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Data:
-5.82436478243877 -0.621652737567691 -0.0336349843977753 -3.02697848597682 -4.22798142293743 1.14237025833959 1.02908220413903 0.555595305330295 1.12792314453944 -0.066755862924568 1.80756299819802 -0.570227840070047 -1.72385795389156 0.670662311494532 1.84924185953611 -0.0130085030045829 3.35929054685062 -3.79205376048185 -2.45081172602413 1.19149744248616 0.847338411185 -2.73955525537295 -0.666151783627008 -0.552458673623032 2.7441584855178 2.33105322990914 1.64774669099951 1.09611748367954 -0.632560600056243 1.98791003676791 -0.549008397572291 0.599492162437574 0.903306056541775 1.0311899874954 -0.859331391379982 0.757827912930699 2.26413275889157 0.902405026904781 0.167275991587499 2.43638473812385 0.0211716991588213 1.43525954653668 -1.14947167511295 -0.311387636845242 -2.40392037348906 -0.979231137647048 0.751694772007258 -1.51621238933278 -2.43288808445548 2.30733609716212 -3.63775271903719 2.66551277987052 2.47085128254201 -0.285072905819739 2.9743563159977 -0.389296122683632 0.0323282874119362 0.795127085717838 1.64958971615139 -0.437327594270599 1.55010216285579 0.38430929385911 1.12934556771318 1.92680847663189 -0.582539015716385 -0.388300084326794 -0.354303844952615 -1.18754740234114 -2.38288687233166 -0.362641741913062 -0.112415870784045 -0.392492222438981 -2.25896768273914 -2.54002376408273 0.596952891994717 1.35411766151955 0.991802243810269 -3.28586066665939 -1.04955534285415 -1.25454580669344 -2.80023865137621 1.94995064727305 1.40160209197166 1.65576036335367 -0.667000734382826 2.67538698751204 1.8276867098052 -0.186397896026001 -0.000466366765930204 -1.20677712935019 -1.40327093169718 -0.431816076724688 4.14888400893566 0.338781736060906 -1.13660223074166 0.176852348932463 -5.72463093527342 1.78453938428773 0.622081558583671 -0.86505922146325 -0.283108461856448 -2.96383873150108 0.766813918812876 -2.28772997050001 1.48306313510734 0.602736124931916 -0.620149263941922 1.43195017798399 -0.878675803278052 1.93244957164332 0.927537636005653 0.855758217175955 -2.60592902599473 1.58587845321852 1.10749999098817 -0.995621801147396 0.582698662131357 -2.27627282668757 0.0292323481245709 1.79283336102111 0.467867252210847 -3.16243637729051 -3.05661689707854 0.850421860682173 -2.44049288212823 -2.3391887574714 1.34418226396186 -0.0110096216984463 3.80496031884146 3.59488484590709 -0.396152130068547 -0.00467543208546239 -0.370042080946616 0.0720657822655318 3.33313928698783 0.0459439394002804 3.75448227908895 -0.373351449499303 0.44677317103643 -3.97462408275447 1.27531741008058 3.02101205109756 2.25781199467474 -1.86273500193559 0.861276539314326 -3.32452405841858 0.0675616331089905 -1.55914327867897 -0.953038438192632 -1.67488692463507 -0.955234707684143 -1.46478576832075 0.0583021128497079 -0.5483258994702 1.13309775338488 -0.181361208964141 -1.28606894417658 1.18958559503454 -0.133386218064494 0.221333143554294 0.451515682896247 1.04403566500785 3.68353353416265 -1.59115952791514 -1.5072632798303 0.529748174086013 -1.72581273118185 0.37324498364813 -1.04761508998489 -0.457117685368226 1.68811173247828 1.80914322910618 1.26421247647346 2.08908233954672 -0.764667634485219 -2.384443398411 -2.74249866230798 1.07347368101824 0.69895025503657
# simulations
blockwidth of bootstrap
Significant digits
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
bandwidth
Chart options
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) par3 <- as.numeric(par3) if (par1 < 10) par1 = 10 if (par1 > 5000) par1 = 5000 if (par2 < 3) par2 = 3 if (par2 > length(x)) par2 = length(x) library(modeest) library(lattice) library(boot) boot.stat <- function(s) { s.mean <- mean(s) s.median <- median(s) s.midrange <- (max(s) + min(s)) / 2 s.mode <- mlv(s,method='mfv')$M s.kernelmode <- mlv(s, method='kernel')$M c(s.mean, s.median, s.midrange, s.mode, s.kernelmode) } (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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') grid() dev.off() bitmap(file='plot7a.png') plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode') grid() dev.off() bitmap(file='plot8a.