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Data:
0.21700345 0.19487753 0.18146407 0.14286458 0.11676572 0.14791562 0.14088193 0.12885434 0.11487708 0.07428488 0.09162578 0.17441780 0.13667815 0.14372917 0.10179335 0.08756846 0.05766416 0.04745797 0.05928558 0.01412109 0.02553917 0.00506876 0.02974750 0.01668761 0.06695228 0.05729958 0.01933319 0.00094003 -0.01668842 0.00294635 -0.00865642 -0.02824939 -0.03007708 -0.03802787 -0.01668314 -0.01662206 -0.00218757 0.00257730 -0.00094003 -0.01687868 -0.02894042 -0.04632411 -0.03676994 -0.04588256 -0.07953927 -0.08640519 -0.09823005 -0.08798609 -0.05230414 -0.04487189 -0.06701285 -0.07426128 -0.06275580 -0.06551282 -0.05606515 -0.04854424 -0.07600280 -0.07896373 -0.06768279 -0.06291119
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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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