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Data:
0.104050633 0.015164835 -0.02212766 -0.011489362 0.048131868 0.057777778 0.050967742 0.008282828 -0.003673469 -0.013548387 -0.000722892 -0.0075 -0.043529412 -0.089230769 -0.099279279 -0.076880734 -0.05 -0.004782609 0.006086957 0.001052632 -0.0075 0.001052632 0.004175824 0.022696629 -0.031111111 -0.077425743 -0.09184466 -0.074901961 -0.017916667 0.016956522 0.029462366 0.041702128 0.052340426 0.071304348 0.091111111 0.057777778 -0.008888889 -0.115918367 -0.16 -0.136326531 -0.078064516 -0.042222222 -0.031111111 -0.028791209 -0.03978022 -0.061758242 -0.059130435 -0.047272727 -0.048915663 -0.022380952 -0.005185185 0.011168831 -0.022531646 -0.035189873 -0.0075 0.040759494 0.074736842 0.081408451 0.077058824 0.072307692 0.064057971 0.05804878 0.01908046 0.011325301 -0.047848101 -0.046666667 -0.025128205 0.035421687 0.096666667 0.143414634 0.18 0.207777778 0.20739726
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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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