posterior.predictive3D {BMAmevt}  R Documentation 
Posterior predictive density on the simplex, for threedimensional extreme value models.
Description
Computes an approximation of the predictive density based on a posterior parameters sample. Only allowed in the threedimensional 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 
densityGrid 
A function returning a 
from 
Integer or 
to 
Integer or 
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 
displ 
logical. Should a plot be produced ? 
... 
Additional graphical parameters and arguments to be passed
to 
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 todgridplot
. 
displ
: logical. Should a plot be produced ? 
invisible
: logical. Should the result be returned asinvisible
? 
...
additional arguments to be passed todgridplot
Only a subsample 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