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Data:
-2.11377 1.62659 -1.24809 -3.02809 9.48726 1.9426 8.40414 -1.58916 -2.28244 0.851045 -0.360797 -2.3704 -0.986595 1.53982 0.812351 0.082788 1.11946 1.50256 -3.14541 0.545672 -1.06176 -1.42175 -1.56273 -1.26661 1.19856 -6.73545 -0.333925 1.75566 2.03471 -2.62398 -2.28299 -0.382925 -1.01934 -0.624919 -0.0244947 -4.34509 2.1962 -0.436158 -0.825006 -3.87832 -3.10309 2.43302 -3.98882 -1.71861 -1.49985 -2.69772 -3.72161 -1.25563 4.2709 -2.56223 -1.60474 0.465273 1.96112 -0.969953 -4.77282 2.20375 1.71293 -3.71927 -4.38384 0.459231 1.10493 -1.28626 -2.62707 -0.415303 -0.260549 -4.25689 1.834 -2.28382 -0.0121782 1.56911 -0.788538 2.09814 1.42428 -2.12916 -1.97728 5.24104 2.14003 3.12924 0.56089 -5.5563 -0.93046 -3.31904 0.509069 -0.45635 0.228635 0.731929 -1.18701 0.145049 3.80745 1.65512 0.543997 0.988396 2.46021 -1.15405 -0.018439 1.54144 -1.676 0.749892 -2.36223 -0.458006 1.71229 1.64389 -4.5951 4.02201 2.17768 -0.126093 3.86451 -1.80596 6.46585 2.32036 -0.047381 -1.85524 -0.240378 1.77236 -0.463133 -0.21987 -1.592045 -4.48776 3.13808 -0.76342 1.21427 -0.494876 -3.92769 -1.19471 -2.31862 -1.80878 -0.38355 1.85838 0.382216 -2.86527 2.37688 -1.92453 1.74833 -0.270601 3.04365 6.41317 0.387799 -0.764125 -2.15154 -2.49602 -3.45325 2.78629 -0.173696 -0.0432014 -0.0344116 0.848073 -3.00692 -3.28598 2.21334 0.593313 3.87359 -2.72697 -2.56981 2.13703 3.9858 0.543997 0.333075 1.85838 -4.03888 3.70604 1.7475 7.33833 1.0124 9.2764 1.99121 5.93146 -0.980061 -0.942786 -0.279144 1.26126 3.50741 0.481168 -4.39316 1.7181 1.18809 -4.55051 -1.12299 2.88395 -2.15201 -1.33349 -2.94118 -5.52027 -1.1531 -2.48216 -0.678616 5.44613 1.25409 0.261203 -1.43738 4.55404 -4.27998 -2.72896 1.90415 0.305804 1.3531 -2.53925 6.34699 2.79858 0.902978 -0.667428 0.185587 -0.975019 -0.188145 -1.04664 -1.50271 0.707313 -0.273833 2.33671 -0.234915 0.972595 -0.339769 -0.0886144 3.50203 -2.75269 1.62594 -0.0318217 0.897482 -0.437463 3.81627 2.22594 -3.12392 -0.459963 1.06037 -3.79536 2.19481 -2.17932 0.247934 -3.20275 4.89235 -2.67488 -1.43997 -1.47114 -4.28241 3.771 -2.86969 -1.64354 1.71396 -2.08022 -4.97526 -3.66011 -4.19274 0.326123 -0.119846 0.869543 2.26227 4.07807 0.748261 7.91382 2.40876 -0.236992 0.936757 2.95233 -3.20266 -0.795812 2.06391 0.113795 4.63661 0.529048 2.25051 -8.46241 -1.75167 -0.972249 3.08095 -1.3944
# 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')
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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