Canopy (version 1.3.0)

canopy.post: Posterior evaluation of MCMC sampled trees

Description

Burnin, thinning, and posterior evaluation of MCMC sampled trees.

Usage

canopy.post(sampchain, projectname, K, numchain, burnin, thin, optK,
            C, post.config.cutoff)

Arguments

sampchain

list of sampled trees returned by canopy.sample

projectname

name of project

K

number of subclones (vector)

numchain

number of MCMC chains with random initiations

burnin

burnin of MCMC chains

thin

MCMC chain thinning.

optK

optimal number of subclones determined by canopy.BIC

C

CNA and CNA-region overlapping matrix, only needed if overlapping CNAs are used as input

post.config.cutoff

cutoff value for posterior probabilities of tree configurations, default is set to be 0.05 (only tree configurations with greater than 0.05 posterior probabilities will be reported by Canopy)

Value

samptreethin

list of sampled posterior trees

samptreethin.lik

vector of likelihood of sampled posterior trees

config

vector of configuration of sampled posterior trees (integer values)

config.summary

summary of configurations of sampled posterior trees

Examples

# NOT RUN {
data(MDA231_sampchain)
data(MDA231)
sampchain = MDA231_sampchain
projectname = 'MD231'
K = 3:6
numchain = 20
burnin = 150
thin = 5
optK = 4
C = MDA231$C
post = canopy.post(sampchain = sampchain, projectname = projectname, K = K,
                   numchain = numchain, burnin = burnin, thin = thin, 
                   optK = optK, C = C)
# }