Description Usage Arguments Details Value References Examples
This function carries out the final step in computing Bayes factors for comparing a sequence of values of the Dirichlet precision parameter M for the ordinary Dirichlet mixing model.
1 
df 
degrees of freedom for the t distribution in the model; df=99 corresponds to a normal distribution. 
from 
is the starting value for the sequence of values of the precision parameter M at which to compute the Bayes factor. 
incr 
is the amount by which to increment the values of M. 
to 
is the ending value for the sequence of values of M. 
cc 
is the vector of nine constants computed by 
mat.list 
list of nine matrices of MCMC output produced by

This function carries out the fourth and final step in the computation
of Bayes factors for the selection of M in the ordinary
Dirichlet mixing model. In the current version of the package, the
Bayes factors for M are computed relative to
the model with M=4. The sequence of steps implements a
multiplechain version of Equation (2.7) of Burr (2012); the details
of the multiplechain algorithm are given in Doss (2012).
Previous steps are calls to bf1
, bf2
, and bf1
again, in that order, as illustrated in the Examples section and in
Burr (2012).
A list with three named components, Mnew
, y
, and
yinfinity
, needed to produce the plot of Bayes factors via the
function draw.bf
. The vector Mnew
is the sequence of
(finite) values of M. The vector y
is the estimates of
the Bayes factors corresponding to the finite values of Mnew
,
and the object yinfinity
is the value of the Bayes factor for
M at infinity, that is, for the parametric model.
Burr, Deborah (2012). “bspmma: An R package for Bayesian semiparametric models for metaanalysis.” Journal of Statistical Software 50(4), 1–23. http://www.jstatsoft.org/v50/i04/.
Doss, Hani (2012). “Hyperparameter and model selection for nonparametric Bayes problems via RadonNikodym derivatives.” Statistica Sinica 22, 1–26.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Not run:
## CPU times are from runs of the R command system.time() on an
## Intel $2.8$ GHz Q$9550$ running Linux.
## Preliminary steps
data(breast.17) # the breast cancer dataset
breast.data < as.matrix(breast.17) # put data in matrix object
chain1.list < bf1(breast.data) # 40.5 secs
cc < bf2(chain1.list) # 1.6 secs
## Next get a second set of 9 chains, with a different seed
chain2.list < bf1(breast.data,seed=2) # 40.4 secs
## Compute and plot the Bayes factors
breast.bfo < bf.o(to=20, cc=cc, mat.list=chain2.list) #51 secs
draw.bf(breast.bfo)
## End(Not run)

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