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*Unverified author*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Tue, 14 May 2013 03:02:46 -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/14/t1368514999492zxjpy67mchun.htm/, Retrieved Thu, 02 May 2024 06:00:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=208938, Retrieved Thu, 02 May 2024 06:00:58 +0000

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Estimated Impact

138

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

-       [Bootstrap Plot - Central Tendency] [test with 95% and...] [2013-05-14 07:02:46] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208938&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]11 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=208938&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208938&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	11 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	-0.13445	0.156	0.62347	0.99448	1.2871	1.6881	2.0019	0.48272	0.66365
median	-0.99282	-0.7629	0.74468	0.75514	1.3686	1.4282	2.9421	0.68309	0.62396
midrange	0.21737	0.21767	0.97464	1.2373	1.3522	1.7337	2.106	0.3748	0.37758
mode	-0.99282	-0.99282	-0.003878	0.99448	1.4419	3.4673	3.4673	1.2343	1.4457
mode k.dens	-0.96998	-0.90519	1.4995e-05	0.99035	1.278	3.0454	3.4637	1.0793	1.278

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.13445 & 0.156 & 0.62347 & 0.99448 & 1.2871 & 1.6881 & 2.0019 & 0.48272 & 0.66365 \tabularnewline
median & -0.99282 & -0.7629 & 0.74468 & 0.75514 & 1.3686 & 1.4282 & 2.9421 & 0.68309 & 0.62396 \tabularnewline
midrange & 0.21737 & 0.21767 & 0.97464 & 1.2373 & 1.3522 & 1.7337 & 2.106 & 0.3748 & 0.37758 \tabularnewline
mode & -0.99282 & -0.99282 & -0.003878 & 0.99448 & 1.4419 & 3.4673 & 3.4673 & 1.2343 & 1.4457 \tabularnewline
mode k.dens & -0.96998 & -0.90519 & 1.4995e-05 & 0.99035 & 1.278 & 3.0454 & 3.4637 & 1.0793 & 1.278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208938&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]-0.13445[/C][C]0.156[/C][C]0.62347[/C][C]0.99448[/C][C]1.2871[/C][C]1.6881[/C][C]2.0019[/C][C]0.48272[/C][C]0.66365[/C][/ROW]
[ROW][C]median[/C][C]-0.99282[/C][C]-0.7629[/C][C]0.74468[/C][C]0.75514[/C][C]1.3686[/C][C]1.4282[/C][C]2.9421[/C][C]0.68309[/C][C]0.62396[/C][/ROW]
[ROW][C]midrange[/C][C]0.21737[/C][C]0.21767[/C][C]0.97464[/C][C]1.2373[/C][C]1.3522[/C][C]1.7337[/C][C]2.106[/C][C]0.3748[/C][C]0.37758[/C][/ROW]
[ROW][C]mode[/C][C]-0.99282[/C][C]-0.99282[/C][C]-0.003878[/C][C]0.99448[/C][C]1.4419[/C][C]3.4673[/C][C]3.4673[/C][C]1.2343[/C][C]1.4457[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.96998[/C][C]-0.90519[/C][C]1.4995e-05[/C][C]0.99035[/C][C]1.278[/C][C]3.0454[/C][C]3.4637[/C][C]1.0793[/C][C]1.278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208938&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208938&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	-0.13445	0.156	0.62347	0.99448	1.2871	1.6881	2.0019	0.48272	0.66365
median	-0.99282	-0.7629	0.74468	0.75514	1.3686	1.4282	2.9421	0.68309	0.62396
midrange	0.21737	0.21767	0.97464	1.2373	1.3522	1.7337	2.106	0.3748	0.37758
mode	-0.99282	-0.99282	-0.003878	0.99448	1.4419	3.4673	3.4673	1.2343	1.4457
mode k.dens	-0.96998	-0.90519	1.4995e-05	0.99035	1.278	3.0454	3.4637	1.0793	1.278

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 = 200 ; par2 = 5 ; par3 = 0 ;

Parameters (R input):

par1 = 200 ; par2 = 5 ; par3 = 0 ;

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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'P1',header=TRUE)
a<-table.element(a,'P5',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'P95',header=TRUE)
a<-table.element(a,'P99',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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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Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code