plot2D: Plot regions of the representative tree in 2D

In MRS: Multi-Resolution Scanning for Cross-Sample and Cross-Group Differences

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",
  abs = TRUE
)

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.
abs	If TRUE, plot the absolute value of the effect size. Only used when type = "eff".

Value

Invisibly returns NULL. The function is called for its side effect of drawing one panel for each selected tree level on the current graphics device. Rectangles encode posterior probabilities or effect sizes; optional points and a smoothed background show the observed data.

References

Soriano J. and Ma L. (2017). Probabilistic multi-resolution scanning for two-sample differences. Journal of the Royal Statistical Society: Series B (Statistical Methodology). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12180")}

Ma L. and Soriano J. (2018). Analysis of distributional variation through multi-scale Beta-Binomial modeling. Journal of Computational and Graphical Statistics, 27(3), 529–541. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2017.1402774")}

Examples

set.seed(12345)
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)

MRS documentation built on July 22, 2026, 5:10 p.m.