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Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationWed, 11 Dec 2013 12:11:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/11/t1386781948mqdauoznq7w8xke.htm/, Retrieved Thu, 28 Mar 2024 13:30:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232118, Retrieved Thu, 28 Mar 2024 13:30:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2013-12-11 17:11:31] [f6b0814d1ccce07ea30140b42d9cb647] [Current]
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Dataseries X:
20
25
15
15
25
25
25
21
30
25
20
40
13
30
25
20
25
20
25
20
20
15
15
12
20
5
20
15
25
22
20
22
25
20
20
35
30
25
20
20
20
25
25
15
20
35
25
25
30
23
10
22
25
25
22
30
20
25
25
22
25
25
25
22
25
12
18
20
20
22
30
25
22
20
50
30
25
20
30
22
25
30
22
25
22
22
25
25
25
20
22
15
20
30
20
25
30
35
22
12
30
15
10
30
9
25
20
20
35
25
35
30
12
25
15
25
25
20
20
6
15
40
20
40
25
25
20
15
15
22
24
22
20
25
25
25
35
40
20
22
22
20
25
25
18
25
20
25
30
20
22
35
22
25
25
25
25
22
23
35
15
25
18
22
25
25
28
30
20
25
25
30
22
30
10
10
25
20
22
25
25
15
22
25
25
28
22
30
25
20
25
25
20
30
20
30
50
19
20
28
20
25
35
25
25
15
16
20
20
25
30
20
25
25
25
20
20
25
25
30
22
20
25
25
18
18
20
25
25
30
25
20
25
20
20
20
22
18
22
20
15
25
25
20
25
15
22
25
25
15
12
25
30
22
15
22
25
12
18
30
25
25
40
24
25
15
25
20
25
25
25
20
30
20
25
30
22
25
25
25
50
19
50
25
35
20
20
20
20
20
25
25
25
20
20
20
20
25
18
25
22
22
30
30
8
20
25
30
50
22
20
10
25
25
25
25
18
25
20
25
30
18
20
25
22
22
20
20
25
20
20
20
20
25
20
10
20
25
30
25
50
30
30
50
15
25
25
22
20
22
30
25
18
22
22
30
40
25
20
10
20
9
15
20
15
20
30
12
15
12
20
15
12
25
20
25
25
25
30
20
25
15
15
22
10
15
10
20
25
20
20
38
20
20
20
40
25
25
30
25
10
20
25
12
15
25
20
22
22
20
25
25
25
15
40
20
20
16
25
15
20
25
20
30
50
20
25
20
30
30
25
25
12
25
25
25
20
20
20
15
20
25
15
25
50
30
20
20
25
12
15
20
20
35
22
15
18
30
22
12
12
20
20
15
25
15
20
20
25
18
30
20
25
25
25
20
20
25
20
22
15
15
22
20
10
25
20
20
15
12
20
5
20
15
15
25
25
25
15
25
22
25
20
18
22
25
35
25
25
25
35
30
22
30
50
15
25
24
20
25
25
25
12
15
22
25
25
25
25
15
20
20
15
35
30
20
22
65
20
25
22
20
25
25
20
25
15
20
12
15
10
25
15
30
35
25
25
25
25
25
40
40
25
25
20
25
25
22
25
30
25
25
30
25
25
30
25
25
20
22
22
20
25
22
25
22
40
25
25
25
22
20
35
20
35
25
22
25
25
25
25
25
40
25
30
25
20
25
25
30
22
22
20
15
15
25
25
20
20
15
25
15
20
22
25
15
15
18
5
15
25
18
40
25
25
20
30
20
25
25
25
22
22
25
25
30
25
25
25
25
20
20
25
25
25
25
20
30
25
22
30
20
20
30
25
25
30
20
25
25
24
25
30
18
15
22
22
25
22
22
25
15
20
22
18
35
20
20
20
25
25
30
15
25
22
26
25
20
25
25
25
22
25
25
20
22
30
15
30
25
20
25
25
35
22
20
25
20
20
18
20
22
25
10
20
25
20
20
30
25
20
15
20
25
10
20
25
22
22
25
25
15
25
20
10
25
16
25
35
25
15
25
25
30
25
10
22
20
25
20
20
25
22
18
30
19
25
20
25
20
25
20
22
12
30
12
22
25
25
25
25
30
30
10
22
22
25
20
22
20
25
20
15
25
20
25
20
30
15
40
25
20
22
22
30
20
40
20
25
20
25
20
50
50
25
25
40
30
22
30
20
25
25
30
25
25
20
18
18
28
25
22
15
40
40
12
12
18
12
25
26
18
25
22
15
25
15
15
15
25
15
12
22
20
20
25
20
12
9
15
12
15
25
20
20
15
15
30
21
25
22
22
50
15
25
15
25
22
18
50
20
50
20
20
30
25
20
22
25
50
40
25
25
25
25
30
40
25
30
20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 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 & 14 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232118&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]14 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=232118&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232118&T=0

As an alternative you can also use a 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 time14 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean22.68722.85123.06923.16623.36423.67523.840.255860.295
median2222222224.525251.31722.5
midrange27.527.527.535353536.7553.64977.5
mode2525252525252500
mode k.dens13.4120.923252325252.24677.6464e-08

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 22.687 & 22.851 & 23.069 & 23.166 & 23.364 & 23.675 & 23.84 & 0.25586 & 0.295 \tabularnewline
median & 22 & 22 & 22 & 22 & 24.5 & 25 & 25 & 1.3172 & 2.5 \tabularnewline
midrange & 27.5 & 27.5 & 27.5 & 35 & 35 & 35 & 36.755 & 3.6497 & 7.5 \tabularnewline
mode & 25 & 25 & 25 & 25 & 25 & 25 & 25 & 0 & 0 \tabularnewline
mode k.dens & 13.41 & 20.9 & 23 & 25 & 23 & 25 & 25 & 2.2467 & 7.6464e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232118&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]22.687[/C][C]22.851[/C][C]23.069[/C][C]23.166[/C][C]23.364[/C][C]23.675[/C][C]23.84[/C][C]0.25586[/C][C]0.295[/C][/ROW]
[ROW][C]median[/C][C]22[/C][C]22[/C][C]22[/C][C]22[/C][C]24.5[/C][C]25[/C][C]25[/C][C]1.3172[/C][C]2.5[/C][/ROW]
[ROW][C]midrange[/C][C]27.5[/C][C]27.5[/C][C]27.5[/C][C]35[/C][C]35[/C][C]35[/C][C]36.755[/C][C]3.6497[/C][C]7.5[/C][/ROW]
[ROW][C]mode[/C][C]25[/C][C]25[/C][C]25[/C][C]25[/C][C]25[/C][C]25[/C][C]25[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]13.41[/C][C]20.9[/C][C]23[/C][C]25[/C][C]23[/C][C]25[/C][C]25[/C][C]2.2467[/C][C]7.6464e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232118&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232118&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean22.68722.85123.06923.16623.36423.67523.840.255860.295
median2222222224.525251.31722.5
midrange27.527.527.535353536.7553.64977.5
mode2525252525252500
mode k.dens13.4120.923252325252.24677.6464e-08



Parameters (Session):
par1 = 50 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 50 ; 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')