posteriorMCMC.nl {BMAmevt} | R Documentation |
The functions generate parameters samples approximating the posterior distribution in the PB model or the NL model.
posteriorMCMC.nl(Nsim, dat, Hpar, MCpar, ...) posteriorMCMC.pb(Nsim, dat, Hpar, MCpar, ...)
Nsim |
Total number of iterations to perform. |
dat |
An angular data set, e.g. constructed by
|
Hpar |
A list containing Hyper-parameters to be passed to
|
MCpar |
A list containing MC MC tuning parameters to be
passed to |
... |
Additional arguments to be passed to
|
The two functions are wrappers simplifying the use of
posteriorMCMC
for the two models implemented in this package.
an object with class attributes "postsample"
and
"PBNLpostsample"
: The posterior sample and some statistics
as returned by function posteriorMCMC
For the Leeds data set, and for simulated data sets with
similar features, setting Nsim=50e+3
and Nbin=15e+3
is enough (possibly too much),
with respect to the Heidelberger and Welch tests implemented in
heidel.diag
.
## Not run: data(Leeds) data(pb.Hpar) data(pb.MCpar) data(nl.Hpar) data(nl.MCpar) pPB <- posteriorMCMC.pb(Nsim=5e+3, dat=Leeds, Hpar=pb.Hpar, MCpar=pb.MCpar) dim(pPB[1]) pPB[-(1:3)] pNL <- posteriorMCMC.nl(Nsim=5e+3, dat=Leeds, Hpar=nl.Hpar, MCpar=nl.MCpar) dim(pNL[1]) pNL[-(1:3)] ## End(Not run)