Description Usage Arguments Value Author(s) Examples

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

1 2 | ```
canopy.post(sampchain, projectname, K, numchain, burnin, thin, optK,
C, post.config.cutoff)
``` |

`sampchain` |
list of sampled trees returned by |

`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 |

`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) |

`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 |

Yuchao Jiang [email protected]

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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)
``` |

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