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Type 'q()' to quit R. > x <- c(102.8,106.3,103.7,106.9,104.3,105.4,96.2,95.7,95.9,93.6,94.7,94.5,96.6,96.7,98.9,102,105.2,106.4,99.3,96.4,93.1,95.6,93.3,96.7,105.6,105.2,107,104.9,104.5,105.2,99.7,100.2,98.5,98.4,97.1,98.4,100.6,111.3,119,117.8,108.8,109.3,103.5,103.7,110,105.5,110.4,106.7,110.2,105.2,108,108.1,107.2,106,99.4,100.2,100.3,100.8,99.5,100.2,103,111,120.5,109.5,106.6,105.5,103.9,104.9,104.8,99.6,97,95.4,99.3,103.9,107.4,107.4,111,113.2,108.5,113.3,113.8,105.3,107.5,109.4,118.9,119,115,124.1,120.5,117.7,117.1,118.1,119.6,118.8,124.9,124,124.9,121.7,121.6,125.1,127.9,129,130.1,130.3,127.9,124.1,125.7,129.2,129.2,132.6,131.5,131,125.8,127.2,127.3,127.5,122,118.4,118.3,115.5) > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) > if (par1 < 10) par1 = 10 > if (par1 > 5000) par1 = 5000 > library(lattice) > library(boot) Attaching package: 'boot' The following object(s) are masked from 'package:lattice': melanoma > boot.stat <- function(s,i) + { + s.mean <- mean(s[i]) + s.median <- median(s[i]) + s.midrange <- (max(s[i]) + min(s[i])) / 2 + c(s.mean, s.median, s.midrange) + } > (r <- boot(x,boot.stat, R=par1, stype='i')) ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = x, statistic = boot.stat, R = par1, stype = "i") Bootstrap Statistics : original bias std. error t1* 109.8317 -0.2592 0.9441858 t2* 106.9500 -0.2940 0.9587215 t3* 112.8500 -0.1260 0.3095817 > postscript(file="/var/www/rcomp/tmp/1qwew1290868282.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/2qwew1290868282.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3qwew1290868282.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/415vz1290868282.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/515vz1290868282.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/615vz1290868282.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/rcomp/tmp/715vz1290868282.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(107.563333333333, 108.955, 109.590833333333, : some notches went outside hinges ('box'): maybe set notch=FALSE > grid() > dev.off() null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/87ost1290868282.tab") > > try(system("convert tmp/1qwew1290868282.ps tmp/1qwew1290868282.png",intern=TRUE)) character(0) > try(system("convert tmp/2qwew1290868282.ps tmp/2qwew1290868282.png",intern=TRUE)) character(0) > try(system("convert tmp/3qwew1290868282.ps tmp/3qwew1290868282.png",intern=TRUE)) character(0) > try(system("convert tmp/415vz1290868282.ps tmp/415vz1290868282.png",intern=TRUE)) character(0) > try(system("convert tmp/515vz1290868282.ps tmp/515vz1290868282.png",intern=TRUE)) character(0) > try(system("convert tmp/615vz1290868282.ps tmp/615vz1290868282.png",intern=TRUE)) character(0) > try(system("convert tmp/715vz1290868282.ps tmp/715vz1290868282.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.440 0.790 3.191