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Data:
43 30 30 54 30 16 42 0 30 44 70 30 5 30 62 91 41 73 60 20 4 60 62 60 76 65 60 88 16 65 35 70 21 60 100 65 80 65 60 31 55 74 32 10 20 40 55 70 80 50 55 29 70 50 60 60 27 38 70 15 40 37 10 75 60 55 91 29 50 10 57 45 70 38 70 40 61 15 25 54 36 50 68 14 68 100 74 59 50 60 60 70 45 60 21 0 65 33 70 20 60 65 60 53 71 32 70 60 60 50 25 20 80 53 39 53 39 70 60 77 80 50 69 70 36 30 57 80 91 8 60 63 60 18 39 41 50 65 80 68 58 30 60 100
# 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) c(s.mean, s.median) } (r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed')) z <- data.frame(cbind(r$t[,1],r$t[,2])) colnames(z) <- list('mean','median') bitmap(file='plot7.png') b <- boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() b 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.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'95% Confidence Intervals',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Mean',1,TRUE) a<-table.element(a,'Median',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Lower Bound',1,TRUE) a<-table.element(a,b$conf[1,1]) a<-table.element(a,b$conf[1,2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Upper Bound',1,TRUE) a<-table.element(a,b$conf[2,1]) a<-table.element(a,b$conf[2,2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab')
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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