This function visualizes the regions of the representative tree
of the output of the mrs function.
For each region the posterior probability of difference (PMAP) or the effect size is plotted.
Usage
plot1D(ans, type = "prob", group = 1, dim = 1, regions = rep(1,
length(ans$RepresentativeTree$Levels)), legend = FALSE, main = "default")
Arguments
ans
An mrs object.
type
What is represented at each node.
The options are type = c("eff", "prob").
Default is type = "prob".
group
If type = "eff", which group effect size is used.
Default is group = 1.
dim
If the data are multivariate, dim is the dimension plotted.
Default is dim = 1.
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 plot.
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
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MRS)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MRS/plot1D.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot1D
> ### Title: Plot regions of the representative tree in 1D
> ### Aliases: plot1D
>
> ### ** Examples
>
> set.seed(1)
> p = 1
> n1 = 200
> n2 = 200
> mu1 = matrix( c(0,10), nrow = 2, byrow = TRUE)
> mu2 = mu1; mu2[2] = mu1[2] + .01
> sigma = c(1,.1)
>
> Z1 = sample(2, n1, replace=TRUE, prob=c(0.9, 0.1))
> Z2 = sample(2, n2, replace=TRUE, prob=c(0.9, 0.1))
> X1 = mu1[Z1] + matrix(rnorm(n1*p), ncol=p)*sigma[Z1]
> X2 = mu2[Z2] + matrix(rnorm(n2*p), ncol=p)*sigma[Z1]
> X = rbind(X1, X2)
> G = c(rep(1, n1), rep(2,n2))
>
> ans = mrs(X, G, K=10)
> plot1D(ans, type = "prob")
> plot1D(ans, type = "eff")
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
