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Data:
4.2 0.5 4.6 5.2 2.7 4.2 1.8 1.7 3.8 0 3.9 3.5 1 1.7 7.8 4.9 3.8 0 7.7 0 7.7 0 10.1 1.7 0 8.6 3.9 4.2 1.7 0 7 1.8 8.4 3.4 7 3.9 2.7 7.7 1.7 1.7 11 1.8 2.1 0.7 1.7 10.5 6.2 1.8 1.7 7 3.5 3.8 0.5 1.8 8.4 7.7 1.7 4.9 0 1.8 6.3 5.2 1 1 0 5.6 5.6 3.8 3.5 6.3 0 5.9 1.7 3.5 1.8 0 8.4 5.1 7 3.4 4.2 0 1.4 3.9 6.3 7 2.1 4.5 0 3.5 8.1 4.2 0 1.7 5.3 1.8 0 13.6 9.4 0 2.1 1.7 2.1 0 3.5 1.8 0 8 0 2.1 3.9 1 7.7 2.1 1.7 0 1 6 0 1.8 3.5 4.2 7.6 1 1.8 2.1 1 1.7 8.4 10.8 1.8 2.1 0 0.7 5.6 6 1.7 1.8 5.2 6.3 0 3.4 1 1 5.6 8.3 2.6 2.1 0 3.5 8.3 7 3.4 1 4.2


Sample Range: (leave blank to include all observations)
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lowest quantile


highest quantile


step size


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R Code
par1 <- as(par1,'numeric') par2 <- as(par2,'numeric') par3 <- as(par3,'numeric') library(Hmisc) myseq <- seq(par1, par2, par3) hd <- hdquantile(x, probs = myseq, se = TRUE, na.rm = FALSE, names = TRUE, weights=FALSE) bitmap(file='test1.png') plot(myseq,hd,col=2,main=main,xlab=xlab,ylab=ylab) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Harrell-Davis Quantiles',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'quantiles',header=TRUE) a<-table.element(a,'value',header=TRUE) a<-table.element(a,'standard error',header=TRUE) a<-table.row.end(a) length(hd) for (i in 1:length(hd)) { a<-table.row.start(a) a<-table.element(a,as(labels(hd)[i],'numeric'),header=TRUE) a<-table.element(a,as.matrix(hd[i])[1,1]) a<-table.element(a,as.matrix(attr(hd,'se')[i])[1,1]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center