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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 computationWed, 17 Dec 2014 15:53:16 +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/17/t1418831653vacavjaf7mqrml8.htm/, Retrieved Thu, 16 May 2024 20:22:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270443, Retrieved Thu, 16 May 2024 20:22:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2014-11-04 10:08:09] [32b17a345b130fdf5cc88718ed94a974]
- RMP   [Bootstrap Plot - Central Tendency] [WS7 4] [2014-11-12 20:43:46] [81f624c2f0b20a2549c93e7c3dccf981]
- R  D      [Bootstrap Plot - Central Tendency] [Mediaan] [2014-12-17 15:53:16] [8188a2bb20af439749c29996b06d1031] [Current]
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Dataseries X:
-26.3309
9.67792
-15.5831
12.2468
-9.58307
0.501988
0.414137
-8.49801
25.7395
11.9947
-4.66813
16.4962
1.17059
2.16176
19.505
6.8151
-12.1696
2.74833
4.48673
21.0005
7.4081
8.24682
3.41112
6.6749
11.4141
0.416931
5.23457
-4.1849
10.3319
-2.42179
-22.7502
0.0797212
4.33187
-12.5861
-3.33673
5.17361
7.92728
-0.269955
8.41693
-0.826387
-13.9995
-2.82941
-1.83802
-19.3367
-24.7684
21.5108
-5.33673
6.48371
19.6779
-5.34618
2.56876
-1.09682
-11.3367
9.39283
4.82455
4.00048
-11.0176
0.836178
-5.34036
-7.1849
7.74833
5.8392
-6.57423
-11.3367
3.41995
-8.51048
13.0005
5.83338
-5.49801
-17.5163
-4.00534
-1.99952
5.82394
16.3282
-8.40412
-10.8352
-14.5105
12.7483
-18.5983
-1.33673
-4.43124
2.65382
-6.1849
6.65382
-13.9355
3.23155
4.8151
13.909
3.90597
1.48952
1.98824
-17.2611
10.5599
-21.0148
-4.76263
11.8151
3.48371
4.48952
-7.41961
14.0761
3.23155
10.8151
-25.1733
3.64499
1.15533
-3.35199
5.07609
-3.01478
8.73888
4.99103
8.48371
12.249
-1.08737
-37.9203
-8.33673
1.66327
1.57178
0.483707
-2.99952
5.33187
6.49617
-1.58027
-5.4922
10.7453
-0.835221
-1.9233
-16.0874
-4.26051
16.8151
-4.42179
-6.33673
6.33187
-0.665106
9.91263
-22.5861
12.6633
-9.00534
-4.26051
6.41693
5.99466
-13.0001
-8.16964
15.3319
-2.33673
0.663269
-11.5831
18.6633
-2.33673
-4.33673
-1.99952
8.66629
3.32908
1.66327
-24.6681
11.2316
11.648
17.7425
1.33187
8.39865
-15.1849
-9.1849
2.9794
-9.16662
5.74251
2.16115
-4.51629
-0.59833
-0.516293
14.0703
6.23155
20.8913
-1.18188
5.15533
2.74833
2.74833
-1.09984
-7.75318
-23.9114
-8.1849
-0.653477
-0.176063
-8.17304
-2.25167
3.64801
-16.7654
3.48371
7.23737
10.0703
11.3224
-0.519088
-6.18188
12.2316
5.15231
10.3166
-12.3404
-5.1849
-3.33371
-6.76845
-3.84467
11.648
-8.16964
9.73306
6.24984
-10.352
-0.41016
-11.5163
-1.09039
-13.5133
-0.419381
2.49836
-1.8535
1.74251
7.58123
5.16394
3.66908
-3.33092
-16.6834
2.90961
-7.93554
-2.84769
4.74833
5.97638
-0.920279
-2.41295
-3.33673
-3.17606
3.41391
7.48371
4.82455
-2.66813
12.2498
0.249836
10.73
-4.75318
-4.26051
4.07972
7.5776
11.502
-22.2517
6.97638
15.8304
-6.26996
1.8151
-1.83885
-5.25833
4.40167
-13.3425
-11.7474
10.4169
9.74833
1.91542
-1.99952
-5.1849
-33.0206
-9.41295
5.31661
-0.269955
11.9154
3.07609
-12.7684
-2.51629
3.82394
-11.1849
6.8151
-2.1849
-2.59833
-17.8477
20.8151
11.2316
5.47789
7.65103
-8.77705
1.31661
2.8151
5.64801
-10.3367
5.73888
-1.08799




