Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
99.8 96.8 87.0 96.3 107.1 115.2 106.1 89.5 91.3 97.6 100.7 104.6 94.7 101.8 102.5 105.3 110.3 109.8 117.3 118.8 131.3 125.9 133.1 147.0 145.8 164.4 149.8 137.7 151.7 156.8 180.0 180.4 170.4 191.6 199.5 218.2 217.5 205.0 194.0 199.3 219.3 211.1 215.2 240.2 242.2 240.7 255.4 253.0 218.2 203.7 205.6 215.6 188.5 202.9 214.0 230.3 230.0 241.0 259.6 247.8 270.3
# 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) s.midrange <- (max(s) + min(s)) / 2 c(s.mean, s.median, s.midrange) } (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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') grid() dev.off() bitmap(file='plot4.png') densityplot(~r$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$t[,3])) colnames(z) <- list('mean','median','midrange') bitmap(file='plot7.png') boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'statistic',header=TRUE) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,'Estimate',header=TRUE) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'IQR',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) q1 <- quantile(r$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$t[,2]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'midrange',header=TRUE) q1 <- quantile(r$t[,3],0.25)[[1]] q3 <- quantile(r$t[,3],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[3]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,3]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation