R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(76.3,71,85.9,109,83.4,101.3,81.6,84.8,88,88.4,85.2,139.5,85.3,79.4,131,91.4,88.2,113.2,81.9,89,95.7,93.2,98,140.5,93,82.1,86.3,84.8,85.7,110.8,87.4,100.4,100.4,99.4,108,161.7,109.3,99.9,110.3,97.5,102.4,122.3,104.4,122.7,127.3,128.4,125.3,187.1,131.4,125.6,142.9,116.6,129.7,155.4,138.7,167.7,155.8,157.1,159.9,244.6,154.3,143.2,147.3,155.1,152.2,166.5,161.2,180.1,169.2,164.5,166.2,267.3)
> par1 = '750'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> par1 <- as.numeric(par1)
> if (par1 < 10) par1 = 10
> if (par1 > 5000) par1 = 5000
> library(lattice)
> library(boot)

Attaching package: 'boot'


	The following object(s) are masked from package:lattice :

	 melanoma 

> boot.stat <- function(s,i)
+ {
+ s.mean <- mean(s[i])
+ s.median <- median(s[i])
+ s.midrange <- (max(s[i]) + min(s[i])) / 2
+ c(s.mean, s.median, s.midrange)
+ }
> (r <- boot(x,boot.stat, R=par1, stype='i'))

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot(data = x, statistic = boot.stat, R = par1, stype = "i")


Bootstrap Statistics :
    original      bias    std. error
t1* 121.8139  0.05308889    4.323394
t2* 110.5500  2.12753333    7.634343
t3* 169.1500 -6.71680000   14.009171
> postscript(file="/var/www/html/rcomp/tmp/142fk1260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/29aax1260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/36fev1260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4653c1260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/51jmi1260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/65xos1260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange')
> dev.off()
null device 
          1 
> z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3]))
> colnames(z) <- list('mean','median','midrange')
> postscript(file="/var/www/html/rcomp/tmp/754f01260637032.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
Warning message:
In bxp(list(stats = c(110.131944444444, 118.854166666667, 121.759722222222,  :
  some notches went outside hinges ('box'): maybe set notch=FALSE
> grid()
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'statistic',header=TRUE)
> a<-table.element(a,'Q1',header=TRUE)
> a<-table.element(a,'Estimate',header=TRUE)
> a<-table.element(a,'Q3',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'IQR',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'mean',header=TRUE)
> q1 <- quantile(r$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="/var/www/html/rcomp/tmp/8lyrn1260637032.tab") 
> 
> try(system("convert tmp/142fk1260637032.ps tmp/142fk1260637032.png",intern=TRUE))
character(0)
> try(system("convert tmp/29aax1260637032.ps tmp/29aax1260637032.png",intern=TRUE))
character(0)
> try(system("convert tmp/36fev1260637032.ps tmp/36fev1260637032.png",intern=TRUE))
character(0)
> try(system("convert tmp/4653c1260637032.ps tmp/4653c1260637032.png",intern=TRUE))
character(0)
> try(system("convert tmp/51jmi1260637032.ps tmp/51jmi1260637032.png",intern=TRUE))
character(0)
> try(system("convert tmp/65xos1260637032.ps tmp/65xos1260637032.png",intern=TRUE))
character(0)
> try(system("convert tmp/754f01260637032.ps tmp/754f01260637032.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.133   1.358   4.820