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*Unverified author*

R Software Module

rwasp_bootstrapplot.wasp

Title produced by software

Blocked Bootstrap Plot - Central Tendency

Date of computation

Sat, 30 Apr 2016 21:43:18 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Apr/30/t1462049031cewk5q8isebqvf0.htm/, Retrieved Sun, 28 Apr 2024 19:37:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295093, Retrieved Sun, 28 Apr 2024 19:37:50 +0000

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Estimated Impact

163

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

-     [Bootstrap Plot - Central Tendency] [] [2016-04-30 20:24:10] [b645833c54bd2f130859241aaeaa0537]
- R P   [Bootstrap Plot - Central Tendency] [] [2016-04-30 20:32:04] [b645833c54bd2f130859241aaeaa0537]
- RMPD      [Blocked Bootstrap Plot - Central Tendency] [] [2016-04-30 20:43:18] [d992272e6b10691b6ed213356daa79d7] [Current]
- R PD        [Blocked Bootstrap Plot - Central Tendency] [] [2016-04-30 20:45:33] [b645833c54bd2f130859241aaeaa0537] 
-   P           [Blocked Bootstrap Plot - Central Tendency] [] [2016-04-30 20:48:03] [b645833c54bd2f130859241aaeaa0537] 

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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	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295093&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]4 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=295093&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295093&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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Estimation Results of Blocked Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	92.148	93.362	95.185	95.543	96.736	97.96	98.824	1.4566	1.5509
median	90.751	94.81	95.01	95.01	98.56	100.13	100.13	2.2483	3.55
midrange	94.365	94.365	94.365	94.365	94.365	96.363	96.795	0.66178	0
mode	88.311	89.81	95.01	101.86	101.86	101.86	101.86	4.5366	6.85
mode k.dens	88.311	89.81	94.91	95.008	100.13	101.86	101.86	3.8446	5.2199

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 92.148 & 93.362 & 95.185 & 95.543 & 96.736 & 97.96 & 98.824 & 1.4566 & 1.5509 \tabularnewline
median & 90.751 & 94.81 & 95.01 & 95.01 & 98.56 & 100.13 & 100.13 & 2.2483 & 3.55 \tabularnewline
midrange & 94.365 & 94.365 & 94.365 & 94.365 & 94.365 & 96.363 & 96.795 & 0.66178 & 0 \tabularnewline
mode & 88.311 & 89.81 & 95.01 & 101.86 & 101.86 & 101.86 & 101.86 & 4.5366 & 6.85 \tabularnewline
mode k.dens & 88.311 & 89.81 & 94.91 & 95.008 & 100.13 & 101.86 & 101.86 & 3.8446 & 5.2199 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295093&T=1

[TABLE]
[ROW][C]Estimation Results of Blocked 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]92.148[/C][C]93.362[/C][C]95.185[/C][C]95.543[/C][C]96.736[/C][C]97.96[/C][C]98.824[/C][C]1.4566[/C][C]1.5509[/C][/ROW]
[ROW][C]median[/C][C]90.751[/C][C]94.81[/C][C]95.01[/C][C]95.01[/C][C]98.56[/C][C]100.13[/C][C]100.13[/C][C]2.2483[/C][C]3.55[/C][/ROW]
[ROW][C]midrange[/C][C]94.365[/C][C]94.365[/C][C]94.365[/C][C]94.365[/C][C]94.365[/C][C]96.363[/C][C]96.795[/C][C]0.66178[/C][C]0[/C][/ROW]
[ROW][C]mode[/C][C]88.311[/C][C]89.81[/C][C]95.01[/C][C]101.86[/C][C]101.86[/C][C]101.86[/C][C]101.86[/C][C]4.5366[/C][C]6.85[/C][/ROW]
[ROW][C]mode k.dens[/C][C]88.311[/C][C]89.81[/C][C]94.91[/C][C]95.008[/C][C]100.13[/C][C]101.86[/C][C]101.86[/C][C]3.8446[/C][C]5.2199[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295093&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295093&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 Blocked Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	92.148	93.362	95.185	95.543	96.736	97.96	98.824	1.4566	1.5509
median	90.751	94.81	95.01	95.01	98.56	100.13	100.13	2.2483	3.55
midrange	94.365	94.365	94.365	94.365	94.365	96.363	96.795	0.66178	0
mode	88.311	89.81	95.01	101.86	101.86	101.86	101.86	4.5366	6.85
mode k.dens	88.311	89.81	94.91	95.008	100.13	101.86	101.86	3.8446	5.2199

Figure 1

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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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Parameters (Session):

par1 = 50 ; par2 = 12 ; par3 = 5 ; par4 = P1 P5 Q1 Q3 P95 P99 ;

Parameters (R input):

par1 = 50 ; par2 = 12 ; par3 = 5 ; 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)
par3 <- as.numeric(par3)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
if (par2 < 3) par2 = 3
if (par2 > length(x)) par2 = length(x)
library(modeest)
library(lattice)
library(boot)
boot.stat <- function(s)
{
s.mean <- mean(s)
s.median <- median(s)
s.midrange <- (max(s) + min(s)) / 2
s.mode <- mlv(s,method='mfv')$M
s.kernelmode <- mlv(s, method='kernel')$M
c(s.mean, s.median, s.midrange, s.mode, s.kernelmode)
}
(r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))
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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot7a.png')
plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')
grid()
dev.off()
bitmap(file='plot8a.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()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]) )
colnames(z) <- list('mean','median','midrange','mode','mode.k.dens')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked Bootstrap',10,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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$t[,1],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[1],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element( a,signif( sqrt(var(r$t[,1])),par3 ) )
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$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$t[,2],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[2],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,2])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$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$t[,3],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[3],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,3])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode',header=TRUE)
q1 <- quantile(r$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$t[,4],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[4],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,4])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode k.dens',header=TRUE)
q1 <- quantile(r$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$t[,5],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[5],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,5])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

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