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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 15:15:41 +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/t1415719073y8g2320ea05wvq5.htm/, Retrieved Sun, 19 May 2024 12:40:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253655, Retrieved Sun, 19 May 2024 12:40:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94

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] [WS7 berkenening t...] [2014-11-11 15:15:41] [8568a324fefbb8dbb43f697bfa8d1be6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,19814
-0,350526
-2,66877
9,32888
1,6214
9,93459
-1,84868
-2,33115
0,49835
-0,561255
-1,16802
-0,47892
2,66376
0,295939
1,06278
1,08969
1,03851
-2,42223
1,65476
-3,00596
-0,874179
-0,16186
-0,228289
0,556501
-6,83157
-0,401632
2,10431
1,9554
-2,25937
-2,44985
0,565526
-0,91088
-1,10172
0,0158956
-5,06817
2,39776
-0,289498
-0,796891
-3,50031
-3,01769
2,36285
-2,3933
-0,528472
-3,34581
-2,60899
-3,31276
-1,27647
4,28993
-2,62574
-1,37154
0,864888
2,46347
-0,187366
-4,64141
-0,538426
1,79638
-3,17015
-4,32754
0,774147
2,15447
-1,34395
-2,78029
0,783906
1,02323
-4,38807
2,59552
-1,83823
0,319833
1,92447
-0,848499
3,13086
1,27872
-1,6582
-2,3029
5,07457
2,43558
3,56448
0,380975
-4,22436
-1,10707
-0,692393
-3,07553
0,603121
-0,30265
0,193688
0,489952
-0,77014
1,46207
4,15595
1,45514
1,95503
0,841168
2,1669
-3,98145
-0,343911
1,95365
-1,80327
0,448734
-2,55534
-0,468088
1,85501
1,45022
-3,99657
3,78113
2,44604
-0,0889025
4,03991
-0,580606
7,61272
1,85603
0,182817
-2,53441
1,69002
1,40792
-0,841567
0,830792
-0,389637
-4,32545
2,31912
0,223467
1,37259
-1,5238
-3,94535
-1,11535
-2,35843
-2,71762
-0,359716
1,4021
0,311555
-2,57886
2,59737
-2,14191
2,16457
-0,367438
2,55551
7,60156
1,02417
0,109089
-1,85724
-2,98978
-2,2889
2,25106
-0,559822
0,061958
-1,28258
1,13165
-2,33256
-3,14328
2,16716
-0,550863
3,58591
-2,56579
-2,52726
2,23791
3,42
1,95503
0,360106
1,4021
-4,45635
2,92925
1,82722

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253655&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]7 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=253655&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253655&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 time7 seconds
R Server'George Udny Yule' @ yule.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.3977-0.31015-0.11420.00701340.134210.288330.405520.18670.24841
median-0.5287-0.36744-0.2895-0.0889020.182820.360110.556590.26420.47232
midrange-0.394010.390581.27231.55152.34372.64662.73950.68741.0715
mode-4.2254-2.9397-0.894371.67861.31572.60073.8281.75262.2101
mode k.dens-0.84472-0.49118-0.196610.250251.46391.97262.09820.902531.6605

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.3977 & -0.31015 & -0.1142 & 0.0070134 & 0.13421 & 0.28833 & 0.40552 & 0.1867 & 0.24841 \tabularnewline
median & -0.5287 & -0.36744 & -0.2895 & -0.088902 & 0.18282 & 0.36011 & 0.55659 & 0.2642 & 0.47232 \tabularnewline
midrange & -0.39401 & 0.39058 & 1.2723 & 1.5515 & 2.3437 & 2.6466 & 2.7395 & 0.6874 & 1.0715 \tabularnewline
mode & -4.2254 & -2.9397 & -0.89437 & 1.6786 & 1.3157 & 2.6007 & 3.828 & 1.7526 & 2.2101 \tabularnewline
mode k.dens & -0.84472 & -0.49118 & -0.19661 & 0.25025 & 1.4639 & 1.9726 & 2.0982 & 0.90253 & 1.6605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253655&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]-0.3977[/C][C]-0.31015[/C][C]-0.1142[/C][C]0.0070134[/C][C]0.13421[/C][C]0.28833[/C][C]0.40552[/C][C]0.1867[/C][C]0.24841[/C][/ROW]
[ROW][C]median[/C][C]-0.5287[/C][C]-0.36744[/C][C]-0.2895[/C][C]-0.088902[/C][C]0.18282[/C][C]0.36011[/C][C]0.55659[/C][C]0.2642[/C][C]0.47232[/C][/ROW]
[ROW][C]midrange[/C][C]-0.39401[/C][C]0.39058[/C][C]1.2723[/C][C]1.5515[/C][C]2.3437[/C][C]2.6466[/C][C]2.7395[/C][C]0.6874[/C][C]1.0715[/C][/ROW]
[ROW][C]mode[/C][C]-4.2254[/C][C]-2.9397[/C][C]-0.89437[/C][C]1.6786[/C][C]1.3157[/C][C]2.6007[/C][C]3.828[/C][C]1.7526[/C][C]2.2101[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.84472[/C][C]-0.49118[/C][C]-0.19661[/C][C]0.25025[/C][C]1.4639[/C][C]1.9726[/C][C]2.0982[/C][C]0.90253[/C][C]1.6605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253655&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253655&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-0.3977-0.31015-0.11420.00701340.134210.288330.405520.18670.24841
median-0.5287-0.36744-0.2895-0.0889020.182820.360110.556590.26420.47232
midrange-0.394010.390581.27231.55152.34372.64662.73950.68741.0715
mode-4.2254-2.9397-0.894371.67861.31572.60073.8281.75262.2101
mode k.dens-0.84472-0.49118-0.196610.250251.46391.97262.09820.902531.6605


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')