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Data:
1576.23 1546.37 1545.05 1552.34 1594.3 1605.78 1673.21 1612.94 1566.34 1530.17 1582.54 1702.16 1701.93 1811.15 1924.2 2034.25 2011.13 2013.04 2151.67 1902.09 1944.01 1916.67 1967.31 2119.88 2216.38 2522.83 2647.64 2631.23 2693.41 3021.76 2953.67 2796.8 2672.05 2251.23 2046.08 2420.04 2608.89 2660.47 2493.98 2541.7 2554.6 2699.61 2805.48 2956.66 3149.51 3372.5 3379.33 3517.54 3527.34 3281.06 3089.65 3222.76 3165.76 3232.43 3229.54 3071.74 2850.17
# 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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