plotTree | R Documentation |
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.
plotTree(ans, type = "prob", group = 1, legend = FALSE, main = "", node.size = 5, abs = TRUE)
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
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.