# bf.c.o: Compute Bayes Factors for Conditional vs. Ordinary Dirichlet... In bspmma: Bayesian Semiparametric Models for Meta-Analysis

## Description

This function carries out the final step in computing Bayes factors for comparing conditional and ordinary Dirichlet mixing models, for a sequence of Dirichlet precision parameters M.

## Usage

 `1` ```bf.c.o(df=-99, from=.4, incr=.1, to, cc, mat.list) ```

## Arguments

 `df` degrees of freedom for the t distribution in the model; df=-99 corresponds to a normal distribution. `cc` is the vector of nine constants computed by `bf1` and `bf2`. `from` is the starting value for the sequence of values of the precision parameter M at which to compute the Bayes factor. `to` is the ending value for the sequence of values of M. `incr` is the amount by which to increment the values of M. `mat.list` list of nine matrices of MCMC output produced by `bf1` for the final computation of the Bayes factors.

## Details

This function carries out the fourth and final step in the computation of Bayes factors for the conditional vs. ordinary Dirichlet mixing models. It 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 two calls to `bf1` and a call to `bf2`, as illustrated in the Examples section and in Burr (2012).

## Value

A list with two named components, `Mnew` and `y`. The vector `Mnew` is the sequence of (finite) values of M. The vector `y` is the estimates of the Bayes factors corresponding to `Mnew`.

## References

Burr, Deborah (2012). “bspmma: An R package for Bayesian semiparametric 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 Sinica22, 1–26.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```## 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 ## OR load the chains and constants saved earlier load("breast-rdat-2lists-1000") load("breast-rdat-2lists-1000") ## Compute and plot the Bayes factors breast.bfco <- bf.c.o(to=20, cc=cc, mat.list=chain2.list) # 107 secs draw.bf(breast.bfco) ## End(Not run) ```

bspmma documentation built on May 2, 2019, 6:50 a.m.