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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, 11 Dec 2014 15:37:53 +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/11/t1418312485qyp52g5qwdpk3xa.htm/, Retrieved Thu, 16 May 2024 17:20:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266132, Retrieved Thu, 16 May 2024 17:20:42 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

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] [] [2014-12-11 15:37:53] [83f8f1d217ef29583e8b7cd372ece6b5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,46429
2,46429
2,46429
-1,53571
3,46429
-7,53571
0,464286
2,46429
1,46429
-0,535714
2,46429
-4,53571
0,464286
-0,535714
-1,53571
-4,53571
0,464286
1,46429
1,46429
-4,53571
-3,53571
3,46429
1,46429
-1,53571
5,46429
-10,5357
10,4643
0,464286
3,46429
-3,53571
-3,53571
-0,535714
5,46429
-1,53571
3,46429
1,46429
-9,53571
-5,53571
2,46429
6,46429
3,46429
3,46429
4,46429
4,46429
-1,53571
3,46429
-4,53571
-0,535714
-3,53571
5,46429
3,46429
-2,53571
-0,535714
1,46429
-1,53571
7,46429
1,46429
-6,53571
-11,5357
9,46429
8,46429
3,46429
1,46429
-0,535714
-0,535714
0,464286
-1,53571
-0,535714
-2,53571
-0,535714
5,46429
-0,535714
2,46429
3,46429
-5,53571
-3,53571
4,46429
0,464286
-7,53571
4,46429
2,46429
-7,53571
2,46429
0,464286
-9,53571
3,46429
-2,53571
2,46429
4,46429
-1,53571
1,46429
0,464286
0,464286
2,46429
-0,535714
0,464286
6,46429
3,46429
4,46429
1,46429
1,46429
-0,535714
-11,5357
-2,53571
0,464286
-8,53571
-11,5357
-4,53571
-1,53571
-1,53571
-0,535714
-0,535714
2,69277
1,69277
0,692771
4,69277
9,69277
-3,30723
6,69277
2,69277
2,69277
-2,30723
-2,30723
2,69277
-1,30723
-5,30723
-0,307229
-4,30723
3,69277
4,69277
4,69277
-1,30723
-1,30723
-4,30723
-1,30723
-1,30723
2,69277
0,692771
1,69277
-1,30723
-0,307229
-0,307229
-17,3072
2,69277
2,69277
-0,307229
-5,30723
-4,30723
-13,3072
3,69277
-3,30723
3,69277
3,69277
-1,30723
4,69277
-0,307229
7,69277
2,69277
6,69277
-2,30723
7,69277
0,692771
-1,30723
2,69277
6,69277
1,69277
7,69277
4,69277
0,692771
1,69277
7,69277
-0,307229
8,69277
4,69277
4,69277
-0,307229
-0,307229
-4,30723
-0,307229
-0,307229
2,69277
-2,30723
4,69277
-2,30723
-1,30723
4,69277
4,69277
4,69277
3,69277
-1,30723
5,69277
-10,3072
-3,30723
-7,30723
-3,30723
9,69277
4,69277
-16,3072
-4,30723
0,692771
2,69277
1,69277
-3,30723
-0,307229
-0,307229
1,69277
-4,30723
2,69277
-20,3072
-2,30723
4,69277
2,69277
-8,30723
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3,69277
-9,30723
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2,69277
3,69277
8,69277
-2,30723
-5,30723
8,69277
-4,30723
-1,30723
1,69277
-4,30723
2,69277
2,69277
-1,30723
-16,3072
-0,307229
3,69277
-0,307229
-16,3072
3,69277
1,69277
-4,30723
-17,3072
-5,30723
3,69277
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-0,307229
6,69277
5,69277
2,69277
-3,30723
-0,307229
1,69277
-1,30723
3,69277
-1,30723
2,69277
-5,30723
6,69277
5,69277
1,69277
1,69277
-2,30723
-5,30723
1,69277
6,69277
-10,3072
-0,307229
-3,30723
2,69277
-1,30723
-7,30723
6,69277
2,69277
-4,30723
4,69277
-18,3072
5,69277
-0,307229
2,69277
1,69277
3,69277

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266132&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266132&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266132&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'Gertrude Mary Cox' @ cox.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.69028-0.44691-0.221712.0612e-060.21670.424550.681740.298240.43841
median-0.30723-0.307230.464290.464290.607091.46431.46540.490680.1428
midrange-5.4215-5.3072-4.9215-4.9215-3.9215-3.4215-2.92150.65611
mode-1.3072-1.3072-0.307232.69282.69283.69284.69281.69083
mode k.dens-0.96545-0.84053-0.456212.38062.67743.08173.26251.5543.1336

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.69028 & -0.44691 & -0.22171 & 2.0612e-06 & 0.2167 & 0.42455 & 0.68174 & 0.29824 & 0.43841 \tabularnewline
median & -0.30723 & -0.30723 & 0.46429 & 0.46429 & 0.60709 & 1.4643 & 1.4654 & 0.49068 & 0.1428 \tabularnewline
midrange & -5.4215 & -5.3072 & -4.9215 & -4.9215 & -3.9215 & -3.4215 & -2.9215 & 0.6561 & 1 \tabularnewline
mode & -1.3072 & -1.3072 & -0.30723 & 2.6928 & 2.6928 & 3.6928 & 4.6928 & 1.6908 & 3 \tabularnewline
mode k.dens & -0.96545 & -0.84053 & -0.45621 & 2.3806 & 2.6774 & 3.0817 & 3.2625 & 1.554 & 3.1336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266132&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]-0.69028[/C][C]-0.44691[/C][C]-0.22171[/C][C]2.0612e-06[/C][C]0.2167[/C][C]0.42455[/C][C]0.68174[/C][C]0.29824[/C][C]0.43841[/C][/ROW]
[ROW][C]median[/C][C]-0.30723[/C][C]-0.30723[/C][C]0.46429[/C][C]0.46429[/C][C]0.60709[/C][C]1.4643[/C][C]1.4654[/C][C]0.49068[/C][C]0.1428[/C][/ROW]
[ROW][C]midrange[/C][C]-5.4215[/C][C]-5.3072[/C][C]-4.9215[/C][C]-4.9215[/C][C]-3.9215[/C][C]-3.4215[/C][C]-2.9215[/C][C]0.6561[/C][C]1[/C][/ROW]
[ROW][C]mode[/C][C]-1.3072[/C][C]-1.3072[/C][C]-0.30723[/C][C]2.6928[/C][C]2.6928[/C][C]3.6928[/C][C]4.6928[/C][C]1.6908[/C][C]3[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.96545[/C][C]-0.84053[/C][C]-0.45621[/C][C]2.3806[/C][C]2.6774[/C][C]3.0817[/C][C]3.2625[/C][C]1.554[/C][C]3.1336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266132&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266132&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-0.69028-0.44691-0.221712.0612e-060.21670.424550.681740.298240.43841
median-0.30723-0.307230.464290.464290.607091.46431.46540.490680.1428
midrange-5.4215-5.3072-4.9215-4.9215-3.9215-3.4215-2.92150.65611
mode-1.3072-1.3072-0.307232.69282.69283.69284.69281.69083
mode k.dens-0.96545-0.84053-0.456212.38062.67743.08173.26251.5543.1336


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 = 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')