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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 computationTue, 16 Dec 2014 18:51:53 +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/16/t1418755966vnbsuut2d0l161q.htm/, Retrieved Sat, 18 May 2024 23:31:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269890, Retrieved Sat, 18 May 2024 23:31:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact50
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Residual Diagnostics] [2014-12-16 18:51:53] [e89b1602ca7c278e2fffead05eac818b] [Current]
- RMPD    [One Sample Tests about the Mean] [tttest] [2014-12-18 19:59:59] [189b7d469e4e3b4e868a6af83e3b3816]
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Dataseries X:
-0.117112
1.42219
1.41826
-4.26669
-3.45651
-1.01968
2.99702
2.45764
-0.735457
-3.24215
-0.401658
-4.90171
1.30001
-1.52066
-0.210336
1.7986
-0.563581
-0.79896
-1.73062
-0.0154464
-3.48878
3.53051
-2.91147
0.209076
0.881589
-4.03856
2.95909
3.02311
-1.71813
0.370962
-0.973537
0.340109
-1.08066
-2.15503
1.53979
1.79432
-0.38513
2.52978
-2.56002
-0.281439
-3.74542
0.427441
1.86578
-0.935718
4.01203
-1.6454
2.10921
-1.75461
2.77117
3.49996
1.05093
-0.300759
0.461355
0.888949
3.48808
0.341279
-4.82992
3.71123
0.563889
2.80697
-3.41284
1.2652
-1.9808
2.30431
-3.13338
0.328507
-2.18163
-2.15018
2.78674
1.49125
-0.737938
1.18271
-1.13394
3.32714
1.54374
0.846316
1.96537
0.982985
1.1136
-2.08689
0.0947617
3.06499
0.530785
-5.01436
-0.751069
0.746987
2.40806
2.65651
-1.58744
1.45677
4.06676
-4.64341
-2.06834
-1.1817
-1.81985
-1.42738
-3.72716
1.83813
3.48323
-2.47086
2.13464
0.239421
-1.03858
-1.2754
4.41505
-1.34023
3.58063
-4.80618
-1.64768
-1.21163
0.948249
1.24085
-6.06545
0.608112
2.96587
2.50765
-1.43168
0.343694
-1.32305
1.77978
-2.83891
1.18649
1.67867
4.27095
-2.47959
-0.559518
1.79034
-0.170973
2.50702
1.12102
1.06854
-1.39211
1.34529
1.45505
-0.0569779
-0.0311979
3.47482
-1.62899
1.63743
1.0443
-1.77074
2.76892
0.0654548
3.25797
0.235691
0.779544
-0.110062
0.115157
0.000739324
1.99449
-5.70237
1.28367
2.69191
-0.0874409
1.86255
-0.451696
1.85533
1.11964
-2.22803
-2.18038
1.29885
2.28864
-0.270706
7.12178
0.709926
-0.956311
1.69248
1.64574
0.0900214
-3.38916
1.33145
2.44362
-2.0174
-0.186914
-0.161283
2.42585
-1.44092
2.9838
-1.2006
-4.17688
-2.84046
-1.665
2.83177
0.0981288
-2.62835
-0.0453575
2.4089
-2.19636
-2.24321
0.293866
-2.23097
0.895463
-3.23241
3.4435
-0.765453
0.630628
-1.47593
0.191352
1.536
1.10409
0.0319253
0.95009
-1.25958
0.359617
-1.02763
1.391
-1.0935
0.243823
2.8213
1.26103
-1.07909
0.784597
-2.18967
-1.00732
-0.84363
-0.953178
1.46597
2.36401
-1.479
-0.725621
2.83932
-4.55448
-0.747245
2.277
2.69435
2.22507
2.46793
1.97296
2.98206
3.27481
3.78836
2.28695
-0.288486
-2.48129
-2.19898
-6.37563
-0.366331
-1.26169
-0.439939
-0.592813
-1.44081
-1.88712
0.757348
0.804209
-2.98889
-0.489011
0.49344
-0.018943
0.490236
-0.375707
-2.37215
-1.9242
0.0412398
0.364841
-2.87109
0.81886
-4.64155
-5.41164
-3.07057
-6.59166
-1.15724
-2.23098
-3.75669
0.799688
1.81202
0.265224
-0.068996
1.44638
1.84512
-1.35288
1.3471
0.291247
0.956701
1.58173
0.518184
0.134498
0.668362
-2.95243




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269890&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269890&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269890&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'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.32048-0.21115-0.0871141.7095e-070.109210.242170.280430.138310.19632
median-0.17137-0.0784840.0654550.162920.278240.370960.518480.152990.21278
midrange-1.2627-1.1604-1.05230.265060.265060.537240.857060.697831.3174
mode-4.645-2.6265-1.09021.7095e-070.961322.98284.27241.81922.0515
mode k.dens-0.63575-0.242080.200940.517210.719421.13841.33280.446620.51848

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.32048 & -0.21115 & -0.087114 & 1.7095e-07 & 0.10921 & 0.24217 & 0.28043 & 0.13831 & 0.19632 \tabularnewline
median & -0.17137 & -0.078484 & 0.065455 & 0.16292 & 0.27824 & 0.37096 & 0.51848 & 0.15299 & 0.21278 \tabularnewline
midrange & -1.2627 & -1.1604 & -1.0523 & 0.26506 & 0.26506 & 0.53724 & 0.85706 & 0.69783 & 1.3174 \tabularnewline
mode & -4.645 & -2.6265 & -1.0902 & 1.7095e-07 & 0.96132 & 2.9828 & 4.2724 & 1.8192 & 2.0515 \tabularnewline
mode k.dens & -0.63575 & -0.24208 & 0.20094 & 0.51721 & 0.71942 & 1.1384 & 1.3328 & 0.44662 & 0.51848 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269890&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]-0.32048[/C][C]-0.21115[/C][C]-0.087114[/C][C]1.7095e-07[/C][C]0.10921[/C][C]0.24217[/C][C]0.28043[/C][C]0.13831[/C][C]0.19632[/C][/ROW]
[ROW][C]median[/C][C]-0.17137[/C][C]-0.078484[/C][C]0.065455[/C][C]0.16292[/C][C]0.27824[/C][C]0.37096[/C][C]0.51848[/C][C]0.15299[/C][C]0.21278[/C][/ROW]
[ROW][C]midrange[/C][C]-1.2627[/C][C]-1.1604[/C][C]-1.0523[/C][C]0.26506[/C][C]0.26506[/C][C]0.53724[/C][C]0.85706[/C][C]0.69783[/C][C]1.3174[/C][/ROW]
[ROW][C]mode[/C][C]-4.645[/C][C]-2.6265[/C][C]-1.0902[/C][C]1.7095e-07[/C][C]0.96132[/C][C]2.9828[/C][C]4.2724[/C][C]1.8192[/C][C]2.0515[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.63575[/C][C]-0.24208[/C][C]0.20094[/C][C]0.51721[/C][C]0.71942[/C][C]1.1384[/C][C]1.3328[/C][C]0.44662[/C][C]0.51848[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269890&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269890&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-0.32048-0.21115-0.0871141.7095e-070.109210.242170.280430.138310.19632
median-0.17137-0.0784840.0654550.162920.278240.370960.518480.152990.21278
midrange-1.2627-1.1604-1.05230.265060.265060.537240.857060.697831.3174
mode-4.645-2.6265-1.09021.7095e-070.961322.98284.27241.81922.0515
mode k.dens-0.63575-0.242080.200940.517210.719421.13841.33280.446620.51848



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')