posterior.predictive.nl {BMAmevt}

Posterior predictive densities in the three dimensional PB, NL and NL3 models

Description

Wrappers for posterior.predictive3D in the PB and NL models.

Usage

posterior.predictive.nl(
  post.sample,
  from = post.sample$Nbin + 1,
  to = post.sample$Nsim,
  thin = 50,
  npoints = 40,
  eps = 0.001,
  equi = T,
  displ = T,
  ...
)

posterior.predictive.pb(
  post.sample,
  from = post.sample$Nbin + 1,
  to = post.sample$Nsim,
  thin = 50,
  npoints = 40,
  eps = 10^(-3),
  equi = T,
  displ = T,
  ...
)

Arguments

post.sample

A posterior sample as returned by posteriorMCMC

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 corresponding to the parameters stored in post.sample. See posterior.predictive3D for details.

Value

A npoints*npoints matrix: the posterior predictive density.

See Also

posterior.predictive3D, posteriorMCMC.pb.