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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationFri, 12 Dec 2014 10:36:51 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/12/t1418380676wecdhh06h1bvn88.htm/, Retrieved Thu, 16 May 2024 04:34:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266483, Retrieved Thu, 16 May 2024 04:34:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2014-12-12 10:36:51] [478aede7ebf40ce01402b5cfefb74b76] [Current]
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Dataseries X:
-0.304577
-1.00458
-0.404577
-5.80458
-6.50458
-0.604577
1.59542
0.0954225
-2.10458
-5.00458
-1.80458
-6.80458
-2.60458
-1.20458
-6.90458
-1.46963
-1.30458
-3.90458
-3.16963
-3.20458
-6.80458
0.595423
-2.40458
0.595423
-1.50458
-2.30458
3.33037
0.63037
-3.30458
-1.70458
-4.90458
-1.50458
-4.20458
-3.50458
-2.40458
-2.90458
-2.80458
-0.0696296
-3.90458
-1.40458
-7.30458
-1.80458
-0.204577
-2.40458
-0.46963
-1.90458
-1.40458
-4.86963
-0.504577
-0.46963
-1.16963
-6.06963
-2.30458
-0.66963
0.0954225
-3.10458
-7.06963
1.09542
-4.76963
0.53037
-3.90458
-0.704577
-5.60458
2.69542
-4.00458
-3.66963
-2.10458
-0.204577
1.29542
-0.56963
-0.904577
-1.80458
-3.96963
1.83037
-0.604577
-0.204577
-0.16963
-0.00457746
-2.86963
-5.50458
-2.26963
0.63037
-1.86963
-8.46963
-2.46963
-0.96963
-1.56963
-1.36963
-4.16963
0.43037
-0.16963
-7.16963
-2.86963
-3.96963
-5.06963
-3.76963
-5.46963
-1.36963
0.83037
-4.86963
-2.06963
-2.46963
-4.46963
-3.16963
1.43037
-4.26963
0.73037
-7.86963
-6.36963
-3.16963
-1.16963
-1.66963
-8.85458
-0.504577
4.89542
4.64542
3.83037
-0.16963
3.89542
5.89542
2.89542
0.145423
5.19542
1.49542
-2.60458
-0.604577
2.99542
0.395423
6.13037
0.895423
1.29542
2.94542
1.54542
1.59542
-0.754577
-0.554577
4.14542
-4.60458
5.19542
2.89542
-1.16963
4.54542
2.04542
4.44542
3.14542
4.44542
0.395423
1.14542
1.54542
5.04542
-3.30458
2.79542
5.04542
3.64542
1.83037
1.08037
5.74542
2.39542
2.08037
-1.01963
5.68037
3.13037
3.89542
2.89542
7.13037
-1.81963
5.68037
2.33037
2.23037
-1.41963
3.18037
5.33037
1.83037
2.19542
2.19542
4.83037
0.145423
5.89542
2.58037
-5.60458
0.63037
1.13037
5.89542
2.48037
0.13037
3.33037
4.58037
0.38037
-0.61963
-0.16963
-2.41963
2.63037
-3.16963
5.43037
0.83037
2.08037
1.54542
1.33037
2.13037
3.48037
6.04542
0.83037
0.395423
2.88037
-0.454577
1.83037
-3.35458
-0.11963
6.43037
3.83037
-1.56963
2.04542
-1.30458
0.43037
3.14542
-0.804577
3.08037
4.94542
-1.61963
2.88037
4.54542
-5.11963
-0.854577
2.39542
6.09542
2.43037
3.89542
2.83037
5.19542
5.84542
5.34542
5.89542
0.33037
-0.354577
-3.70458
-8.70458
-0.91963
0.395423
-1.50458
-0.36963
0.145423
-1.36963
2.13037
7.13037
-1.56963
1.83037
4.39542
0.845423
2.89542
0.145423
-1.35458
-1.25458
1.98037
2.38037
-0.00457746
4.08037
-4.91963
-5.50458
-0.16963
-4.91963
-1.81963
-0.41963
-2.81963
2.13037
3.88037
0.63037
1.18037
2.93037
4.08037
-1.81963
2.58037
-0.56963
2.33037
4.98037
2.43037
1.39542
3.88037




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266483&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266483&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266483&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.50592-0.35734-0.173-1.1174e-060.137310.365290.477180.221040.31031
median-0.57802-0.46963-0.20458-0.119630.0954220.395420.430370.242820.3
midrange-1.3796-1.2121-0.86211-0.86211-0.7871-0.66963-0.0871050.198330.075
mode-3.9202-3.1696-0.169630.830371.83045.19545.89542.15022
mode k.dens-1.2666-1.1063-0.75871-0.4673-0.226020.320892.05880.549460.53269

