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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 computationWed, 17 Dec 2014 10:01:48 +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/t1418810558g9dg8472xb2zglz.htm/, Retrieved Thu, 16 May 2024 10:31:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270016, Retrieved Thu, 16 May 2024 10:31:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-16 10:01:39] [3d5212c89039da1a3a24d8e18d23c716]
- RMPD    [Bootstrap Plot - Central Tendency] [] [2014-12-17 10:01:48] [cffb402fbd03ba50ead9426cec86a7a5] [Current]
- RM        [Bootstrap Plot - Central Tendency] [] [2014-12-17 12:12:46] [a86b94943a9e798c2d09bb837c6a8141]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,943151
1,93488
1,59365
-3,74507
-3,54577
0,74195
3,6163
1,52934
-0,74687
-3,1207
0,701791
-4,73673
1,46245
-1,0326
-1,71871
1,38426
-0,0217053
-0,962395
-2,13819
0,654446
-2,79326
1,976
-0,919187
2,05947
0,714447
-1,45962
1,38903
2,47688
-0,842418
1,25478
-1,22665
0,886873
-0,498445
-3,00844
0,928978
0,470671
0,289022
2,07041
-2,61005
-0,0394285
-4,1215
0,170541
1,5853
-0,029606
2,53209
-0,858974
1,83879
-3,02296
3,31235
2,32646
1,04938
-2,33603
1,24398
1,83566
2,69108
0,26036
-4,86749
4,61513
-0,763781
2,21903
-2,69378
1,9624
-1,52117
3,52463
-1,80004
-0,661775
-1,19354
-0,0449652
3,15832
0,988177
-0,77248
1,15138
-1,72964
2,408
1,85634
1,56628
1,47572
1,79037
-0,380415
-2,44049
-0,589581
3,12272
0,00613861
-5,01623
-1,21834
0,120117
0,922991
1,29604
-2,04363
2,37932
3,05907
-4,48424
-0,78483
-1,32035
-1,9986
-0,954419
-3,3465
1,88184
3,89579
-2,91334
1,14976
0,248063
-1,1974
-1,14895
3,83839
-2,1744
3,24349
-5,00046
-2,78981
-1,1591
1,18015
1,56062
-5,43794
2,17748
2,27391
1,99363
-1,73981
0,592852
-0,826756
1,57612
-3,02344
1,84635
1,11307
5,56521
-2,76439
-0,611745
0,507194
-0,516471
1,62592
0,449954
0,832164
-1,33267
1,00375
1,07947
0,32981
0,384627
3,20101
-0,998316
1,14183
0,772498
-0,677209
2,50498
-0,246527
2,22841
-0,681449
-0,206509
-0,328454
-0,401173
-1,58538
2,03523
-6,0682
0,700179
2,00912
-0,436312
2,301
-0,206327
1,03738
0,970084
-2,01016
-1,9257
1,44366
2,63928
-0,786074
6,51022
0,691773
1,25873
0,227441
1,10766
-0,41337
-2,36348
0,911036
2,26237
-2,85661
-0,885784
-0,855254
2,3495
-1,60338
2,11487
-1,97413
-2,38963
-1,58713
-2,09133
2,00306
-0,300121
-3,73535
0,0611127
2,60331
-2,082
-1,74623
1,66569
-1,16477
0,863274
-2,848
3,18724
-0,169924
1,26748
-2,63909
0,999662
2,24099
0,817941
1,79461
0,519313
-2,58109
0,253274
-1,51395
1,42435
-0,139311
0,987106
1,3655
1,53393
0,0410347
-0,500312
-2,69804
0,39202
-1,18175
-0,885771
0,765205
1,86767
-0,444265
-0,669942
2,12911
-2,7079
-1,33812
0,833997
2,57231
4,12007
1,4694
2,92558
3,07882
2,5567
3,33401
0,826113
0,460824
-2,75847
-1,85633
-5,2214
1,08458
-1,92419
-0,741007
0,165304
-1,66287
-1,47826
0,33342
1,01262
-1,73816
-0,644194
-0,214059
0,643578
-0,654921
-1,88941
-3,12884
-1,02335
1,19346
1,39985
-2,91341
0,564083
-4,23401
-4,00464
-2,83711
-7,29115
0,274851
-1,3576
-2,36969
0,441311
1,34874
0,20435
0,213599
1,35418
1,58484
0,024722
1,16102
-0,238338
1,66962
1,23533
-0,159636
0,00736388
1,06962
-2,19884

