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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 computationTue, 11 Nov 2014 10:33:31 +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/11/t14157021125rrvycloqv5gyho.htm/, Retrieved Sun, 19 May 2024 12:34:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253541, Retrieved Sun, 19 May 2024 12:34:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

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] [] [2014-11-11 10:33:31] [e4bec374a19c70fe4499af2adad38eb7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-2.11377
1.62659
-1.24809
-3.02809
9.48726
1.9426
8.40414
-1.58916
-2.28244
0.851045
-0.360797
-2.3704
-0.986595
1.53982
0.812351
0.082788
1.11946
1.50256
-3.14541
0.545672
-1.06176
-1.42175
-1.56273
-1.26661
1.19856
-6.73545
-0.333925
1.75566
2.03471
-2.62398
-2.28299
-0.382925
-1.01934
-0.624919
-0.0244947
-4.34509
2.1962
-0.436158
-0.825006
-3.87832
-3.10309
2.43302
-3.98882
-1.71861
-1.49985
-2.69772
-3.72161
-1.25563
4.2709
-2.56223
-1.60474
0.465273
1.96112
-0.969953
-4.77282
2.20375
1.71293
-3.71927
-4.38384
0.459231
1.10493
-1.28626
-2.62707
-0.415303
-0.260549
-4.25689
1.834
-2.28382
-0.0121782
1.56911
-0.788538
2.09814
1.42428
-2.12916
-1.97728
5.24104
2.14003
3.12924
0.56089
-5.5563
-0.93046
-3.31904
0.509069
-0.45635
0.228635
0.731929
-1.18701
0.145049
3.80745
1.65512
0.543997
0.988396
2.46021
-1.15405
-0.018439
1.54144
-1.676
0.749892
-2.36223
-0.458006
1.71229
1.64389
-4.5951
4.02201
2.17768
-0.126093
3.86451
-1.80596
6.46585
2.32036
-0.047381
-1.85524
-0.240378
1.77236
-0.463133
-0.21987
-0.592045
-4.48776
3.13808
-0.76342
1.21427
-0.494876
-3.92769
-1.19471
-2.31862
-1.80878
-0.38355
1.85838
0.382216
-2.86527
2.37688
-1.92453
1.74833
-0.270601
3.04365
6.41317
0.387799
-0.764125
-2.15154
-2.49602
-3.45325
2.78629
-0.173696
-0.0432014
-0.0344116
0.848073
-3.00692
-3.28598
2.21334
0.593313
3.87359
-2.72697
-2.56981
2.13703
3.9858
0.543997
0.333075
1.85838
-4.03888
3.70604
1.7475
7.33833
1.0124
9.2764
1.99121
5.93146
-0.980061
-0.942786
-0.279144
1.26126
3.50741
0.481168
-4.39316
1.7181
1.18809
-4.55051
-1.12299
2.88395
-2.15201
-1.33349
-2.94118
-5.52027
-1.1531
-2.48216
-0.678616
5.44613
1.25409
0.261203
-1.43738
4.55404
-4.27998
-2.72896
1.90415
0.305804
1.3531
-2.53925
6.34699
2.79858
0.902978
-0.667428
0.185587
-0.975019
-0.188145
-1.04664
-1.50271
0.707313
-0.273833
2.33671
-0.234915
0.972595
-0.339769
-0.0886144
3.50203
-2.75269
1.62594
-0.0318217
0.897482
-0.437463
3.81627
2.22594
-3.12392
-0.459963
1.06037
-3.79536
2.19481
-2.17932
0.247934
-3.20275
4.89235
-2.67488
-1.43997
-1.47114
-4.28241
3.771
-2.86969
-1.64354
1.71396
-2.08022
-4.97526
-3.66011
-4.19274
0.326123
-0.119846
0.869543
2.26227
4.07807
0.748261
7.91382
2.40876
-0.236992
0.936757
2.95233
-3.20266
-0.795812
2.06391
0.113795
4.63661
0.529048
2.25051
-8.46241
-1.75167
-0.972249
3.08095
-1.3944

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'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253541&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253541&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253541&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.38292-0.27679-0.11971-1.1364e-080.0777680.240970.326540.159390.19748
median-0.45636-0.38292-0.24038-0.14989-0.0344120.145280.254630.152170.20597
midrange-0.56204-0.0291350.512420.512421.29681.96552.15160.640730.78441
mode-4.5969-3.9332-1.25561.20121.53292.79865.4512.13632.7885
mode k.dens-0.92174-0.76773-0.398-0.183420.0509830.8411.72870.496710.44898

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.38292 & -0.27679 & -0.11971 & -1.1364e-08 & 0.077768 & 0.24097 & 0.32654 & 0.15939 & 0.19748 \tabularnewline
median & -0.45636 & -0.38292 & -0.24038 & -0.14989 & -0.034412 & 0.14528 & 0.25463 & 0.15217 & 0.20597 \tabularnewline
midrange & -0.56204 & -0.029135 & 0.51242 & 0.51242 & 1.2968 & 1.9655 & 2.1516 & 0.64073 & 0.78441 \tabularnewline
mode & -4.5969 & -3.9332 & -1.2556 & 1.2012 & 1.5329 & 2.7986 & 5.451 & 2.1363 & 2.7885 \tabularnewline
mode k.dens & -0.92174 & -0.76773 & -0.398 & -0.18342 & 0.050983 & 0.841 & 1.7287 & 0.49671 & 0.44898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253541&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]-0.38292[/C][C]-0.27679[/C][C]-0.11971[/C][C]-1.1364e-08[/C][C]0.077768[/C][C]0.24097[/C][C]0.32654[/C][C]0.15939[/C][C]0.19748[/C][/ROW]
[ROW][C]median[/C][C]-0.45636[/C][C]-0.38292[/C][C]-0.24038[/C][C]-0.14989[/C][C]-0.034412[/C][C]0.14528[/C][C]0.25463[/C][C]0.15217[/C][C]0.20597[/C][/ROW]
[ROW][C]midrange[/C][C]-0.56204[/C][C]-0.029135[/C][C]0.51242[/C][C]0.51242[/C][C]1.2968[/C][C]1.9655[/C][C]2.1516[/C][C]0.64073[/C][C]0.78441[/C][/ROW]
[ROW][C]mode[/C][C]-4.5969[/C][C]-3.9332[/C][C]-1.2556[/C][C]1.2012[/C][C]1.5329[/C][C]2.7986[/C][C]5.451[/C][C]2.1363[/C][C]2.7885[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.92174[/C][C]-0.76773[/C][C]-0.398[/C][C]-0.18342[/C][C]0.050983[/C][C]0.841[/C][C]1.7287[/C][C]0.49671[/C][C]0.44898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253541&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253541&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-0.38292-0.27679-0.11971-1.1364e-080.0777680.240970.326540.159390.19748
median-0.45636-0.38292-0.24038-0.14989-0.0344120.145280.254630.152170.20597
midrange-0.56204-0.0291350.512420.512421.29681.96552.15160.640730.78441
mode-4.5969-3.9332-1.25561.20121.53292.79865.4512.13632.7885
mode k.dens-0.92174-0.76773-0.398-0.183420.0509830.8411.72870.496710.44898


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 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')