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() z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]) ) colnames(z) <- list('mean','median','midrange','mode','mode.k.dens') bitmap(file='plot7.png') boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() if (par4 == 'P1 P5 Q1 Q3 P95 P99') { myq.1 <- 0.01 myq.2 <- 0.05 myq.3 <- 0.95 myq.4 <- 0.99 myl.1 <- 'P1' myl.2 <- 'P5' myl.3 <- 'P95' myl.4 <- 'P99' } if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') { myq.1 <- 0.005 myq.2 <- 0.025 myq.3 <- 0.975 myq.4 <- 0.995 myl.1 <- 'P0.5' myl.2 <- 'P2.5' myl.3 <- 'P97.5' myl.4 <- 'P99.5' } if (par4 == 'P10 P20 Q1 Q3 P80 P90') { myq.1 <- 0.10 myq.2 <- 0.20 myq.3 <- 0.80 myq.4 <- 0.90 myl.1 <- 'P10' myl.2 <- 'P20' myl.3 <- 'P80' myl.4 <- 'P90' } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimation Results of Blocked Bootstrap',10,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'statistic',header=TRUE) a<-table.element(a,myl.1,header=TRUE) a<-table.element(a,myl.2,header=TRUE) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,'Estimate',header=TRUE) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,myl.3,header=TRUE) a<-table.element(a,myl.4,header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'IQR',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) q1 <- quantile(r$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$t[,1],myq.4)[[1]] a<-table.element(a,signif(p01,par3)) a<-table.element(a,signif(p05,par3)) a<-table.element(a,signif(q1,par3)) a<-table.element(a,signif(r$t0[1],par3)) a<-table.element(a,signif(q3,par3)) a<-table.element(a,signif(p95,par3)) a<-table.element(a,signif(p99,par3)) a<-table.element( a,signif( sqrt(var(r$t[,1])),par3 ) ) a<-table.element(a,signif(q3-q1,par3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) q1 <- quantile(r$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$t[,2],myq.4)[[1]] a<-table.element(a,signif(p01,par3)) a<-table.element(a,signif(p05,par3)) a<-table.element(a,signif(q1,par3)) a<-table.element(a,signif(r$t0[2],par3)) a<-table.element(a,signif(q3,par3)) a<-table.element(a,signif(p95,par3)) a<-table.element(a,signif(p99,par3)) a<-table.element(a,signif(sqrt(var(r$t[,2])),par3)) a<-table.element(a,signif(q3-q1,par3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'midrange',header=TRUE) q1 <- quantile(r$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$t[,3],myq.4)[[1]] a<-table.element(a,signif(p01,par3)) a<-table.element(a,signif(p05,par3)) a<-table.element(a,signif(q1,par3)) a<-table.element(a,signif(r$t0[3],par3)) a<-table.element(a,signif(q3,par3)) a<-table.element(a,signif(p95,par3)) a<-table.element(a,signif(p99,par3)) a<-table.element(a,signif(sqrt(var(r$t[,3])),par3)) a<-table.element(a,signif(q3-q1,par3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mode',header=TRUE) q1 <- quantile(r$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$t[,4],myq.4)[[1]] a<-table.element(a,signif(p01,par3)) a<-table.element(a,signif(p05,par3)) a<-table.element(a,signif(q1,par3)) a<-table.element(a,signif(r$t0[4],par3)) a<-table.element(a,signif(q3,par3)) a<-table.element(a,signif(p95,par3)) a<-table.element(a,signif(p99,par3)) a<-table.element(a,signif(sqrt(var(r$t[,4])),par3)) a<-table.element(a,signif(q3-q1,par3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mode k.dens',header=TRUE) q1 <- quantile(r$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$t[,5],myq.4)[[1]] a<-table.element(a,signif(p01,par3)) a<-table.element(a,signif(p05,par3)) a<-table.element(a,signif(q1,par3)) a<-table.element(a,signif(r$t0[5],par3)) a<-table.element(a,signif(q3,par3)) a<-table.element(a,signif(p95,par3)) a<-table.element(a,signif(p99,par3)) a<-table.element(a,signif(sqrt(var(r$t[,5])),par3)) a<-table.element(a,signif(q3-q1,par3)) 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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