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Data:
425.25 417.75 410.25 395.25 546.75 539.25 425.25 349.50 357.00 357.00 364.50 380.25 334.50 288.75 251.25 251.25 395.25 410.25 296.25 167.25 235.50 235.50 288.75 319.50 312.00 235.50 273.75 258.75 387.75 357.00 235.50 144.75 228.00 251.25 273.75 303.75 243.00 190.50 213.00 220.50 417.75 417.75 303.75 288.75 334.50 312.00 372.75 448.50 463.50 357.00 327.00 296.25 501.75 516.75 478.50 516.75 509.25 448.50 516.75 592.50 623.25 531.75 471.00 516.75 714.00 774.75 759.75 789.75 782.25 706.50 835.50 866.25 911.25 774.75 721.50 782.25 927.00 1056.00 1025.25 1025.25 1040.25 987.75 1124.25 1124.25 1101.00 972.00 995.25 1010.25 1109.25 1238.25 1146.75 1192.50 1154.25 1131.75 1306.50 1268.25 1215.00 1139.25 1215.00 1253.25 1299.00 1359.75 1299.00 1336.50 1290.75 1283.25 1473.00 1488.75 1428.00 1321.50 1412.25 1450.50 1496.25 1564.50 1496.25 1549.50 1526.25 1443.00 1617.75 1617.75
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R Code
par3 <- '0.1' par2 <- '0.9' par1 <- '0.1' 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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