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Type 'q()' to quit R. > x <- c(361.58,363.19,363.61,364.14,365.51,365.51,365.5,365.5,364.59,364.63,364.54,363.67,365.22,369.05,370.45,370.46,370.46,370.58,370.58,370.22,370.21,370.29,370.29,370.2,370.2,372.55,374.51,375.58,375.75,375.75,375.75,375.69,375.76,377.5,377.51,377.74,369.82,373.1,374.55,375.01,374.81,375.31,375.31,375.39,375.59,376.26,377.18,377.26,377.26,381.87,387.09,387.14,388.78,389.16,389.16,389.42,389.49,388.97,388.97,389.09,389.09,391.76,390.96,391.76,392.8,393.06,393.06,393.26,393.87,394.47,394.57,394.57,394.57,399.57,406.13,407.03,409.46,409.9,409.9,410.14,410.54,410.69,410.79,410.97) > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) > if (par1 < 10) par1 = 10 > if (par1 > 5000) par1 = 5000 > library(lattice) > library(boot) Attaching package: 'boot' The following object(s) are masked from package:lattice : melanoma > boot.stat <- function(s,i) + { + s.mean <- mean(s[i]) + s.median <- median(s[i]) + s.midrange <- (max(s[i]) + min(s[i])) / 2 + c(s.mean, s.median, s.midrange) + } > (r <- boot(x,boot.stat, R=par1, stype='i')) ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = x, statistic = boot.stat, R = par1, stype = "i") Bootstrap Statistics : original bias std. error t1* 381.7045 0.1981786 1.4901024 t2* 376.0100 1.3991000 3.3441682 t3* 386.2750 0.2376000 0.4270631 > postscript(file="/var/www/html/rcomp/tmp/1ct2b1290595793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2n21e1290595793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3n21e1290595793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4n21e1290595793.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5qm3c1290595794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/6qm3c1290595794.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/7qm3c1290595794.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(379.250952380952, 380.987380952381, 381.965059523810, : 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/8x51o1290595794.tab") > > try(system("convert tmp/1ct2b1290595793.ps tmp/1ct2b1290595793.png",intern=TRUE)) character(0) > try(system("convert tmp/2n21e1290595793.ps tmp/2n21e1290595793.png",intern=TRUE)) character(0) > try(system("convert tmp/3n21e1290595793.ps tmp/3n21e1290595793.png",intern=TRUE)) character(0) > try(system("convert tmp/4n21e1290595793.ps tmp/4n21e1290595793.png",intern=TRUE)) character(0) > try(system("convert tmp/5qm3c1290595794.ps tmp/5qm3c1290595794.png",intern=TRUE)) character(0) > try(system("convert tmp/6qm3c1290595794.ps tmp/6qm3c1290595794.png",intern=TRUE)) character(0) > try(system("convert tmp/7qm3c1290595794.ps tmp/7qm3c1290595794.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.709 1.045 3.866