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270443&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]6 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=270443&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270443&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-1.2117-1.0291-0.417057.2734e-070.331530.905281.50760.582660.74857
median-1.0874-0.55871-0.0599070.582631.34971.95372.61220.858191.4096
midrange-8.5149-8.4599-8.2047-6.0904-5.2263-0.29570.485552.42772.9784
mode-15.601-11.387-4.2605-1.17063.231610.74711.6486.06117.4921
mode k.dens-2.5541-1.8182-0.212672.12083.70545.03665.65482.26163.9181

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -1.2117 & -1.0291 & -0.41705 & 7.2734e-07 & 0.33153 & 0.90528 & 1.5076 & 0.58266 & 0.74857 \tabularnewline
median & -1.0874 & -0.55871 & -0.059907 & 0.58263 & 1.3497 & 1.9537 & 2.6122 & 0.85819 & 1.4096 \tabularnewline
midrange & -8.5149 & -8.4599 & -8.2047 & -6.0904 & -5.2263 & -0.2957 & 0.48555 & 2.4277 & 2.9784 \tabularnewline
mode & -15.601 & -11.387 & -4.2605 & -1.1706 & 3.2316 & 10.747 & 11.648 & 6.0611 & 7.4921 \tabularnewline
mode k.dens & -2.5541 & -1.8182 & -0.21267 & 2.1208 & 3.7054 & 5.0366 & 5.6548 & 2.2616 & 3.9181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270443&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]-1.2117[/C][C]-1.0291[/C][C]-0.41705[/C][C]7.2734e-07[/C][C]0.33153[/C][C]0.90528[/C][C]1.5076[/C][C]0.58266[/C][C]0.74857[/C][/ROW]
[ROW][C]median[/C][C]-1.0874[/C][C]-0.55871[/C][C]-0.059907[/C][C]0.58263[/C][C]1.3497[/C][C]1.9537[/C][C]2.6122[/C][C]0.85819[/C][C]1.4096[/C][/ROW]
[ROW][C]midrange[/C][C]-8.5149[/C][C]-8.4599[/C][C]-8.2047[/C][C]-6.0904[/C][C]-5.2263[/C][C]-0.2957[/C][C]0.48555[/C][C]2.4277[/C][C]2.9784[/C][/ROW]
[ROW][C]mode[/C][C]-15.601[/C][C]-11.387[/C][C]-4.2605[/C][C]-1.1706[/C][C]3.2316[/C][C]10.747[/C][C]11.648[/C][C]6.0611[/C][C]7.4921[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-2.5541[/C][C]-1.8182[/C][C]-0.21267[/C][C]2.1208[/C][C]3.7054[/C][C]5.0366[/C][C]5.6548[/C][C]2.2616[/C][C]3.9181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270443&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270443&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-1.2117-1.0291-0.417057.2734e-070.331530.905281.50760.582660.74857
median-1.0874-0.55871-0.0599070.582631.34971.95372.61220.858191.4096
midrange-8.5149-8.4599-8.2047-6.0904-5.2263-0.29570.485552.42772.9784
mode-15.601-11.387-4.2605-1.17063.231610.74711.6486.06117.4921
mode k.dens-2.5541-1.8182-0.212672.12083.70545.03665.65482.26163.9181



Parameters (Session):
par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
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')