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Description of Statistical Computation

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 computationSun, 18 Jan 2015 21:28:02 +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/2015/Jan/18/t14216165354u670yz8jawfvjj.htm/, Retrieved Mon, 13 May 2024 23:19:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=274317, Retrieved Mon, 13 May 2024 23:19:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2015-01-18 21:28:02] [3a47cc92becfffb332a48f98591f891c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.294153
2.97156
-2.71955
-2.10608
5.15598
3.88145
3.48012
-0.793748
0.0474647
0.949459
1.70415
3.53959
-3.16424
2.70588
2.45478
0.885167
0.432262
1.36526
-1.255
2.42932
2.88737
-2.42299
-0.36997
-1.32201
1.79733
-6.83795
1.13673
0.968342
1.29799
-2.62899
0.532419
0.742768
2.12812
-0.0241935
0.256096
0.854528
-1.36766
0.889595
1.90098
-2.04375
-0.553176
2.57853
0.103225
-0.921825
0.567044
-2.36129
-0.183641
0.333117
3.66465
-1.59683
0.909003
0.806532
-0.386175
-1.39188
-1.72474
1.63465
1.91825
-0.283804
-3.07451
-1.19565
-2.36197
-1.51284
-3.52005
1.12206
1.48021
-5.05159
-1.57071
-2.46861
1.56108
1.43552
0.719528
3.42149
0.566379
-0.363746
-1.9214
-0.056789
3.01548
0.632589
1.37487
-2.02638
0.115316
-0.491874
1.71652
0.792612
-0.0500028
1.06084
-0.257791
0.293489
-3.39489
3.46282
0.104144
0.834079
0.760705
-0.886211
0.991821
-0.812128
-0.861605
2.03306
0.0326958
1.75233
-0.919433
0.872158
-3.38395
1.95822
-2.34112
1.01075
2.0363
-2.8444
0.975645
1.16927
-2.19003
-2.24943
1.93964
3.96052
0.344235
1.01153
0.283773
-1.06596
0.296354
-0.483775
0.441724
0.121994
-1.00658
0.382647
-1.80252
0.786913
1.54651
4.15924
1.47515
-1.67088
-1.41748
-0.355202
2.53319
0.754977
2.30513
1.73636
0.713535
-0.826256
0.873435
-0.698291
0.318378
2.25138
-0.706501
0.692002
1.48385
1.44925
-2.39905
-2.72706
-2.40806
1.89067
0.355387
0.489124
-2.45246
-2.4997
1.47025
0.104144
0.770858
4.15924
-2.73299
0.0487413
0.520571
0.817289
0.750211
4.29504
-2.03717
2.04186
-0.19388
-0.864039
-3.7685
-3.03779
0.446489
1.59975
-5.15257
1.75479
2.51998
-2.48453
-3.35533
0.512109
1.30535
-2.29359
-0.352145
-1.80944
0.0351127
-1.20099
2.02659
1.20875
0.216629
0.686974
0.498628
0.817199
-1.94897
-1.28106
2.27819
-1.65853
1.7838
-2.26586
2.1398
0.426097
-3.24974
-0.945592
-3.29269
1.12158
2.78591
0.297647
0.379211
1.07392
-0.692765
3.21317
-0.0115483
1.49078
-2.81386
1.25588
-1.34621
-3.96611
-1.36908
1.58447
1.86706
-0.45464
-2.05585
1.21455
-3.09837
2.11414
-2.31201
0.0218416
-0.877228
1.63328
4.97042
-1.91696
-1.51227
-2.50048
0.0158494
-3.08591
-0.165203
0.240808
0.874019
-1.99099
0.755342
-0.0764699
-4.61544
-2.70354
-2.92081
-2.85065
0.241454
-0.409056
1.2673
0.219823
0.100763
5.08859
-0.195174
0.358255
2.02625
1.07606
-1.31709
-0.952895
0.0362316
-0.868792
-2.06858
-2.59497
2.19119
-4.78671
0.0887831
1.2742
-2.92786
-0.0466467

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=274317&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=274317&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274317&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ yule.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.29961-0.22419-0.0891264.5455e-080.0940080.206070.24630.131130.18313
median0.00161190.0415670.118660.248780.307530.404370.44460.113290.18887
midrange-1.2721-0.93377-0.84099-0.84099-0.0235660.0521950.185490.442230.81742
mode-4.7904-2.3682-0.565492.13170.954183.45844.16741.74881.5197
mode k.dens0.222580.287130.416580.551650.66820.872211.26540.200830.25161

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.29961 & -0.22419 & -0.089126 & 4.5455e-08 & 0.094008 & 0.20607 & 0.2463 & 0.13113 & 0.18313 \tabularnewline
median & 0.0016119 & 0.041567 & 0.11866 & 0.24878 & 0.30753 & 0.40437 & 0.4446 & 0.11329 & 0.18887 \tabularnewline
midrange & -1.2721 & -0.93377 & -0.84099 & -0.84099 & -0.023566 & 0.052195 & 0.18549 & 0.44223 & 0.81742 \tabularnewline
mode & -4.7904 & -2.3682 & -0.56549 & 2.1317 & 0.95418 & 3.4584 & 4.1674 & 1.7488 & 1.5197 \tabularnewline
mode k.dens & 0.22258 & 0.28713 & 0.41658 & 0.55165 & 0.6682 & 0.87221 & 1.2654 & 0.20083 & 0.25161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=274317&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]-0.29961[/C][C]-0.22419[/C][C]-0.089126[/C][C]4.5455e-08[/C][C]0.094008[/C][C]0.20607[/C][C]0.2463[/C][C]0.13113[/C][C]0.18313[/C][/ROW]
[ROW][C]median[/C][C]0.0016119[/C][C]0.041567[/C][C]0.11866[/C][C]0.24878[/C][C]0.30753[/C][C]0.40437[/C][C]0.4446[/C][C]0.11329[/C][C]0.18887[/C][/ROW]
[ROW][C]midrange[/C][C]-1.2721[/C][C]-0.93377[/C][C]-0.84099[/C][C]-0.84099[/C][C]-0.023566[/C][C]0.052195[/C][C]0.18549[/C][C]0.44223[/C][C]0.81742[/C][/ROW]
[ROW][C]mode[/C][C]-4.7904[/C][C]-2.3682[/C][C]-0.56549[/C][C]2.1317[/C][C]0.95418[/C][C]3.4584[/C][C]4.1674[/C][C]1.7488[/C][C]1.5197[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.22258[/C][C]0.28713[/C][C]0.41658[/C][C]0.55165[/C][C]0.6682[/C][C]0.87221[/C][C]1.2654[/C][C]0.20083[/C][C]0.25161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=274317&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274317&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.29961-0.22419-0.0891264.5455e-080.0940080.206070.24630.131130.18313
median0.00161190.0415670.118660.248780.307530.404370.44460.113290.18887
midrange-1.2721-0.93377-0.84099-0.84099-0.0235660.0521950.185490.442230.81742
mode-4.7904-2.3682-0.565492.13170.954183.45844.16741.74881.5197
mode k.dens0.222580.287130.416580.551650.66820.872211.26540.200830.25161


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Figure 11
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
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')