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Author

*Unverified author*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Tue, 21 May 2013 09:46:32 -0400

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/May/21/t1369145414equ4f3bc9kxquj7.htm/, Retrieved Wed, 01 May 2024 23:56:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=209216, Retrieved Wed, 01 May 2024 23:56:07 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Bootstrap Plot - Central Tendency] [registraties nieu...] [2013-05-21 13:46:32] [618e20b48371a4632e04cdc6ff96552f] [Current]

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Dataseries X:

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Histogram

Boxplots

-5.3
-7.1
-8
-8.9
-7.7
-1.1
4
9.6
10.9
13
14.9
20.1
10.8
11
3.8
10.8
7.6
10.2
2.2
-0.1
-1.7
-4.8
-9.9
-13.5
-18.1
-18
-15.7
-15.2
-15.1
-17.9
-14.5
-9.4
-4.2
-2.2
4.5
12.4
15.8
11.5
14.1
18.8
26.1
27.9
25.4
23.4
11.5
9.9
8.1
12.6
8.2
5.4
1
-2.9
-3.7
-7
-7.2
-11.8
-2.1
1.2
2.5
4.8
-6.6
-16
-22.7
-17.7
-18.2
-18.9
-16
-12.2
-17.1
-18.6
-17.5
-24.9

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	22 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 & 22 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=209216&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]22 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=209216&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=209216&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	22 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-4.3084	-3.446	-1.9962	-0.90972	0.17639	1.639	2.9371	1.5787	2.1726
median	-7.1	-5.95	-3.3	-1.9	0.45	3	4.953	2.6561	3.75
midrange	-2.1795	0.25	0.6	1.5	2.6	4.5	4.85	1.3199	2
mode	-22.7	-17.5	-8.9	2.1	10.075	15.478	24.088	11.038	18.975
mode k.dens	-17.509	-17.003	-15.319	-13.973	8.1863	11.013	11.725	10.553	23.506

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -4.3084 & -3.446 & -1.9962 & -0.90972 & 0.17639 & 1.639 & 2.9371 & 1.5787 & 2.1726 \tabularnewline
median & -7.1 & -5.95 & -3.3 & -1.9 & 0.45 & 3 & 4.953 & 2.6561 & 3.75 \tabularnewline
midrange & -2.1795 & 0.25 & 0.6 & 1.5 & 2.6 & 4.5 & 4.85 & 1.3199 & 2 \tabularnewline
mode & -22.7 & -17.5 & -8.9 & 2.1 & 10.075 & 15.478 & 24.088 & 11.038 & 18.975 \tabularnewline
mode k.dens & -17.509 & -17.003 & -15.319 & -13.973 & 8.1863 & 11.013 & 11.725 & 10.553 & 23.506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=209216&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]-4.3084[/C][C]-3.446[/C][C]-1.9962[/C][C]-0.90972[/C][C]0.17639[/C][C]1.639[/C][C]2.9371[/C][C]1.5787[/C][C]2.1726[/C][/ROW]
[ROW][C]median[/C][C]-7.1[/C][C]-5.95[/C][C]-3.3[/C][C]-1.9[/C][C]0.45[/C][C]3[/C][C]4.953[/C][C]2.6561[/C][C]3.75[/C][/ROW]
[ROW][C]midrange[/C][C]-2.1795[/C][C]0.25[/C][C]0.6[/C][C]1.5[/C][C]2.6[/C][C]4.5[/C][C]4.85[/C][C]1.3199[/C][C]2[/C][/ROW]
[ROW][C]mode[/C][C]-22.7[/C][C]-17.5[/C][C]-8.9[/C][C]2.1[/C][C]10.075[/C][C]15.478[/C][C]24.088[/C][C]11.038[/C][C]18.975[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-17.509[/C][C]-17.003[/C][C]-15.319[/C][C]-13.973[/C][C]8.1863[/C][C]11.013[/C][C]11.725[/C][C]10.553[/C][C]23.506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=209216&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=209216&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
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-4.3084	-3.446	-1.9962	-0.90972	0.17639	1.639	2.9371	1.5787	2.1726
median	-7.1	-5.95	-3.3	-1.9	0.45	3	4.953	2.6561	3.75
midrange	-2.1795	0.25	0.6	1.5	2.6	4.5	4.85	1.3199	2
mode	-22.7	-17.5	-8.9	2.1	10.075	15.478	24.088	11.038	18.975
mode k.dens	-17.509	-17.003	-15.319	-13.973	8.1863	11.013	11.725	10.553	23.506

Figure 1

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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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Parameters (Session):

par1 = 750 ; par2 = 5 ; par3 = 0 ;

Parameters (R input):

par1 = 750 ; par2 = 5 ; par3 = 0 ;

R code (references can be found in the software module):

par3 <- '0'
par2 <- '5'
par1 <- '200'
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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'P1',header=TRUE)
a<-table.element(a,'P5',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'P95',header=TRUE)
a<-table.element(a,'P99',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
p01 <- quantile(r$t[,1],0.01)[[1]]
p05 <- quantile(r$t[,1],0.05)[[1]]
p95 <- quantile(r$t[,1],0.95)[[1]]
p99 <- quantile(r$t[,1],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,2],0.05)[[1]]
p95 <- quantile(r$t[,2],0.95)[[1]]
p99 <- quantile(r$t[,2],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,3],0.05)[[1]]
p95 <- quantile(r$t[,3],0.95)[[1]]
p99 <- quantile(r$t[,3],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,4],0.05)[[1]]
p95 <- quantile(r$t[,4],0.95)[[1]]
p99 <- quantile(r$t[,4],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,5],0.05)[[1]]
p95 <- quantile(r$t[,5],0.95)[[1]]
p99 <- quantile(r$t[,5],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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')

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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code