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Data:
613.20 614.70 618.40 628.20 629.00 629.70 630.40 630.40 639.30 639.40 640.90 640.80 642.10 645.30 647.60 648.40 648.80 648.90 648.90 648.90 650.30 650.30 650.00 650.00 650.50 658.40 666.00 675.50 680.70 690.60 690.60 691.10 692.90 693.80 692.80 697.50 699.00 702.10 704.80 715.50 721.80 726.40 727.70 727.40 731.30 734.40 733.40 733.40 738.10 742.60 747.20 751.10 752.60 758.90 759.10 764.30 765.60 767.60 767.60 765.60 768.20 770.90 775.10 777.60 778.60 778.90 779.40 779.90 781.70 789.10 788.70 788.80 790.80 794.10 795.10 797.30 803.80 805.60 804.60 804.50 805.80 806.80 805.20 814.90 816.60 819.50 823.00 824.00 831.40 831.70 831.10 832.10 833.30 838.80 838.00 837.30 994.20 994.20 994.20 994.20 994.20 1092.60 1100.00 1100.00 1092.60 1000.70 1000.70 1000.50 1000.50 1000.50 1000.50 1000.50 1000.50 1087.70 1113.20 1116.00 1085.20 1031.30 1028.70 1027.50 1027.50 1027.50 1027.50 1027.50 1027.50 1152.20 1155.30 1154.00 1119.90 1079.30 1074.30 1069.80
# simulations
blockwidth of bootstrap
Significant digits
Quantiles
P1 P5 Q1 Q3 P95 P99
P0.5 P2.5 Q1 Q3 P97.5 P99.5
P10 P20 Q1 Q3 P80 P90
bandwidth
Chart options
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) if (par1 < 10) par1 = 10 if (par1 > 5000) par1 = 5000 if (par2 < 3) par2 = 3 if (par2 > length(x)) par2 = length(x) library(lattice) library(boot) boot.stat <- function(s) { s.mean <- mean(s) s.median <- median(s) s.midrange <- (max(s) + min(s)) / 2 c(s.mean, s.median, s.midrange) } (r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed')) bitmap(file='plot1.png') plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean') grid() dev.off() bitmap(file='plot2.png') plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median') grid() dev.off() bitmap(file='plot3.png') plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') grid() dev.off() bitmap(file='plot4.png') densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean') dev.off() bitmap(file='plot5.png') densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median') dev.off() bitmap(file='plot6.png') densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange') dev.off() z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3])) colnames(z) <- list('mean','median','midrange') bitmap(file='plot7.png') boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'statistic',header=TRUE) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,'Estimate',header=TRUE) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'IQR',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) q1 <- quantile(r$t[,1],0.25)[[1]] q3 <- quantile(r$t[,1],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[1]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,1]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) q1 <- quantile(r$t[,2],0.25)[[1]] q3 <- quantile(r$t[,2],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[2]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,2]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'midrange',header=TRUE) q1 <- quantile(r$t[,3],0.25)[[1]] q3 <- quantile(r$t[,3],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[3]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,3]))) a<-table.element(a,q3-q1) 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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