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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 computationSun, 23 Aug 2015 00:38:08 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Aug/23/t1440286774sbm05fc97b8dl54.htm/, Retrieved Sun, 19 May 2024 10:11:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280310, Retrieved Sun, 19 May 2024 10:11:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

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] [] [2015-08-22 23:38:08] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-1.29645
-3.76725
3.90394
0.0256704
-0.263031
0.964993
-5.10262
-0.754058
-0.719175
-6.18235
-0.959807
-5.65419
-5.32505
-0.186947
-3.74666
-0.0899766
-4.73029
0.344772
0.342963
-1.55106
-1.62251
0.949893
-1.81629
-7.71729
-2.3688
-1.45806
-2.12354
-1.35365
-4.31565
0.747161
-0.40803
-5.70667
-3.84458
-2.77395
-4.70137
-3.94307
-5.52807
-1.82072
1.44154
-4.70527
-1.70174
-2.17259
-2.515
-3.24076
0.860907
-4.319
0.0771163
-7.38614
-6.10739
-4.38256
-1.69982
-1.88349
2.54144
-1.01406
4.62323
0.658101
2.0009
-0.70019
0.994737
-0.387287
5.59678
3.18632
5.01802
-0.631933
6.31677
1.72454
0.933196
-0.812001
1.86469
4.8215
1.78195
5.68898
3.27304
0.0287654
0.93089
3.18451
1.02909
3.12794
4.39558
0.205992
-0.853247
-0.212624
-1.52523
2.76632
-3.31129
5.1438
1.12494
1.63141
1.32901
2.65448
2.42112
-1.05168
3.59909
3.14248
-0.898404
-0.469419
7.15791
4.33695
-1.70029
0.103503
4.73549
-0.542946
2.32482
-4.02634
2.23257
2.64728
-0.143374
-1.60004
-0.243714
-0.109677
2.18763
6.33567
-2.7923
2.51815
2.83186
5.77003
3.88405
-3.84735
-0.50475
-4.69168
-2.36181
0.0839599
-2.0676
1.82964
4.0741
-0.35445
1.68154
3.0107
3.28306
-1.85556
3.38573
-0.557104
2.60181
4.26535
2.14411
4.02654
-3.6397

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280310&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280310&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280310&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'Herman Ole Andreas Wold' @ wold.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.56016-0.42715-0.18793.3139e-070.178140.435410.566750.267850.36603
median-0.63193-0.46942-0.24371-0.143370.025670.342960.93320.274090.26938
midrange-1.0142-0.97363-0.52523-0.27969-0.114110.487780.525260.344010.41112
mode-7.7173-4.7303-1.6513.3139e-071.47223.9245.6042.68073.1232
mode k.dens-1.2647-0.96501-0.63732-0.42759-0.175961.62982.74230.776590.46136

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.56016 & -0.42715 & -0.1879 & 3.3139e-07 & 0.17814 & 0.43541 & 0.56675 & 0.26785 & 0.36603 \tabularnewline
median & -0.63193 & -0.46942 & -0.24371 & -0.14337 & 0.02567 & 0.34296 & 0.9332 & 0.27409 & 0.26938 \tabularnewline
midrange & -1.0142 & -0.97363 & -0.52523 & -0.27969 & -0.11411 & 0.48778 & 0.52526 & 0.34401 & 0.41112 \tabularnewline
mode & -7.7173 & -4.7303 & -1.651 & 3.3139e-07 & 1.4722 & 3.924 & 5.604 & 2.6807 & 3.1232 \tabularnewline
mode k.dens & -1.2647 & -0.96501 & -0.63732 & -0.42759 & -0.17596 & 1.6298 & 2.7423 & 0.77659 & 0.46136 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280310&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]-0.56016[/C][C]-0.42715[/C][C]-0.1879[/C][C]3.3139e-07[/C][C]0.17814[/C][C]0.43541[/C][C]0.56675[/C][C]0.26785[/C][C]0.36603[/C][/ROW]
[ROW][C]median[/C][C]-0.63193[/C][C]-0.46942[/C][C]-0.24371[/C][C]-0.14337[/C][C]0.02567[/C][C]0.34296[/C][C]0.9332[/C][C]0.27409[/C][C]0.26938[/C][/ROW]
[ROW][C]midrange[/C][C]-1.0142[/C][C]-0.97363[/C][C]-0.52523[/C][C]-0.27969[/C][C]-0.11411[/C][C]0.48778[/C][C]0.52526[/C][C]0.34401[/C][C]0.41112[/C][/ROW]
[ROW][C]mode[/C][C]-7.7173[/C][C]-4.7303[/C][C]-1.651[/C][C]3.3139e-07[/C][C]1.4722[/C][C]3.924[/C][C]5.604[/C][C]2.6807[/C][C]3.1232[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.2647[/C][C]-0.96501[/C][C]-0.63732[/C][C]-0.42759[/C][C]-0.17596[/C][C]1.6298[/C][C]2.7423[/C][C]0.77659[/C][C]0.46136[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280310&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280310&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-0.56016-0.42715-0.18793.3139e-070.178140.435410.566750.267850.36603
median-0.63193-0.46942-0.24371-0.143370.025670.342960.93320.274090.26938
midrange-1.0142-0.97363-0.52523-0.27969-0.114110.487780.525260.344010.41112
mode-7.7173-4.7303-1.6513.3139e-071.47223.9245.6042.68073.1232
mode k.dens-1.2647-0.96501-0.63732-0.42759-0.175961.62982.74230.776590.46136


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')