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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 15:59:14 +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/t141831363003rzv7t2zd8fjsf.htm/, Retrieved Thu, 16 May 2024 12:34:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266147, Retrieved Thu, 16 May 2024 12:34:14 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

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 15:59:14] [83f8f1d217ef29583e8b7cd372ece6b5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2,00833
1,24051
3,00833
-2,75949
2,24051
-8,75949
1,00833
1,24051
0,240506
-1,75949
1,24051
-5,75949
-0,759494
0,00833333
-0,991667
-3,99167
-0,759494
2,00833
0,240506
-3,99167
-4,75949
2,24051
2,00833
-2,75949
4,24051
-11,7595
9,24051
1,00833
2,24051
-2,99167
-2,99167
0,00833333
4,24051
-2,75949
2,24051
0,240506
-8,99167
-6,75949
1,24051
7,00833
2,24051
2,24051
3,24051
3,24051
-2,75949
4,00833
-5,75949
-1,75949
-2,99167
4,24051
2,24051
-3,75949
-1,75949
0,240506
-2,75949
6,24051
2,00833
-7,75949
-10,9917
8,24051
7,24051
4,00833
2,00833
-1,75949
0,00833333
-0,759494
-0,991667
-1,75949
-3,75949
0,00833333
6,00833
0,00833333
3,00833
2,24051
-4,99167
-2,99167
3,24051
1,00833
-6,99167
3,24051
3,00833
-6,99167
3,00833
-0,759494
-8,99167
2,24051
-3,75949
3,00833
5,00833
-0,991667
0,240506
-0,759494
-0,759494
3,00833
-1,75949
1,00833
5,24051
2,24051
3,24051
0,240506
0,240506
0,00833333
-12,7595
-3,75949
-0,759494
-7,99167
-10,9917
-3,99167
-0,991667
-0,991667
0,00833333
-1,75949
2,24051
1,24051
0,240506
4,24051
11,0083
-3,75949
6,24051
4,00833
2,24051
-0,991667
-0,991667
2,24051
-1,75949
-5,75949
-0,759494
-4,75949
3,24051
4,24051
4,24051
0,00833333
-1,75949
-4,75949
-1,75949
-1,75949
2,24051
0,240506
3,00833
-1,75949
-0,759494
-0,759494
-17,7595
2,24051
4,00833
1,00833
-5,75949
-2,99167
-11,9917
3,24051
-1,99167
3,24051
3,24051
0,00833333
4,24051
-0,759494
7,24051
4,00833
8,00833
-0,991667
9,00833
0,240506
0,00833333
2,24051
8,00833
1,24051
9,00833
4,24051
2,00833
3,00833
7,24051
1,00833
8,24051
4,24051
4,24051
-0,759494
-0,759494
-2,99167
-0,759494
1,00833
4,00833
-0,991667
4,24051
-0,991667
-1,75949
6,00833
6,00833
6,00833
5,00833
-1,75949
5,24051
-10,7595
-3,75949
-5,99167
-1,99167
9,24051
6,00833
-14,9917
-2,99167
2,00833
2,24051
1,24051
-1,99167
1,00833
-0,759494
3,00833
-4,75949
2,24051
-18,9917
-2,75949
4,24051
2,24051
-6,99167
-0,991667
5,00833
-9,75949
-2,75949
2,24051
3,24051
10,0083
-0,991667
-3,99167
8,24051
-4,75949
0,00833333
3,00833
-2,99167
2,24051
2,24051
0,00833333
-14,9917
1,00833
3,24051
-0,759494
-16,7595
3,24051
3,00833
-4,75949
-17,7595
-5,75949
5,00833
-1,99167
-0,759494
8,00833
5,24051
2,24051
-1,99167
-0,759494
3,00833
-1,75949
3,24051
0,00833333
2,24051
-3,99167
6,24051
7,00833
1,24051
3,00833
-0,991667
-5,75949
1,24051
8,00833
-10,7595
-0,759494
-1,99167
2,24051
0,00833333
-5,99167
6,24051
2,24051
-2,99167
4,24051
-16,9917
7,00833
-0,759494
4,00833
3,00833
3,24051

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'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266147&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266147&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266147&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.63091-0.49011-0.17253-6.9921e-070.223490.491390.569190.283840.39602
median-0.759490.00833330.211480.240510.72041.01991.24050.435230.50891
midrange-4.8756-4.8756-4.2885-3.9917-3.8756-2.9917-2.87180.52090.41292
mode-1.7595-0.759492.24052.24052.24052.24053.00831.26960
mode k.dens-1.2689-1.0212-0.701212.54352.54952.77262.88161.61383.2508

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.63091 & -0.49011 & -0.17253 & -6.9921e-07 & 0.22349 & 0.49139 & 0.56919 & 0.28384 & 0.39602 \tabularnewline
median & -0.75949 & 0.0083333 & 0.21148 & 0.24051 & 0.7204 & 1.0199 & 1.2405 & 0.43523 & 0.50891 \tabularnewline
midrange & -4.8756 & -4.8756 & -4.2885 & -3.9917 & -3.8756 & -2.9917 & -2.8718 & 0.5209 & 0.41292 \tabularnewline
mode & -1.7595 & -0.75949 & 2.2405 & 2.2405 & 2.2405 & 2.2405 & 3.0083 & 1.2696 & 0 \tabularnewline
mode k.dens & -1.2689 & -1.0212 & -0.70121 & 2.5435 & 2.5495 & 2.7726 & 2.8816 & 1.6138 & 3.2508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266147&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]-0.63091[/C][C]-0.49011[/C][C]-0.17253[/C][C]-6.9921e-07[/C][C]0.22349[/C][C]0.49139[/C][C]0.56919[/C][C]0.28384[/C][C]0.39602[/C][/ROW]
[ROW][C]median[/C][C]-0.75949[/C][C]0.0083333[/C][C]0.21148[/C][C]0.24051[/C][C]0.7204[/C][C]1.0199[/C][C]1.2405[/C][C]0.43523[/C][C]0.50891[/C][/ROW]
[ROW][C]midrange[/C][C]-4.8756[/C][C]-4.8756[/C][C]-4.2885[/C][C]-3.9917[/C][C]-3.8756[/C][C]-2.9917[/C][C]-2.8718[/C][C]0.5209[/C][C]0.41292[/C][/ROW]
[ROW][C]mode[/C][C]-1.7595[/C][C]-0.75949[/C][C]2.2405[/C][C]2.2405[/C][C]2.2405[/C][C]2.2405[/C][C]3.0083[/C][C]1.2696[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.2689[/C][C]-1.0212[/C][C]-0.70121[/C][C]2.5435[/C][C]2.5495[/C][C]2.7726[/C][C]2.8816[/C][C]1.6138[/C][C]3.2508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266147&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266147&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-0.63091-0.49011-0.17253-6.9921e-070.223490.491390.569190.283840.39602
median-0.759490.00833330.211480.240510.72041.01991.24050.435230.50891
midrange-4.8756-4.8756-4.2885-3.9917-3.8756-2.9917-2.87180.52090.41292
mode-1.7595-0.759492.24052.24052.24052.24053.00831.26960
mode k.dens-1.2689-1.0212-0.701212.54352.54952.77262.88161.61383.2508


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')