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270016&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]10 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=270016&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270016&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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.30949-0.22531-0.094467-6.8741e-080.0815120.203680.268060.129780.17598
median-0.18851-0.0467420.137060.237750.309420.507190.619050.176570.17235
midrange-1.5869-1.338-0.86297-0.39046-0.251490.536140.645440.539530.61148
mode-4.869-3.3644-1.1855-6.8741e-081.01172.4683.00731.75522.1973
mode k.dens-0.3976-0.104080.892461.04551.21211.35341.52430.389640.31966

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.30949 & -0.22531 & -0.094467 & -6.8741e-08 & 0.081512 & 0.20368 & 0.26806 & 0.12978 & 0.17598 \tabularnewline
median & -0.18851 & -0.046742 & 0.13706 & 0.23775 & 0.30942 & 0.50719 & 0.61905 & 0.17657 & 0.17235 \tabularnewline
midrange & -1.5869 & -1.338 & -0.86297 & -0.39046 & -0.25149 & 0.53614 & 0.64544 & 0.53953 & 0.61148 \tabularnewline
mode & -4.869 & -3.3644 & -1.1855 & -6.8741e-08 & 1.0117 & 2.468 & 3.0073 & 1.7552 & 2.1973 \tabularnewline
mode k.dens & -0.3976 & -0.10408 & 0.89246 & 1.0455 & 1.2121 & 1.3534 & 1.5243 & 0.38964 & 0.31966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270016&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]-0.30949[/C][C]-0.22531[/C][C]-0.094467[/C][C]-6.8741e-08[/C][C]0.081512[/C][C]0.20368[/C][C]0.26806[/C][C]0.12978[/C][C]0.17598[/C][/ROW]
[ROW][C]median[/C][C]-0.18851[/C][C]-0.046742[/C][C]0.13706[/C][C]0.23775[/C][C]0.30942[/C][C]0.50719[/C][C]0.61905[/C][C]0.17657[/C][C]0.17235[/C][/ROW]
[ROW][C]midrange[/C][C]-1.5869[/C][C]-1.338[/C][C]-0.86297[/C][C]-0.39046[/C][C]-0.25149[/C][C]0.53614[/C][C]0.64544[/C][C]0.53953[/C][C]0.61148[/C][/ROW]
[ROW][C]mode[/C][C]-4.869[/C][C]-3.3644[/C][C]-1.1855[/C][C]-6.8741e-08[/C][C]1.0117[/C][C]2.468[/C][C]3.0073[/C][C]1.7552[/C][C]2.1973[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.3976[/C][C]-0.10408[/C][C]0.89246[/C][C]1.0455[/C][C]1.2121[/C][C]1.3534[/C][C]1.5243[/C][C]0.38964[/C][C]0.31966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270016&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270016&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-0.30949-0.22531-0.094467-6.8741e-080.0815120.203680.268060.129780.17598
median-0.18851-0.0467420.137060.237750.309420.507190.619050.176570.17235
midrange-1.5869-1.338-0.86297-0.39046-0.251490.536140.645440.539530.61148
mode-4.869-3.3644-1.1855-6.8741e-081.01172.4683.00731.75522.1973
mode k.dens-0.3976-0.104080.892461.04551.21211.35341.52430.389640.31966


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 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')