This function visualizes the regions of the representative tree
of the output of the mrs
function.
1 2 3 4 5 
ans 
An 
type 
Different options on how to visualize the rectangular regions.
The options are 
data.points 
Different options on how to plot the data points.
The options are 
background 
Different options on the background.
The options are 
group 
If 
dim 
If the data are multivariate,

levels 
Vector with the level of the regions to plot. The default is to plot regions at all levels. 
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 legend. 
abs 
If 
Soriano J. and Ma L. (2016). Probabilistic multiresolution scanning for twosample 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 multiscale BetaBinomial 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 19 20 21 22 23  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)
plot2D(ans, type = "prob", legend = TRUE)
plot2D(ans, type="empty", data.points = "differential",
background = "none")
plot2D(ans, type="none", data.points = "differential",
background = "smeared", levels = 4)

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