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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, 12 Nov 2014 18:13:55 +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/Nov/12/t1415816055u6rndzdygxue3th.htm/, Retrieved Sun, 19 May 2024 14:38:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=254009, Retrieved Sun, 19 May 2024 14:38:42 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

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] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
- R  D    [Bootstrap Plot - Central Tendency] [mean] [2014-11-12 18:13:55] [6c0da333d5967ad7c023c95d07a1face] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-1,02234
1,25661
-1,10746
-2,99274
9,35725
1,76395
9,50123
-2,05082
-2,35807
0.670491
-0.590127
-1,07094
-0.935465
2,69592
0.222574
0.802387
0.907219
0.852856
-2,65139
1,44736
-0.546293
-1,45242
-0.483651
-0.480391
0.617687
-7,43737
-0.45902
1,90351
1,71548
-2,59544
-0,02691
0.385116
-0.829568
-1,25285
-0.201747
-0,43188
2,22406
-0.352467
-0.975977
-3,81211
-3,17968
2,33373
-2,79809
-0.780622
-1,32217
-2,69782
-0,32488
-1,42033
4,33925
-0,28078
-1.5
0.643049
2,19171
-0.536747
-4,57151
1,97642
1,74186
-3,45881
-4,57027
0.413144
0,16305
-1,61684
-3,26555
0.620897
0.763961
-4,93667
2,01676
-2,55031
0.559968
1,79002
-0.579125
3,15637
1,00757
-1,82654
-2,76871
0,46594
2,45438
3,18535
0.211875
-0,46401
-1,29292
-3,20998
0.56063
-0.463203
-0.120312
0.612277
-1,05926
1,00292
3,44167
1,66789
1,50549
0.778814
0,18164
-1,78633
-0.333205
1,60028
-0,20693
0.428054
-2,60182
-0.465218
1,53082
1,10216
-4,53416
3,66394
2,09189
-0.205533
4,04165
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0,76925
1,58407
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1,57109
0,17374
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0.762091
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0.058871
0.90934
-1,38182
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1,59237
0.373362
-2,94073
2,24784
-2,13454
2,03879
-0.323605
2,35748
7,04514
0.847798
-0.0673811
-1,73363
-2,51536
-2,46911
2,14513
-0.988204
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-0.935489
0.692663
-2,54692
-3,63761
1,77334
-0.0783773
3,48269
-0,25879
-2,73978
1,95991
3,44858
1,50549
0.386234
1,59237
-4,81745
2,81072
0,17723
7,25385
0.345357
8,86953
0,17141
6,52165
-1,41637
-1,36945
-1,18215
0.815396
4,16857
0.604835
-5,04139
2,16089
1,23691
-5,00989
-1,27673
2,78212
-1,85874
-1,66645
-1,93625
-5,43405
-0.979624
-2,93758
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5,12032
1,13291
-0.588463
-0.693818
4,84191
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1,96691
0.667527
0,13111
-2,54114
5,88783
1,93234
1,01114
-0,11257
-0.036374
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0.616938
-1,01665
3,56476
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0.362555
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2,43502
-3,50724
1,70786
0.257511
0.317318
-1,06805
4,00114
1,72901
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0.427354
0.760296
-3,86478
3,71926
-0,25585
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0.96417
0.297038
0.982176
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0.822369
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2,75714
0.424294
0.96187
2,41105
-0,33041
-0.929784
2,10855
0.211235
4,15837
0.665408
2,55459
-9,54205
-0,14383
-0.747074
3,16659
-0,15259

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=254009&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=254009&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254009&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-0.37371-0.22889-0.0555510.037520.180380.328710.398840.171290.23593
median-0.33192-0.28427-0.17586-0.0728790.167230.260310.373430.185350.34309
midrange-0.34434-0.0924-0.02041-0.020410.971852.03362.05760.769090.99226
mode-4.8186-3.0021-0.917231.54891.25233.92476.0552.11782.1695
mode k.dens-0.53322-0.36735-0.19981-0.00393350.176530.433090.630320.263680.37634

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.37371 & -0.22889 & -0.055551 & 0.03752 & 0.18038 & 0.32871 & 0.39884 & 0.17129 & 0.23593 \tabularnewline
median & -0.33192 & -0.28427 & -0.17586 & -0.072879 & 0.16723 & 0.26031 & 0.37343 & 0.18535 & 0.34309 \tabularnewline
midrange & -0.34434 & -0.0924 & -0.02041 & -0.02041 & 0.97185 & 2.0336 & 2.0576 & 0.76909 & 0.99226 \tabularnewline
mode & -4.8186 & -3.0021 & -0.91723 & 1.5489 & 1.2523 & 3.9247 & 6.055 & 2.1178 & 2.1695 \tabularnewline
mode k.dens & -0.53322 & -0.36735 & -0.19981 & -0.0039335 & 0.17653 & 0.43309 & 0.63032 & 0.26368 & 0.37634 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254009&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]-0.37371[/C][C]-0.22889[/C][C]-0.055551[/C][C]0.03752[/C][C]0.18038[/C][C]0.32871[/C][C]0.39884[/C][C]0.17129[/C][C]0.23593[/C][/ROW]
[ROW][C]median[/C][C]-0.33192[/C][C]-0.28427[/C][C]-0.17586[/C][C]-0.072879[/C][C]0.16723[/C][C]0.26031[/C][C]0.37343[/C][C]0.18535[/C][C]0.34309[/C][/ROW]
[ROW][C]midrange[/C][C]-0.34434[/C][C]-0.0924[/C][C]-0.02041[/C][C]-0.02041[/C][C]0.97185[/C][C]2.0336[/C][C]2.0576[/C][C]0.76909[/C][C]0.99226[/C][/ROW]
[ROW][C]mode[/C][C]-4.8186[/C][C]-3.0021[/C][C]-0.91723[/C][C]1.5489[/C][C]1.2523[/C][C]3.9247[/C][C]6.055[/C][C]2.1178[/C][C]2.1695[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.53322[/C][C]-0.36735[/C][C]-0.19981[/C][C]-0.0039335[/C][C]0.17653[/C][C]0.43309[/C][C]0.63032[/C][C]0.26368[/C][C]0.37634[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254009&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254009&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-0.37371-0.22889-0.0555510.037520.180380.328710.398840.171290.23593
median-0.33192-0.28427-0.17586-0.0728790.167230.260310.373430.185350.34309
midrange-0.34434-0.0924-0.02041-0.020410.971852.03362.05760.769090.99226
mode-4.8186-3.0021-0.917231.54891.25233.92476.0552.11782.1695
mode k.dens-0.53322-0.36735-0.19981-0.00393350.176530.433090.630320.263680.37634


Figures (Output of Computation)

Figure 1
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Figure 2
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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')