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Data:
14097.80 14776.80 16833.30 15385.50 15172.60 16858.90 14143.50 14731.80 16471.60 15214.00 17637.40 17972.40 16896.20 16698.00 19691.60 15930.70 17444.60 17699.40 15189.80 15672.70 17180.80 17664.90 17862.90 16162.30 17463.60 16772.10 19106.90 16721.30 18161.30 18509.90 17802.70 16409.90 17967.70 20286.60 19537.30 18021.90 20194.30 19049.60 20244.70 21473.30 19673.60 21053.20 20159.50 18203.60 21289.50 20432.30 17180.40 15816.80 15076.60 14531.60 15761.30 14345.50 13916.80 15496.80 14285.60 13597.30 16263.10 16773.30 15986.90 16842.60 15911.90 15782.90 18622.80 17422.50 16989.80 18990.50 16849.30 16511.30 18704.50 19111.10 19420.70 18985.10
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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')
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Big Analytics Cloud Computing Center
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