mysegplot<-function(stats,labs){
miny<-min(eval(parse(text=stats[2])))-.0025
maxy<-max(eval(parse(text=stats[3])))+.0025
ys<-seq(miny,maxy,(maxy-miny)/14)
plot(1:15,ys,xlab="",ylab="",ylim=c(miny,maxy),type="n",axes=FALSE)
  nums<-seq(0,((3*length(stats)+4)-3),4)
for (j in 1:length(stats)){
  k<-0
  for (i in (nums[j]+1):(nums[j]+3)){
    k<-k+1
      tmpstat<-eval(parse(text=stats[j]))
      points(i,tmpstat[4,k],type="p",pch=1,cex=.5)  #Medians
#      segments(i-.25,tmpstat[3,k],i+.25,tmpstat[3,k]) #Mean Line
      segments(i,tmpstat[2,k],i,tmpstat[1,k]) #25% - 5%
      segments(i,tmpstat[5,k],i,tmpstat[6,k]) # 75% - 95%
    text(i,tmpstat[6,k]+.003,labels=k,cex=.66,offset=0)
  }
}
axis(1, at = seq(2,((3*length(stats))+3),4), labels = labs ,tick=FALSE,line=-2)
axis(2)
title(ylab="Standard Error")
}



par(mfrow=c(1,1),mar=c(0,4,0,0)+.1)

#Split the Standard Errors for each variable by the design.

CbyDesign<-split(SEConst,design)
XbyDesign<-split(SEX,design)
Z1byDesign<-split(SEZ1,design)
Z2byDesign<-split(SEZ2,design)
XZ1byDesign<-split(SEXZ1,design)
XZ2byDesign<-split(SEXZ2,design)

#Calculate summary statistics for each variable by design.
sumC<-sapply(CbyDesign,FUN=function(x) c(quantile(x,c(.05,.25,.50)),mean(x),quantile(x,c(.75,.95))))
sumX<-sapply(XbyDesign,FUN=function(x) c(quantile(x,c(.05,.25,.50)),mean(x),quantile(x,c(.75,.95))))
sumZ1<-sapply(Z1byDesign,FUN=function(x) c(quantile(x,c(.05,.25,.50)),mean(x),quantile(x,c(.75,.95))))
sumZ2<-sapply(Z2byDesign,FUN=function(x) c(quantile(x,c(.05,.25,.50)),mean(x),quantile(x,c(.75,.95))))
sumXZ1<-sapply(XZ1byDesign,FUN=function(x) c(quantile(x,c(.05,.25,.50)),mean(x),quantile(x,c(.75,.95))))
sumXZ2<-sapply(XZ2byDesign,FUN=function(x) c(quantile(x,c(.05,.25,.50)),mean(x),quantile(x,c(.75,.95))))


#Make a list of the objects to be plotted.
sumstuffsmall<-as.list(c("sumC","sumX","sumZ1","sumXZ1"))

#Call the plotting function.
mysegplot(stats=sumstuffsmall,
           labs=c("Constant","Micro","Macro","Cross-Level"))