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Data:
3.23507 0.265899 0.685995 -4.51963 -3.66513 -0.873463 2.71259 0.25908 -0.69719 -3.43973 -1.00121 -4.29834 0.468763 -0.945874 1.30822 -0.867992 -0.0189584 -1.24503 -3.16102 -1.57429 -4.69989 0.609592 -0.913137 1.55224 0.194162 -2.61533 -0.367329 0.623841 -1.1467 -0.0742932 -2.59339 -0.234101 -0.87143 -3.73877 -1.39391 -0.164323 -0.994389 -0.0804383 -3.25867 -1.58861 -5.54757 -0.572814 0.552778 -0.0668002 -0.162364 -1.74025 -0.367992 -5.34741 2.96674 0.185995 -1.48211 -4.54825 -0.494389 2.10185 1.45378 -0.379597 -5.01281 3.83487 0.222917 2.51378 -1.00465 0.0261874 -4.58246 2.35808 -1.00465 -1.40619 -2.45337 0.47207 1.17985 0.640175 -1.47996 0.326705 -0.818791 0.671683 1.19349 0.552778 1.27407 1.29249 -2.84876 -4.2341 -2.00121 1.97902 -1.4213 -3.81238 -3.06201 -0.367318 -0.413007 -2.1152 -3.18079 2.56709 1.69214 -4.85301 -0.327089 -2.1467 -4.60905 -1.2731 -1.95309 0.0323325 2.61224 -5.6152 -0.0134873 -0.486222 -1.63975 -3.05452 1.83764 -2.67262 0.651106 -4.39991 -3.88027 -2.58246 -0.127606 0.539826 -3.68875 1.62864 1.2146 2.11444 1.51364 1.61237 0.214597 2.2146 -0.785403 3.51221 0.573213 4.83225 -2.18667 -0.186672 0.935135 -1.48636 1.07321 1.31333 0.0927902 -0.52694 1.49264 -0.548594 1.93306 -0.428863 3.41348 -2.88829 0.573213 1.01364 0.612367 0.393905 -2.10609 0.97306 0.614443 0.97306 -1.48636 0.413482 -2.60609 0.893905 -5.62709 1.59279 0.893905 1.11444 3.61237 1.71252 1.33168 2.81333 0.913482 0.291675 2.63071 1.67129 0.214597 4.61171 2.07321 2.2321 0.831676 2.31333 0.59279 -0.787479 0.130715 3.01364 0.0142898 1.07652 1.07652 2.51364 -0.586518 2.2146 -0.385557 -1.58859 -0.828709 0.171945 2.2146 -0.307056 -4.92679 1.01364 3.41348 -1.7279 -0.428209 1.11171 2.31125 1.67194 -1.38763 1.23087 2.61237 2.71252 -2.60609 0.812674 2.97098 0.692944 1.89391 2.61237 -0.98571 0.772098 -2.3064 3.61237 -0.488441 4.87348 0.431829 1.51364 3.63194 0.193598 -1.82806 1.53321 0.614443 -1.32806 1.91348 1.47306 2.16987 0.772098 0.393905 -1.33013 0.713175 -1.2854 3.35598 1.82895 0.214597 4.61237 2.37225 0.752521 0.252521 2.2146 2.11237 -2.88556 -0.808478 -4.00944 1.51156 -3.2854 0.0332126 -1.82871 -0.586518 -2.82871 1.17194 2.07321 1.83291 1.81333 0.714597 -2.44844 -0.785403 -2.38556 -3.88556 -0.866634 3.29168 2.07114 -2.06486 2.91348 -1.98779 -3.96679 -0.186672 -6.08652 2.2321 2.51221 -0.566941 1.17194 1.7721 1.47098 3.43306 -1.26913 2.91348 2.2321 3.71317 0.533213 1.81267 2.19294 0.530561 1.81333 1.7721 2.41471
# simulations
Significant digits
Bandwidth
(?)
Quantiles
P1 P5 Q1 Q3 P95 P99
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')
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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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