MRS (version 1.1)

plot2D: Plot regions of the representative tree in 2D

Description

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

Usage

plot2D(ans, type = "prob", data.points = "all", background = "none", group = 1, dim = c(1, 2), levels = sort(unique(ans$RepresentativeTree$Levels)), regions = rep(1, length(ans$RepresentativeTree$Levels)), legend = FALSE, main = "default")

Arguments

ans

An mrs object.

type

Different options on how to visualize the rectangular regions. The options are type = c("eff", "prob", "empty", "none"). Default is type = "prob".

data.points

Different options on how to plot the data points. The options are data.points = c("all", "differential", "none"). Default is data.points = "all".

background

Different options on the background. The options are background = c("smeared", "none") .

group

If type = "eff", which group effect size is used. Default is group = 1.

dim

If the data are multivariate, dim are the two dimensions plotted. Default is dim = c(1,2).

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 legend = FALSE.

main

Overall title for the legend.

References

Soriano J. and Ma L. (2014). Multi-resolution two-sample comparison through the divide-merge Markov tree. Preprint. http://arxiv.org/abs/1404.3753

Examples

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)