posterior.predictive3D {BMAmevt} | R Documentation |
Computes an approximation of the predictive density based on a posterior parameters sample. Only allowed in the three-dimensional case.
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,
...
)
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 |
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.
A npoints*npoints
matrix: the posterior predictive density.
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.
Anne Sabourin