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Data:
0.0991579 -0.24856 -0.129539 -5.65751 -6.22954 -0.265554 1.6447 0.434446 -2.27816 -4.98621 -1.59352 -6.75458 -2.16921 -1.12149 -6.5023 -1.78987 -1.12515 -3.72588 -3.33027 -2.89718 -6.88182 0.74249 -2.41856 1.28818 -1.4553 -2.02954 3.04104 0.310134 -3.09352 -1.65385 -4.69425 -1.42222 -3.99352 -3.2928 -2.28987 -2.40595 -2.14563 -0.453851 -3.62954 -0.905954 -6.99718 -1.36921 -0.284001 -2.28987 -0.822221 -1.21327 -1.28987 -5.09352 0.25144 -0.565554 -1.32954 -6.26262 -2.18987 -1.24653 -0.0141772 -2.79718 -7.48766 1.56387 -4.57653 0.210134 -3.72588 -0.172872 -4.97726 2.81013 -3.66628 -3.6045 -1.79718 0.038105 1.63445 -0.793524 -0.886207 -1.49718 -3.93758 2.11981 0.150714 -0.0582362 -0.104501 0.17412 -3.09425 -5.38987 -2.42954 0.534446 -2.18987 -8.56555 -2.88693 -1.32222 -1.88987 -1.2736 -4.13686 0.495499 -0.298636 -7.20157 -3.38182 -4.1619 -5.1332 -4.08987 -5.69352 -1.3045 1.24851 -4.70889 -2.42222 -2.78987 -4.21182 -3.61784 1.36608 -4.65458 0.891114 -8.0619 -6.40157 -3.07287 -1.39352 -1.15515 -8.83693 -0.422221 5.01013 4.76013 3.73372 -0.393524 4.20282 6.10648 2.94542 0.646226 5.82347 1.61086 -2.45824 -0.58548 3.01307 0.221838 5.81013 0.946149 1.41013 3.15648 1.81973 1.61307 -0.542798 -0.439866 4.16379 -4.77816 5.47046 2.91379 -0.720589 4.72485 2.25648 4.56013 3.26013 4.72046 0.510134 1.26013 1.66013 5.35282 -3.12661 2.78071 5.16013 3.85648 1.47778 0.695423 5.6351 2.60648 1.79249 -1.33987 5.36013 2.64908 4.1381 2.7851 7.3551 -2.04352 5.45575 2.10648 2.10209 -1.77222 2.57184 5.07339 1.76608 2.37412 2.37412 4.57412 0.29249 6.33006 2.26013 -4.75222 0.662417 1.22568 6.07412 2.09542 -0.158963 3.10648 4.5168 0.25209 -0.619213 -0.619289 -2.67661 2.27778 -3.29718 5.14176 1.02347 1.76013 1.72339 0.977779 2.06608 3.09542 6.22485 0.509408 0.605749 2.49542 0.174196 1.60648 -3.40019 -0.280266 6.686 4.24778 -1.98621 1.9351 -1.19059 0.880864 3.54916 -0.400842 2.79176 4.89981 -2.03693 2.75282 4.72412 -5.66418 -0.772221 2.79843 6.21013 2.46242 4.1072 2.60648 5.08582 6.05648 5.55575 6.10575 -0.0538508 -0.20751 -3.87816 -8.62222 -0.727257 0.734446 -1.32588 -0.914177 0.324846 -1.75385 1.81013 7.3551 -1.3758 1.60575 4.22111 1.28151 3.17046 0.32412 -1.11117 -0.947183 1.66013 1.89908 0.398431 3.79176 -5.30385 -5.26117 -0.00888631 -5.27222 -2.04352 -0.387583 -3.13987 1.81013 3.56013 0.405749 1.05282 2.61013 3.72778 -2.33255 2.25868 -1.43701 2.04176 4.66013 2.04542 1.51013 3.84843 -4.51034
# simulations
Significant digits
Bandwidth
(?)
Quantiles
P1 P5 Q1 Q3 P95 P99
P0.5 P2.5 Q1 Q3 P97.5 P99.5
P10 P20 Q1 Q3 P80 P90
Chart options
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) if (par3 == '0') bw <- NULL if (par3 != '0') bw <- as.numeric(par3) if (par1 < 10) par1 = 10 if (par1 > 5000) par1 = 5000 library(modeest) library(lattice) library(boot) boot.stat <- function(s,i) { s.mean <- mean(s[i]) s.median <- median(s[i]) s.midrange <- (max(s[i]) + min(s[i])) / 2 s.mode <- mlv(s[i], method='mfv')$M s.kernelmode <- mlv(s[i], method='kernel', bw=bw)$M c(s.mean, s.median, s.midrange, s.mode, s.kernelmode) } (r <- boot(x,boot.stat, R=par1, stype='i')) 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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') grid() dev.off() bitmap(file='plot7.png') plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode') grid() dev.off() bitmap(file='plot8.png') plot(r$t[,5],type='p',ylab='simulated values',main='Simulation of Mode of Kernel Density') grid() dev.off() bitmap(file='plot4.png') densityplot(~r$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() bitmap(file='plot9.png') densityplot(~r$t[,4],col='black',main='Density Plot',xlab='mode') dev.off() bitmap(file='plot10.png') densityplot(~r$t[,5],col='black',main='Density Plot',xlab='mode of kernel dens.') dev.off() z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5])) colnames(z) <- list('mean','median','midrange','mode','mode k.dens') bitmap(file='plot11.png') boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE) a<-table.row.end(a) if (par4 == 'P1 P5 Q1 Q3 P95 P99') { myq.1 <- 0.01 myq.2 <- 0.05 myq.3 <- 0.95 myq.4 <- 0.99 myl.1 <- 'P1' myl.2 <- 'P5' myl.3 <- 'P95' myl.4 <- 'P99' } if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') { myq.1 <- 0.005 myq.2 <- 0.025 myq.3 <- 0.975 myq.4 <- 0.995 myl.1 <- 'P0.5' myl.2 <- 'P2.5' myl.3 <- 'P97.5' myl.4 <- 'P99.5' } if (par4 == 'P10 P20 Q1 Q3 P80 P90') { myq.1 <- 0.10 myq.2 <- 0.20 myq.3 <- 0.80 myq.4 <- 0.90 myl.1 <- 'P10' myl.2 <- 'P20' myl.3 <- 'P80' myl.4 <- 'P90' } a<-table.row.start(a) a<-table.element(a,'statistic',header=TRUE) a<-table.element(a,myl.1,header=TRUE) a<-table.element(a,myl.2,header=TRUE) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,'Estimate',header=TRUE) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,myl.3,header=TRUE) a<-table.element(a,myl.4,header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'IQR',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) q1 <- quantile(r$t[,1],0.25)[[1]] q3 <- quantile(r$t[,1],0.75)[[1]] p01 <- quantile(r$t[,1],myq.1)[[1]] p05 <- quantile(r$t[,1],myq.2)[[1]] p95 <- quantile(r$t[,1],myq.3)[[1]] p99 <- quantile(r$t[,1],myq.4)[[1]] a<-table.element(a,signif(p01,par2)) a<-table.element(a,signif(p05,par2)) a<-table.element(a,signif(q1,par2)) a<-table.element(a,signif(r$t0[1],par2)) a<-table.element(a,signif(q3,par2)) a<-table.element(a,signif(p95,par2)) a<-table.element(a,signif(p99,par2)) a<-table.element( a,signif( sqrt(var(r$t[,1])),par2 ) ) a<-table.element(a,signif(q3-q1,par2)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) q1 <- quantile(r$t[,2],0.25)[[1]] q3 <- quantile(r$t[,2],0.75)[[1]] p01 <- quantile(r$t[,2],myq.1)[[1]] p05 <- quantile(r$t[,2],myq.2)[[1]] p95 <- quantile(r$t[,2],myq.3)[[1]] p99 <- quantile(r$t[,2],myq.4)[[1]] a<-table.element(a,signif(p01,par2)) a<-table.element(a,signif(p05,par2)) a<-table.element(a,signif(q1,par2)) a<-table.element(a,signif(r$t0[2],par2)) a<-table.element(a,signif(q3,par2)) a<-table.element(a,signif(p95,par2)) a<-table.element(a,signif(p99,par2)) a<-table.element(a,signif(sqrt(var(r$t[,2])),par2)) a<-table.element(a,signif(q3-q1,par2)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'midrange',header=TRUE) q1 <- quantile(r$t[,3],0.25)[[1]] q3 <- quantile(r$t[,3],0.75)[[1]] p01 <- quantile(r$t[,3],myq.1)[[1]] p05 <- quantile(r$t[,3],myq.2)[[1]] p95 <- quantile(r$t[,3],myq.3)[[1]] p99 <- quantile(r$t[,3],myq.4)[[1]] a<-table.element(a,signif(p01,par2)) a<-table.element(a,signif(p05,par2)) a<-table.element(a,signif(q1,par2)) a<-table.element(a,signif(r$t0[3],par2)) a<-table.element(a,signif(q3,par2)) a<-table.element(a,signif(p95,par2)) a<-table.element(a,signif(p99,par2)) a<-table.element(a,signif(sqrt(var(r$t[,3])),par2)) a<-table.element(a,signif(q3-q1,par2)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mode',header=TRUE) q1 <- quantile(r$t[,4],0.25)[[1]] q3 <- quantile(r$t[,4],0.75)[[1]] p01 <- quantile(r$t[,4],myq.1)[[1]] p05 <- quantile(r$t[,4],myq.2)[[1]] p95 <- quantile(r$t[,4],myq.3)[[1]] p99 <- quantile(r$t[,4],myq.4)[[1]] a<-table.element(a,signif(p01,par2)) a<-table.element(a,signif(p05,par2)) a<-table.element(a,signif(q1,par2)) a<-table.element(a,signif(r$t0[4],par2)) a<-table.element(a,signif(q3,par2)) a<-table.element(a,signif(p95,par2)) a<-table.element(a,signif(p99,par2)) a<-table.element(a,signif(sqrt(var(r$t[,4])),par2)) a<-table.element(a,signif(q3-q1,par2)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mode k.dens',header=TRUE) q1 <- quantile(r$t[,5],0.25)[[1]] q3 <- quantile(r$t[,5],0.75)[[1]] p01 <- quantile(r$t[,5],myq.1)[[1]] p05 <- quantile(r$t[,5],myq.2)[[1]] p95 <- quantile(r$t[,5],myq.3)[[1]] p99 <- quantile(r$t[,5],myq.4)[[1]] a<-table.element(a,signif(p01,par2)) a<-table.element(a,signif(p05,par2)) a<-table.element(a,signif(q1,par2)) a<-table.element(a,signif(r$t0[5],par2)) a<-table.element(a,signif(q3,par2)) a<-table.element(a,signif(p95,par2)) a<-table.element(a,signif(p99,par2)) a<-table.element(a,signif(sqrt(var(r$t[,5])),par2)) a<-table.element(a,signif(q3-q1,par2)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
Compute
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Raw Input
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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