R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(162.9,164.7,165,167.2,168.6,169.5,169.8,171.9,172,173.7,173.9,175.9,175.6,176.1,176.3,179.4,179.7,179.9,180.4,182.5,183.6,183.9,184.5,187.6,188,188.5,188.6,191.9,193.5,194.9,194.9,196.2,196.2,198,198.6,201.3,203.5,204.1,204.8,206.5,207.8,208.6,209.7,210,211.7,212.4,213.7,214.8,216.4,217.5,218.6,220.4,221.8,222.5,223.4,225.5,226.5,227.8,228.5,229.1,229.9,230.8,231.9,236,237.5,239.1,240.5,241.4,243.2,243.6,244.3,244.5,245.1,245.8,246.7,247.7,248.5)
> par1 = '50'
> #'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* 205.4195 0.354987   2.8396923
t2* 204.8000 0.162000   5.4680851
t3* 205.7000 0.280000   0.8331156
> postscript(file="/var/wessaorg/rcomp/tmp/102f21322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/20ii41322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/3d7km1322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/4qqn31322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean')
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/5q64c1322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median')
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/6ziii1322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> densityplot(~r$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$t[,3]))
> colnames(z) <- list('mean','median','midrange')
> postscript(file="/var/wessaorg/rcomp/tmp/7wmqk1322570303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
> grid()
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'statistic',header=TRUE)
> a<-table.element(a,'Q1',header=TRUE)
> a<-table.element(a,'Estimate',header=TRUE)
> a<-table.element(a,'Q3',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'IQR',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'mean',header=TRUE)
> q1 <- quantile(r$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/wessaorg/rcomp/tmp/8q81q1322570303.tab") 
> 
> try(system("convert tmp/102f21322570303.ps tmp/102f21322570303.png",intern=TRUE))
character(0)
> try(system("convert tmp/20ii41322570303.ps tmp/20ii41322570303.png",intern=TRUE))
character(0)
> try(system("convert tmp/3d7km1322570303.ps tmp/3d7km1322570303.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qqn31322570303.ps tmp/4qqn31322570303.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q64c1322570303.ps tmp/5q64c1322570303.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ziii1322570303.ps tmp/6ziii1322570303.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wmqk1322570303.ps tmp/7wmqk1322570303.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.258   0.358   2.648