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

function.
For each region the posterior probability of difference (PMAP) or the effect size is plotted.

1 2 3 |

`ans` |
An |

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

`group` |
If |

`dim` |
If the data are multivariate, |

`regions` |
Binary vector indicating the regions to plot. The default is to plot all regions. |

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

`main` |
Overall title for the plot. |

`abs` |
If |

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 18 | ```
set.seed(1)
p = 1
n1 = 200
n2 = 200
mu1 = matrix( c(0,10), nrow = 2, byrow = TRUE)
mu2 = mu1; mu2[2] = mu1[2] + .01
sigma = c(1,.1)
Z1 = sample(2, n1, replace=TRUE, prob=c(0.9, 0.1))
Z2 = sample(2, n2, replace=TRUE, prob=c(0.9, 0.1))
X1 = mu1[Z1] + matrix(rnorm(n1*p), ncol=p)*sigma[Z1]
X2 = mu2[Z2] + matrix(rnorm(n2*p), ncol=p)*sigma[Z1]
X = rbind(X1, X2)
G = c(rep(1, n1), rep(2,n2))
ans = mrs(X, G, K=10)
plot1D(ans, type = "prob")
plot1D(ans, type = "eff")
``` |

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