describe.post: Brief summary statistics of the posterior for convenient...

In bspmma: Bayesian Semiparametric Models for Meta-Analysis

Description

Compute, print posterior means and posterior P(odds ratio < 1) for the individual study parameters and hyperparameters of the model.

Usage

describe.post(mcout,burnin=1000)

Arguments

mcout

is a list. Each item in the list is a matrix of MCMC output, corresponding to different values of M, the precision parameter of the Dirichlet model. If the matrices are output from dirichlet.c, each matrix has ncycles +1 rows and m+2 columns, where m is the number of studies in the meta-analysis and ncycles is the number of runs of the Markov chain. The matrix output from the ordinary Dirichlet model function dirichlet.o is similar but has an additional column. The rows hold output from separate Markov chain runs (the first row is the initial values.) Columns 1 through m hold the individual study parameters, the ψ_i's. The next two columns hold the mean and standard deviation parameters of the centering normal distribution of the Dirichlet prior, mu and tau. In the case of the ordinary Dirichlet model, an additional column is added to hold the values of eta.

burnin

is the number of initial chains to omit from the estimates.

Value

List with two named components, means.table and probs.table, returned invisibly.

Examples

## Not run: 
## Set up the data.

data(breast.17) # the breast cancer dataset
breast.data <- as.matrix(breast.17) # put data in matrix object

## Generate at least two chains, from models which are the same except
## for different \eqn{M} values.

set.seed(1) # initialize the seed at 1 
breast.c1 <- dirichlet.c(breast.data, ncycles=4000, M=5)
breast.c2 <- dirichlet.c(breast.data,ncycles=4000, M=1000)

## Create list object.
breast.c1c2 <- list("5"=breast.c1$chain, "1000"= breast.c2$chain)

## Decide on some number of initial runs to omit from the analysis.
describe.post(breast.c1c2, burnin=100)

## End(Not run)

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