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.
degrees of freedom for the t distribution in the model; df=-99 corresponds to a normal distribution.
is the starting value for the sequence of values of the precision parameter M at which to compute the Bayes factor.
is the amount by which to increment the values of M.
is the ending value for the sequence of values of M.
is the vector of nine constants computed by
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
multiple-chain version of Equation (2.7) of Burr (2012); the details
of the multiple-chain algorithm are given in Doss (2012).
Previous steps are calls to
again, in that order, as illustrated in the Examples section and in
A list with three named components,
yinfinity, needed to produce the plot of Bayes factors via the
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
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 semi-parametric models for meta-analysis.” 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 Radon-Nikodym 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.