Description Usage Arguments Note References Examples

This function visualizes the representative tree of the output of the `mrs`

function.
For each node of the representative tree, the posterior probability of difference (PMAP) or the effect size is plotted.
Each node in the tree is associated to a region of the sample space.
All non-terminal nodes have two children nodes obtained by partitiing the parent region with a dyadic cut along a given direction.
The numbers under the vertices represent the cutting direction.

1 2 |

`ans` |
A |

`type` |
What is represented at each node.
The options are |

`group` |
If |

`legend` |
Color legend for type. Default is |

`main` |
Main title. Default is |

`node.size` |
Size of the nodes. Default is |

`abs` |
If |

The package igraph is required.

Soriano J. and Ma L. (2016).
Probabilistic multi-resolution scanning for two-sample differences.
*Journal of the Royal Statistical Society: Series B (Statistical Methodology)*.
http://onlinelibrary.wiley.com/doi/10.1111/rssb.12180/abstract

Ma L. and Soriano J. (2016).
Analysis of distributional variation through multi-scale Beta-Binomial modeling.
*arXiv*.
http://arxiv.org/abs/1604.01443

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
set.seed(1)
p = 2
n1 = 200
n2 = 200
mu1 = matrix( c(9,9,0,4,-2,-10,3,6,6,-10), nrow = 5, byrow=TRUE)
mu2 = mu1; mu2[2,] = mu1[2,] + 1
Z1 = sample(5, n1, replace=TRUE)
Z2 = sample(5, n2, replace=TRUE)
X1 = mu1[Z1,] + matrix(rnorm(n1*p), ncol=p)
X2 = mu2[Z2,] + matrix(rnorm(n2*p), ncol=p)
X = rbind(X1, X2)
colnames(X) = c(1,2)
G = c(rep(1, n1), rep(2,n2))
ans = mrs(X, G, K=8)
plotTree(ans, type = "prob", legend = TRUE)
``` |

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