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*Unverified author*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Wed, 22 May 2013 11:14:32 -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/22/t136923576960rqpn822ezu8s1.htm/, Retrieved Sat, 27 Apr 2024 20:01:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=210226, Retrieved Sat, 27 Apr 2024 20:01:05 +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] [Bootstrap eigen r...] [2013-05-22 15:14:32] [65a89fc2585e1033f9f51d37a51d872c] [Current]

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Histogram

Boxplots

-1
-2
-5
-4
-6
-2
-2
-2
-2
2
1
-8
-1
1
-1
2
2
1
-1
-2
-2
-1
-8
-4
-6
-3
-3
-7
-9
-11
-13
-11
-9
-17
-22
-25
-20
-24
-24
-22
-19
-18
-17
-11
-11
-12
-10
-15
-15
-15
-13
-8
-13
-9
-7
-4
-4
-2
0
-2
-3
1
-2
-1
1
-3
-4
-9
-9
-7
-14
-12
-16
-20
-12
-12
-10
-10
-13
-16
-14
-17
-24
-25

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210226&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]9 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=210226&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210226&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	9 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-10.7	-10.176	-9.4315	-8.6548	-8.2887	-7.3982	-6.6177	0.92034	1.1429
median	-11	-10.275	-9	-8	-7.125	-6	-4	1.4386	1.875
midrange	-12	-11.775	-11.5	-11.5	-11.5	-11	-10.49	0.29692	0
mode	-12.02	-10.55	-3.75	-2	-2	-0.725	1	3.3792	1.75
mode k.dens	-12.154	-10.867	-2.8039	-1.9625	-1.7042	-0.98138	-0.67896	3.4866	1.0998

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -10.7 & -10.176 & -9.4315 & -8.6548 & -8.2887 & -7.3982 & -6.6177 & 0.92034 & 1.1429 \tabularnewline
median & -11 & -10.275 & -9 & -8 & -7.125 & -6 & -4 & 1.4386 & 1.875 \tabularnewline
midrange & -12 & -11.775 & -11.5 & -11.5 & -11.5 & -11 & -10.49 & 0.29692 & 0 \tabularnewline
mode & -12.02 & -10.55 & -3.75 & -2 & -2 & -0.725 & 1 & 3.3792 & 1.75 \tabularnewline
mode k.dens & -12.154 & -10.867 & -2.8039 & -1.9625 & -1.7042 & -0.98138 & -0.67896 & 3.4866 & 1.0998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210226&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]-10.7[/C][C]-10.176[/C][C]-9.4315[/C][C]-8.6548[/C][C]-8.2887[/C][C]-7.3982[/C][C]-6.6177[/C][C]0.92034[/C][C]1.1429[/C][/ROW]
[ROW][C]median[/C][C]-11[/C][C]-10.275[/C][C]-9[/C][C]-8[/C][C]-7.125[/C][C]-6[/C][C]-4[/C][C]1.4386[/C][C]1.875[/C][/ROW]
[ROW][C]midrange[/C][C]-12[/C][C]-11.775[/C][C]-11.5[/C][C]-11.5[/C][C]-11.5[/C][C]-11[/C][C]-10.49[/C][C]0.29692[/C][C]0[/C][/ROW]
[ROW][C]mode[/C][C]-12.02[/C][C]-10.55[/C][C]-3.75[/C][C]-2[/C][C]-2[/C][C]-0.725[/C][C]1[/C][C]3.3792[/C][C]1.75[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-12.154[/C][C]-10.867[/C][C]-2.8039[/C][C]-1.9625[/C][C]-1.7042[/C][C]-0.98138[/C][C]-0.67896[/C][C]3.4866[/C][C]1.0998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210226&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210226&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	-10.7	-10.176	-9.4315	-8.6548	-8.2887	-7.3982	-6.6177	0.92034	1.1429
median	-11	-10.275	-9	-8	-7.125	-6	-4	1.4386	1.875
midrange	-12	-11.775	-11.5	-11.5	-11.5	-11	-10.49	0.29692	0
mode	-12.02	-10.55	-3.75	-2	-2	-0.725	1	3.3792	1.75
mode k.dens	-12.154	-10.867	-2.8039	-1.9625	-1.7042	-0.98138	-0.67896	3.4866	1.0998

Figure 1

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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 = 50 ; par2 = 5 ; par3 = 0 ;

Parameters (R input):

par1 = 50 ; par2 = 5 ; par3 = 0 ;

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

par3 <- '0'
par2 <- '5'
par1 <- '50'
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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