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#' Plot regions of the representative tree in 1D
#'
#' This function visualizes the regions of the representative tree
#' of the output of the \code{\link{mrs}} function.
#' For each region the posterior probability of difference (PMAP) or the effect size is plotted.
#'
#' @param ans An \code{mrs} object.
#' @param type What is represented at each node.
#' The options are \code{type = c("eff", "prob")}.
#' Default is \code{type = "prob"}.
#' @param group If \code{type = "eff"}, which group effect size is used.
#' Default is \code{group = 1}.
#' @param dim If the data are multivariate, \code{dim} is the dimension plotted.
#' Default is \code{dim = 1}.
#' @param regions Binary vector indicating the regions to plot.
#' The default is to plot all regions.
#' @param legend Color legend for type. Default is \code{legend = FALSE}.
#' @param main Overall title for the plot.
#' @param abs If \code{TRUE}, plot the absolute value of the effect size.
#' Only used when \code{type = "eff"}.
#' @references Soriano J. and Ma L. (2016).
#' Probabilistic multi-resolution scanning for two-sample differences.
#' \emph{Journal of the Royal Statistical Society: Series B (Statistical Methodology)}.
#' \url{http://onlinelibrary.wiley.com/doi/10.1111/rssb.12180/abstract}
#' @references Ma L. and Soriano J. (2016).
#' Analysis of distributional variation through multi-scale Beta-Binomial modeling.
#' \emph{arXiv}.
#' \url{http://arxiv.org/abs/1604.01443}
#' @export
#' @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")
plot1D <- function( ans,
type = "prob",
group = 1,
dim = 1,
regions = rep(1,length(ans$RepresentativeTree$Levels)),
legend = FALSE,
main = "default",
abs = TRUE)
{
if(!is(ans,"mrs")) {
print("ERROR: ans should be an MRS object.")
return(0)
}
.pardefault <- par(no.readonly = T)
if(legend)
{
layout(matrix(c(1,2), nrow=1),widths=c(.8,.2))
par(mar=c(5.1, 3.1, 4.1, 0.1))
}
else
{
layout(1)
}
if(type == "prob")
{
names = round(ans$RepresentativeTree$AltProbs[which(regions==1)], digits=2)
col_range <- colorRampPalette(c("white","darkblue"))(100)
col = col_range[ ceiling(names*99+ 1) ]
if (main == "default")
main = "PMAPs"
}
else if(type == "eff")
{
if (abs == TRUE) {
names = abs(ans$RepresentativeTree$EffectSizes[which(regions==1),group])
col_range <- colorRampPalette(c("white","darkred"))(100)
col = col_range[ ceiling( names/max(names + 0.01)*99 + 1) ]
} else {
names = ans$RepresentativeTree$EffectSizes[which(regions==1), group]
col_range <- c(colorRampPalette(c("red","white"))(50),
colorRampPalette(c("white","dodgerblue"))(50))
col = col_range[ ceiling(names/max(abs(names)+0.01)*100/2+50) ]
}
if (main == "default")
main = paste("Effect Size Group", group)
}
else
{
names = rep(1, length(ans$RepresentativeTree$AltProbs[which(regions==1)]))
col = rep(NA, length(ans$RepresentativeTree$altProbs[which(regions==1)]))
if (main == "default")
main = ""
}
max.level = max(ans$RepresentativeTree$Levels[which(regions==1)])
plot(1, type="n", yaxt='n', xlab="", ylab="",
xlim = ans$Data$Omega[dim,], ylim=c(0,max.level+.75), main = main)
mtext("level", side=2, line = 2, at= (max.level+.75)/2 )
axis(2, at= seq(0.4, max.level+0.4),
labels=seq(max.level, 0, by=-1) )
it = 1
for (i in which(regions == 1) )
{
region = ans$RepresentativeTree$Regions[i,]
rect(xleft=(region[2*dim-1]) ,
ybottom= (max.level - ans$RepresentativeTree$Levels[i]) ,
xright=region[2*dim] ,
ytop=(max.level - ans$RepresentativeTree$Levels[i] + .75), col = col[it], border = col[it])
it = it + 1
}
if(legend)
{
plot(NA,type="n",ann=FALSE,xlim=c(1,2),ylim=c(1,2),xaxt="n",yaxt="n",bty="n")
rect(xleft = 1.25,
ybottom = head(seq(1,2,1/100),-1),
xright = 1.75,
ytop = tail(seq(1,2,1/100),-1),
col=col_range, border = col_range )
rect(1.25, 1, 1.75, 2.0)
if(type == "prob")
mtext(formatC(seq(0,1,.1), format = "f", digits = 1),side=2,at=seq(1,2.,by=.1),las=2,cex=1, line=0)
if(type == "eff") {
if (abs == FALSE) {
mtext(formatC(seq( -max(abs(names)), max(abs(names)), length.out=11), format = "f", digits = 1),
side=2,at=seq(1,2.,by=.1),las=2,cex=1, line=0)
} else {
mtext(formatC(seq( 0, max(names), length.out=11), format = "f", digits = 1),
side=2,at=seq(1,2.,by=.1),las=2,cex=1, line=0)
}
}
}
par(.pardefault)
}
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