plot1D | R Documentation |
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.
plot1D(ans, type = "prob", group = 1, dim = 1, regions = rep(1, length(ans$RepresentativeTree$Levels)), legend = FALSE, main = "default", abs = TRUE)
ans |
An |
type |
What is represented at each node.
The options are |
group |
If |
dim |
If the data are multivariate, |
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 plot. |
abs |
If |
Soriano J. and Ma L. (2016). Probabilistic multi-resolution scanning for two-sample 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 multi-scale Beta-Binomial modeling. arXiv. http://arxiv.org/abs/1604.01443
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")
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