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Type 'q()' to quit R. > x <- c(101.02,101.15,101.51,101.75,101.8,101.8,101.8,101.82,101.99,102.25,102.34,102.35,102.35,102.39,102.49,102.67,102.68,102.7,102.71,102.72,102.83,102.92,103.04,103.08,103.09,103.11,103.18,103.18,103.22,103.25,103.25,103.25,103.47,103.57,103.66,103.7,103.7,103.75,103.85,104.02,104.13,104.17,104.18,104.2,104.5,104.78,104.88,104.89,104.9,104.95,105.24,105.35,105.44,105.46,105.47,105.48,105.75,106.1,106.19,106.23,106.24,106.25,106.35,106.48,106.52,106.55,106.55,106.56,106.89,107.09,107.24,107.28,107.3,107.31,107.47,107.35,107.31,107.32,107.32,107.34,107.53,107.72,107.75,107.79,107.81,107.9,107.8,107.86,107.8,107.74,107.75,107.83,107.8,107.81,107.86,107.83) > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) > if (par1 < 10) par1 = 10 > if (par1 > 5000) par1 = 5000 > library(lattice) > library(boot) Attaching package: 'boot' The following object(s) are masked from package:lattice : melanoma > boot.stat <- function(s,i) + { + s.mean <- mean(s[i]) + s.median <- median(s[i]) + s.midrange <- (max(s[i]) + min(s[i])) / 2 + c(s.mean, s.median, s.midrange) + } > (r <- boot(x,boot.stat, R=par1, stype='i')) ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = x, statistic = boot.stat, R = par1, stype = "i") Bootstrap Statistics : original bias std. error t1* 104.9479 -0.01953125 0.1892305 t2* 104.8950 -0.05640000 0.4866263 t3* 104.4600 0.04170000 0.1141187 > postscript(file="/var/www/html/rcomp/tmp/1x7sx1291147718.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2pgrz1291147718.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3pgrz1291147718.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4pgrz1291147718.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5ip8k1291147718.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/6ip8k1291147718.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/www/html/rcomp/tmp/7ip8k1291147718.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/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/8p9ne1291147718.tab") > > try(system("convert tmp/1x7sx1291147718.ps tmp/1x7sx1291147718.png",intern=TRUE)) character(0) > try(system("convert tmp/2pgrz1291147718.ps tmp/2pgrz1291147718.png",intern=TRUE)) character(0) > try(system("convert tmp/3pgrz1291147718.ps tmp/3pgrz1291147718.png",intern=TRUE)) character(0) > try(system("convert tmp/4pgrz1291147718.ps tmp/4pgrz1291147718.png",intern=TRUE)) character(0) > try(system("convert tmp/5ip8k1291147718.ps tmp/5ip8k1291147718.png",intern=TRUE)) character(0) > try(system("convert tmp/6ip8k1291147718.ps tmp/6ip8k1291147718.png",intern=TRUE)) character(0) > try(system("convert tmp/7ip8k1291147718.ps tmp/7ip8k1291147718.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.628 1.040 4.122