments to be passed to dgridplot

Only a sub-sample is used: one out of thin parameters is used (thinning). Further, only the parameters produced between time from and time to (included) are kept.

Value

A npoints*npoints matrix: the posterior predictive density.

Note

The computational burden may be high: it is proportional to npoints^2. Therefore, the function assigned to densityGridplot should be optimized, typically by calling .C with an internal, user defined C function.

Author(s)

Anne Sabourin