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Data:
-14.22222222 4.666666667 -1.375 -5.208333333 -9.055555556 -2.441860465 -2.95 -2.780487805 -5.9 -7.208333333 -6.642857143 -11.1875 -35.66666667 19.4 19.375 38.5 -3.5 -1.6 2.333333333 -3.5 9.5 3.5 4.1 3.882352941 5.935483871 5.121212121 2.967741935 -1.228571429 -0.983333333 -1.350877193 -1.14893617 -0.547619048 -1.333333333 0.522727273 -2.08 16.66666667 9 7.578947368 -5.571428571 -4.111111111 -3.823529412 -0.290322581 1.952380952 -35 -10.16666667 -0.657894737 1.1 2.075757576 2.320754717 1.763157895 0.468085106 0.257575758 -1.636363636 -1.043478261 -0.483333333 -0.5 -0.625 -1.962962963 -2.366666667 -1.951219512 -2.225 -2.851851852 -0.423076923 1.290322581 2.181818182 3.633333333 6.5 11.46153846 13.4 8.307692308 3.4375 2.111111111 3.272727273 7.6 4.25 5.5 1 -1.307692308 4.363636364 2.25 1.8 1.483606557 1.24137931 0.98125 0.910179641 0.926380368 1.059171598
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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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