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Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationSun, 23 Aug 2015 04:20:30 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Aug/23/t1440300052loj4xj24uwhtn0e.htm/, Retrieved Wed, 15 May 2024 13:30:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280319, Retrieved Wed, 15 May 2024 13:30:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
- R PD    [Bootstrap Plot - Central Tendency] [] [2015-08-23 03:20:30] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataseries X:
-2.09932
-4.84251
-2.43717
2.23842
-0.994156
-6.30191
-1.61435
-3.71349
-1.76023
-2.08391
-1.03251
-0.587098
-2.77059
-3.09963
-3.05576
-0.21967
-2.60986
0.0242731
-4.91041
-6.69324
-0.668194
-3.80288
-5.06502
-1.62139
-1.24041
-2.82753
-3.28203
-5.70037
0.923769
0.380148
-0.851016
-2.63918
-3.05701
0.691438
-1.10207
-3.28964
0.0724608
-4.62822
0.764143
-3.4116
-3.89861
-1.66264
-5.11018
-1.96328
-7.39661
-7.39808
-4.5128
-1.50231
0.90303
4.60453
-0.106345
2.12746
2.39272
5.82691
0.196652
2.99066
1.3657
-2.98911
0.0516059
0.0511147
-1.5868
2.89534
2.98175
-0.154392
-1.97188
4.66668
1.71898
4.33017
6.10227
-0.0105635
-1.4616
3.66895
5.95893
-5.90954
-1.23771
-0.765454
2.93981
1.48673
3.81607
0.360167
-0.849121
4.42105
-2.21143
2.12976
0.0836164
1.41483
1.88848
-0.125797
3.88288
0.650323
6.22632
-0.86777
1.46702
3.79771
-2.66756
2.30125
5.98084
-3.92821
7.11718
1.47472
2.37792
5.76448
4.95993
3.79232
-1.1497
-0.582457
0.750941
5.18233
2.90061
2.3492
-0.643702
3.27654
3.23586
-3.5607
0.0717344
-0.106063
2.67257
-2.27157
3.03075
3.26596
1.78739
3.94493
-0.0137698
3.89776




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280319&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280319&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280319&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'Herman Ole Andreas Wold' @ wold.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.63298-0.42526-0.20337-3.3871e-070.230610.477370.627990.291370.43398
median-0.86065-0.66819-0.15439-0.0599160.0516060.360170.671090.304480.206
midrange-0.70872-0.64791-0.58588-0.14045-0.139710.211970.711360.325080.44617
mode-5.126-3.4116-1.4622-3.3871e-071.76394.62235.98212.47083.2261
mode k.dens-2.3747-1.7961-0.89657-0.60558-0.179711.28972.95450.946020.71686

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.63298 & -0.42526 & -0.20337 & -3.3871e-07 & 0.23061 & 0.47737 & 0.62799 & 0.29137 & 0.43398 \tabularnewline
median & -0.86065 & -0.66819 & -0.15439 & -0.059916 & 0.051606 & 0.36017 & 0.67109 & 0.30448 & 0.206 \tabularnewline
midrange & -0.70872 & -0.64791 & -0.58588 & -0.14045 & -0.13971 & 0.21197 & 0.71136 & 0.32508 & 0.44617 \tabularnewline
mode & -5.126 & -3.4116 & -1.4622 & -3.3871e-07 & 1.7639 & 4.6223 & 5.9821 & 2.4708 & 3.2261 \tabularnewline
mode k.dens & -2.3747 & -1.7961 & -0.89657 & -0.60558 & -0.17971 & 1.2897 & 2.9545 & 0.94602 & 0.71686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280319&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]-0.63298[/C][C]-0.42526[/C][C]-0.20337[/C][C]-3.3871e-07[/C][C]0.23061[/C][C]0.47737[/C][C]0.62799[/C][C]0.29137[/C][C]0.43398[/C][/ROW]
[ROW][C]median[/C][C]-0.86065[/C][C]-0.66819[/C][C]-0.15439[/C][C]-0.059916[/C][C]0.051606[/C][C]0.36017[/C][C]0.67109[/C][C]0.30448[/C][C]0.206[/C][/ROW]
[ROW][C]midrange[/C][C]-0.70872[/C][C]-0.64791[/C][C]-0.58588[/C][C]-0.14045[/C][C]-0.13971[/C][C]0.21197[/C][C]0.71136[/C][C]0.32508[/C][C]0.44617[/C][/ROW]
[ROW][C]mode[/C][C]-5.126[/C][C]-3.4116[/C][C]-1.4622[/C][C]-3.3871e-07[/C][C]1.7639[/C][C]4.6223[/C][C]5.9821[/C][C]2.4708[/C][C]3.2261[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-2.3747[/C][C]-1.7961[/C][C]-0.89657[/C][C]-0.60558[/C][C]-0.17971[/C][C]1.2897[/C][C]2.9545[/C][C]0.94602[/C][C]0.71686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280319&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280319&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-0.63298-0.42526-0.20337-3.3871e-070.230610.477370.627990.291370.43398
median-0.86065-0.66819-0.15439-0.0599160.0516060.360170.671090.304480.206
midrange-0.70872-0.64791-0.58588-0.14045-0.139710.211970.711360.325080.44617
mode-5.126-3.4116-1.4622-3.3871e-071.76394.62235.98212.47083.2261
mode k.dens-2.3747-1.7961-0.89657-0.60558-0.179711.28972.95450.946020.71686



Parameters (Session):
Parameters (R input):
par1 = 200 ; 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')