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.50592 & -0.35734 & -0.173 & -1.1174e-06 & 0.13731 & 0.36529 & 0.47718 & 0.22104 & 0.31031 \tabularnewline
median & -0.57802 & -0.46963 & -0.20458 & -0.11963 & 0.095422 & 0.39542 & 0.43037 & 0.24282 & 0.3 \tabularnewline
midrange & -1.3796 & -1.2121 & -0.86211 & -0.86211 & -0.7871 & -0.66963 & -0.087105 & 0.19833 & 0.075 \tabularnewline
mode & -3.9202 & -3.1696 & -0.16963 & 0.83037 & 1.8304 & 5.1954 & 5.8954 & 2.1502 & 2 \tabularnewline
mode k.dens & -1.2666 & -1.1063 & -0.75871 & -0.4673 & -0.22602 & 0.32089 & 2.0588 & 0.54946 & 0.53269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266483&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P1[/C][C]P5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P95[/C][C]P99[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]-0.50592[/C][C]-0.35734[/C][C]-0.173[/C][C]-1.1174e-06[/C][C]0.13731[/C][C]0.36529[/C][C]0.47718[/C][C]0.22104[/C][C]0.31031[/C][/ROW]
[ROW][C]median[/C][C]-0.57802[/C][C]-0.46963[/C][C]-0.20458[/C][C]-0.11963[/C][C]0.095422[/C][C]0.39542[/C][C]0.43037[/C][C]0.24282[/C][C]0.3[/C][/ROW]
[ROW][C]midrange[/C][C]-1.3796[/C][C]-1.2121[/C][C]-0.86211[/C][C]-0.86211[/C][C]-0.7871[/C][C]-0.66963[/C][C]-0.087105[/C][C]0.19833[/C][C]0.075[/C][/ROW]
[ROW][C]mode[/C][C]-3.9202[/C][C]-3.1696[/C][C]-0.16963[/C][C]0.83037[/C][C]1.8304[/C][C]5.1954[/C][C]5.8954[/C][C]2.1502[/C][C]2[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.2666[/C][C]-1.1063[/C][C]-0.75871[/C][C]-0.4673[/C][C]-0.22602[/C][C]0.32089[/C][C]2.0588[/C][C]0.54946[/C][C]0.53269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266483&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266483&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.50592-0.35734-0.173-1.1174e-060.137310.365290.477180.221040.31031
median-0.57802-0.46963-0.20458-0.119630.0954220.395420.430370.242820.3
midrange-1.3796-1.2121-0.86211-0.86211-0.7871-0.66963-0.0871050.198330.075
mode-3.9202-3.1696-0.169630.830371.83045.19545.89542.15022
mode k.dens-1.2666-1.1063-0.75871-0.4673-0.226020.320892.05880.549460.53269



Parameters (Session):
par1 = 277 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 277 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par3 == '0') bw <- NULL
if (par3 != '0') bw <- as.numeric(par3)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
library(modeest)
library(lattice)
library(boot)
boot.stat <- function(s,i)
{
s.mean <- mean(s[i])
s.median <- median(s[i])
s.midrange <- (max(s[i]) + min(s[i])) / 2
s.mode <- mlv(s[i], method='mfv')$M
s.kernelmode <- mlv(s[i], method='kernel', bw=bw)$M
c(s.mean, s.median, s.midrange, s.mode, s.kernelmode)
}
(r <- boot(x,boot.stat, R=par1, stype='i'))
bitmap(file='plot1.png')
plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean')
grid()
dev.off()
bitmap(file='plot2.png')
plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median')
grid()
dev.off()
bitmap(file='plot3.png')
plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot7.png')
plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')
grid()
dev.off()
bitmap(file='plot8.png')
plot(r$t[,5],type='p',ylab='simulated values',main='Simulation of Mode of Kernel Density')
grid()
dev.off()
bitmap(file='plot4.png')
densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean')
dev.off()
bitmap(file='plot5.png')
densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median')
dev.off()
bitmap(file='plot6.png')
densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange')
dev.off()
bitmap(file='plot9.png')
densityplot(~r$t[,4],col='black',main='Density Plot',xlab='mode')
dev.off()
bitmap(file='plot10.png')
densityplot(~r$t[,5],col='black',main='Density Plot',xlab='mode of kernel dens.')
dev.off()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
p01 <- quantile(r$t[,1],myq.1)[[1]]
p05 <- quantile(r$t[,1],myq.2)[[1]]
p95 <- quantile(r$t[,1],myq.3)[[1]]
p99 <- quantile(r$t[,1],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[1],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,par2))
a<-table.element( a,signif( sqrt(var(r$t[,1])),par2 ) )
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$t[,2],0.25)[[1]]
q3 <- quantile(r$t[,2],0.75)[[1]]
p01 <- quantile(r$t[,2],myq.1)[[1]]
p05 <- quantile(r$t[,2],myq.2)[[1]]
p95 <- quantile(r$t[,2],myq.3)[[1]]
p99 <- quantile(r$t[,2],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[2],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,par2))
a<-table.element(a,signif(sqrt(var(r$t[,2])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$t[,3],0.25)[[1]]
q3 <- quantile(r$t[,3],0.75)[[1]]
p01 <- quantile(r$t[,3],myq.1)[[1]]
p05 <- quantile(r$t[,3],myq.2)[[1]]
p95 <- quantile(r$t[,3],myq.3)[[1]]
p99 <- quantile(r$t[,3],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[3],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,par2))
a<-table.element(a,signif(sqrt(var(r$t[,3])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode',header=TRUE)
q1 <- quantile(r$t[,4],0.25)[[1]]
q3 <- quantile(r$t[,4],0.75)[[1]]
p01 <- quantile(r$t[,4],myq.1)[[1]]
p05 <- quantile(r$t[,4],myq.2)[[1]]
p95 <- quantile(r$t[,4],myq.3)[[1]]
p99 <- quantile(r$t[,4],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[4],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,par2))
a<-table.element(a,signif(sqrt(var(r$t[,4])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode k.dens',header=TRUE)
q1 <- quantile(r$t[,5],0.25)[[1]]
q3 <- quantile(r$t[,5],0.75)[[1]]
p01 <- quantile(r$t[,5],myq.1)[[1]]
p05 <- quantile(r$t[,5],myq.2)[[1]]
p95 <- quantile(r$t[,5],myq.3)[[1]]
p99 <- quantile(r$t[,5],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[5],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,par2))
a<-table.element(a,signif(sqrt(var(r$t[,5])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')