plot2D {MRS} | R Documentation |
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 |
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 |
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). 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. Vol. 27, No. 3, 529-541.. doi:10.1080/10618600.2017.1402774
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)