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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 15:52:09 +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/t14189179589x1nz50juo7en23.htm/, Retrieved Fri, 17 May 2024 15:02:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271100, Retrieved Fri, 17 May 2024 15:02:29 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

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] [hhh] [2014-12-18 15:52:09] [a9ee49ff8435be51911716bad99dd485] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-6,41976
1,95584
2,481
1,59106
0,171945
0,924867
-1,98636
1,58925
-0,152465
1,38311
1,73434
5,07961
-2,56681
-0,934386
1,11955
0,27169
2,59868
0,746788
0,743703
-1,10954
1,20628
1,28789
0,574467
0,289864
2,65568
-0,850359
1,92317
1,73955
0,734516
1,06408
0,430102
3,41979
-0,513054
0,834576
-1,06684
-0,23728
-0,757966
1,17223
-7,12696
1,70212
1,59014
-1,33625
1,47937
-0,243855
1,37461
1,02118
-1,93599
-2,24733
1,81495
2,11697
-1,16262
7,89236
0,92947
0,596671
2,0531
0,770575
-0,461071
-2,37481
1,12163
2,42824
-2,11389
-1,54811
-1,47402
3,06412
-0,871647
2,17516
-1,8446
-2,48319
-1,07726
-2,33453
2,72132
-0,0353912
-3,16605
0,411643
1,89442
-2,63232
-3,57092
0,736266
-2,20346
1,40921
-3,18183
2,84427
-0,372136
1,74669
-2,0398
0,41461
1,11717
0,174612
-0,292515
0,400766
-2,44281
-0,493005
-0,829031
1,42393
0,0555369
0,991915
3,76617
3,10303
-0,144162
-0,843882
-1,84347
0,0981675
-0,419483
-0,301596
1,80971
0,626071
-0,711711
-0,711518
1,86585
-3,61085
-1,97666
1,67152
1,8776
3,62651
1,03047
2,04961
3,9987
2,48891
1,57707
2,96076
-0,49069
-3,90152
-2,82126
-6,12903
0,333796
-0,947818
0,0351173
-1,03416
-1,31424
-1,12314
0,190157
1,96992
-2,48381
-0,292196
-0,375422
0,701582
0,756858
-1,40347
-3,48661
-0,770958
0,767651
2,30313
-2,67823
0,697071
-4,49394
-5,17974
-1,87493
-7,00293
-0,415171
-2,08349
-3,15921
0,741913
1,539
0,805109
-0,416035
0,606332
1,06906
-1,16961
-0,565184
-0,886862
1,34725
1,76899
0,350464
-0,821405
0,90109
-1,25667

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=271100&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=271100&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271100&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-0.35163-0.29472-0.111684.5181e-080.138230.278830.348620.169910.24991
median-0.29378-0.152460.135060.280780.406580.652190.734520.238560.27152
midrange-1.7502-1.5641-1.02370.38270.38270.73631.35630.831051.4064
mode-4.5132-2.8738-0.850364.5181e-081.06912.48692.96361.65261.9194
mode k.dens-0.76765-0.670340.477170.870981.08391.42391.57590.61880.60677

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.35163 & -0.29472 & -0.11168 & 4.5181e-08 & 0.13823 & 0.27883 & 0.34862 & 0.16991 & 0.24991 \tabularnewline
median & -0.29378 & -0.15246 & 0.13506 & 0.28078 & 0.40658 & 0.65219 & 0.73452 & 0.23856 & 0.27152 \tabularnewline
midrange & -1.7502 & -1.5641 & -1.0237 & 0.3827 & 0.3827 & 0.7363 & 1.3563 & 0.83105 & 1.4064 \tabularnewline
mode & -4.5132 & -2.8738 & -0.85036 & 4.5181e-08 & 1.0691 & 2.4869 & 2.9636 & 1.6526 & 1.9194 \tabularnewline
mode k.dens & -0.76765 & -0.67034 & 0.47717 & 0.87098 & 1.0839 & 1.4239 & 1.5759 & 0.6188 & 0.60677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271100&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]-0.35163[/C][C]-0.29472[/C][C]-0.11168[/C][C]4.5181e-08[/C][C]0.13823[/C][C]0.27883[/C][C]0.34862[/C][C]0.16991[/C][C]0.24991[/C][/ROW]
[ROW][C]median[/C][C]-0.29378[/C][C]-0.15246[/C][C]0.13506[/C][C]0.28078[/C][C]0.40658[/C][C]0.65219[/C][C]0.73452[/C][C]0.23856[/C][C]0.27152[/C][/ROW]
[ROW][C]midrange[/C][C]-1.7502[/C][C]-1.5641[/C][C]-1.0237[/C][C]0.3827[/C][C]0.3827[/C][C]0.7363[/C][C]1.3563[/C][C]0.83105[/C][C]1.4064[/C][/ROW]
[ROW][C]mode[/C][C]-4.5132[/C][C]-2.8738[/C][C]-0.85036[/C][C]4.5181e-08[/C][C]1.0691[/C][C]2.4869[/C][C]2.9636[/C][C]1.6526[/C][C]1.9194[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.76765[/C][C]-0.67034[/C][C]0.47717[/C][C]0.87098[/C][C]1.0839[/C][C]1.4239[/C][C]1.5759[/C][C]0.6188[/C][C]0.60677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271100&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271100&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-0.35163-0.29472-0.111684.5181e-080.138230.278830.348620.169910.24991
median-0.29378-0.152460.135060.280780.406580.652190.734520.238560.27152
midrange-1.7502-1.5641-1.02370.38270.38270.73631.35630.831051.4064
mode-4.5132-2.8738-0.850364.5181e-081.06912.48692.96361.65261.9194
mode k.dens-0.76765-0.670340.477170.870981.08391.42391.57590.61880.60677


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')