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Data:
-32.0928134170003 -128.744158053173 -275.904809333092 471.760903738039 -69.1654637163138 346.218578823237 163.216457137027 -198.373745010757 204.401273185691 -175.940320227463 17.4783262653075 223.539436263922 51.4668945111487 260.738288102879 204.977677230572 -232.867546347215 -217.221406054262 387.746049688947 63.5945545423544 364.638829260013 96.4516787351593 -29.7694719663539 286.722082027268 211.416619028106 -185.782838773910 -141.320849733312 238.502069839504 209.511569776478 285.264826114983 20.084745860321 -487.431942052935 107.320923964348 34.3269970752997 141.696685920852 -133.438481853825 -229.255640647698 69.0555896372578 -139.443214460286 80.0405548902168 -140.866735807813 415.577107396144 151.513504277031 -24.7248794807892 8.4258084117008 272.69433043438 313.164475266341 179.140854480554 -495.203375413695
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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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