Description Usage Arguments Examples
Draw overlapping kernel density estimates of the posterior distributions of the parameters of the conditional or ordinary Dirichlet model, where the posteriors arise from different values of the Dirichlet precision parameter M.
1 |
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 |
burnin |
is the number of initial chains to omit from the estimates, must be no larger than ncycles - 10. |
ind.par |
an integer vector, containing indices of which columns
of |
adjust |
is the bin width argument for the call to the R base package function density. |
... |
additional arguments to plot may be supplied. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
## Conditional Dirichlet model
## Set up the breast cancer dataset.
data(breast.17)
breast.data <- as.matrix(breast.17) # Data must be a matrix object.
## Generate at least two chains, from models which are the same except
## for different \eqn{M}{M} values.
set.seed(1) # initialize the seed at 1 for test purposes
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.
draw.post(breast.c1c2, burnin=100) # plots for hyperparameters only
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.