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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 computationThu, 18 Dec 2014 14:39:17 +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/18/t1418913599otjtfct5bw5xr0b.htm/, Retrieved Fri, 17 May 2024 11:00:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271008, Retrieved Fri, 17 May 2024 11:00:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81

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] [bootstrap plot re...] [2014-12-18 14:39:17] [e719845b3f21bd57beb5defeff8ed9b5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,23507
0,265899
0,685995
-4,51963
-3,66513
-0,873463
2,71259
0,25908
-0,69719
-3,43973
-1,00121
-4,29834
0,468763
-0,945874
1,30822
-0,867992
-0,0189584
-1,24503
-3,16102
-1,57429
-4,69989
0,609592
-0,913137
1,55224
0,194162
-2,61533
-0,367329
0,623841
-1,1467
-0,0742932
-2,59339
-0,234101
-0,87143
-3,73877
-1,39391
-0,164323
-0,994389
-0,0804383
-3,25867
-1,58861
-5,54757
-0,572814
0,552778
-0,0668002
-0,162364
-1,74025
-0,367992
-5,34741
2,96674
0,185995
-1,48211
-4,54825
-0,494389
2,10185
1,45378
-0,379597
-5,01281
3,83487
0,222917
2,51378
-1,00465
0,0261874
-4,58246
2,35808
-1,00465
-1,40619
-2,45337
0,47207
1,17985
0,640175
-1,47996
0,326705
-0,818791
0,671683
1,19349
0,552778
1,27407
1,29249
-2,84876
-4,2341
-2,00121
1,97902
-1,4213
-3,81238
-3,06201
-0,367318
-0,413007
-2,1152
-3,18079
2,56709
1,69214
-4,85301
-0,327089
-2,1467
-4,60905
-1,2731
-1,95309
0,0323325
2,61224
-5,6152
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1,83764
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0,651106
-4,39991
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-0,127606
0,539826
-3,68875
1,62864
1,2146
2,11444
1,51364
1,61237
0,214597
2,2146
-0,785403
3,51221
0,573213
4,83225
-2,18667
-0,186672
0,935135
-1,48636
1,07321
1,31333
0,0927902
-0,52694
1,49264
-0,548594
1,93306
-0,428863
3,41348
-2,88829
0,573213
1,01364
0,612367
0,393905
-2,10609
0,97306
0,614443
0,97306
-1,48636
0,413482
-2,60609
0,893905
-5,62709
1,59279
0,893905
1,11444
3,61237
1,71252
1,33168
2,81333
0,913482
0,291675
2,63071
1,67129
0,214597
4,61171
2,07321
2,2321
0,831676
2,31333
0,59279
-0,787479
0,130715
3,01364
0,0142898
1,07652
1,07652
2,51364
-0,586518
2,2146
-0,385557
-1,58859
-0,828709
0,171945
2,2146
-0,307056
-4,92679
1,01364
3,41348
-1,7279
-0,428209
1,11171
2,31125
1,67194
-1,38763
1,23087
2,61237
2,71252
-2,60609
0,812674
2,97098
0,692944
1,89391
2,61237
-0,98571
0,772098
-2,3064
3,61237
-0,488441
4,87348
0,431829
1,51364
3,63194
0,193598
-1,82806
1,53321
0,614443
-1,32806
1,91348
1,47306
2,16987
0,772098
0,393905
-1,33013
0,713175
-1,2854
3,35598
1,82895
0,214597
4,61237
2,37225
0,752521
0,252521
2,2146
2,11237
-2,88556
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1,51156
-3,2854
0,0332126
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-2,82871
1,17194
2,07321
1,83291
1,81333
0,714597
-2,44844
-0,785403
-2,38556
-3,88556
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3,29168
2,07114
-2,06486
2,91348
-1,98779
-3,96679
-0,186672
-6,08652
2,2321
2,51221
-0,566941
1,17194
1,7721
1,47098
3,43306
-1,26913
2,91348
2,2321
3,71317
0,533213
1,81267
2,19294
0,530561
1,81333
1,7721
2,41471

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'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=271008&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=271008&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271008&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'Gwilym Jenkins' @ jenkins.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.31654-0.23361-0.0949021.982e-070.0752560.1950.258930.130470.17016
median-0.0710550.0288090.193520.25580.39390.533210.583090.149450.20039
midrange-0.7374-0.73707-0.62714-0.60652-0.37681-0.36917-0.336040.13960.25033
mode-2.6197-1.70790.198812.21462.21462.61243.61241.43752.0158
mode k.dens-0.200650.00951210.366570.579150.776441.19261.5420.36620.40986

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.31654 & -0.23361 & -0.094902 & 1.982e-07 & 0.075256 & 0.195 & 0.25893 & 0.13047 & 0.17016 \tabularnewline
median & -0.071055 & 0.028809 & 0.19352 & 0.2558 & 0.3939 & 0.53321 & 0.58309 & 0.14945 & 0.20039 \tabularnewline
midrange & -0.7374 & -0.73707 & -0.62714 & -0.60652 & -0.37681 & -0.36917 & -0.33604 & 0.1396 & 0.25033 \tabularnewline
mode & -2.6197 & -1.7079 & 0.19881 & 2.2146 & 2.2146 & 2.6124 & 3.6124 & 1.4375 & 2.0158 \tabularnewline
mode k.dens & -0.20065 & 0.0095121 & 0.36657 & 0.57915 & 0.77644 & 1.1926 & 1.542 & 0.3662 & 0.40986 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271008&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]-0.31654[/C][C]-0.23361[/C][C]-0.094902[/C][C]1.982e-07[/C][C]0.075256[/C][C]0.195[/C][C]0.25893[/C][C]0.13047[/C][C]0.17016[/C][/ROW]
[ROW][C]median[/C][C]-0.071055[/C][C]0.028809[/C][C]0.19352[/C][C]0.2558[/C][C]0.3939[/C][C]0.53321[/C][C]0.58309[/C][C]0.14945[/C][C]0.20039[/C][/ROW]
[ROW][C]midrange[/C][C]-0.7374[/C][C]-0.73707[/C][C]-0.62714[/C][C]-0.60652[/C][C]-0.37681[/C][C]-0.36917[/C][C]-0.33604[/C][C]0.1396[/C][C]0.25033[/C][/ROW]
[ROW][C]mode[/C][C]-2.6197[/C][C]-1.7079[/C][C]0.19881[/C][C]2.2146[/C][C]2.2146[/C][C]2.6124[/C][C]3.6124[/C][C]1.4375[/C][C]2.0158[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.20065[/C][C]0.0095121[/C][C]0.36657[/C][C]0.57915[/C][C]0.77644[/C][C]1.1926[/C][C]1.542[/C][C]0.3662[/C][C]0.40986[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271008&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271008&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-0.31654-0.23361-0.0949021.982e-070.0752560.1950.258930.130470.17016
median-0.0710550.0288090.193520.25580.39390.533210.583090.149450.20039
midrange-0.7374-0.73707-0.62714-0.60652-0.37681-0.36917-0.336040.13960.25033
mode-2.6197-1.70790.198812.21462.21462.61243.61241.43752.0158
mode k.dens-0.200650.00951210.366570.579150.776441.19261.5420.36620.40986


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 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')