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R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Mon, 18 Dec 2017 15:07:27 +0100

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/2017/Dec/18/t1513606127pc61qtw6k6uv2yk.htm/, Retrieved Mon, 13 May 2024 23:27:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310186, Retrieved Mon, 13 May 2024 23:27:52 +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] [] [2017-12-18 14:07:27] [f1ade19563a25eb31edff11eb9af1158] [Current]

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Dataseries X:

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Histogram

Boxplots

5,12
2,70
6,16
2,26
1,77
1,47
2,17
1,78
1,75
4,04
1,60
1,60
2,00
4,39
3,47
4,99
2,07
1,45
4,00
2,43
2,11
1,97
4,00
2,64
1,44
3,38
1,41
2,98
4,03
2,50
2,24
2,11
5,08
2,17
2,12
1,78
6,49
5,07
1,95
2,62
2,12
3,02
1,35
1,53
1,35
1,61
2,09
2,29
1,26
1,50
2,07
3,57
1,88
1,56
1,13
4,77
1,52
2,01
5,71
1,54
1,88
1,87
1,76
2,32
1,81
3,63

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	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310186&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310186&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310186&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	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	2.1265	2.3162	2.5009	2.613	2.7037	2.8816	2.9091	0.14927	0.20273
median	1.8796	1.9248	2.08	2.11	2.14	2.2921	2.3753	0.085466	0.06
midrange	3.125	3.19	3.645	3.81	3.8262	3.92	3.95	0.16426	0.18125
mode	1.13	1.35	1.8688	2.12	2.6381	4.0005	5.1268	0.79375	0.76937
mode k.dens	1.518	1.5402	1.7438	1.8777	1.9886	2.0921	2.149	0.15322	0.24479

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P0.5 & P2.5 & Q1 & Estimate & Q3 & P97.5 & P99.5 & S.D. & IQR \tabularnewline
mean & 2.1265 & 2.3162 & 2.5009 & 2.613 & 2.7037 & 2.8816 & 2.9091 & 0.14927 & 0.20273 \tabularnewline
median & 1.8796 & 1.9248 & 2.08 & 2.11 & 2.14 & 2.2921 & 2.3753 & 0.085466 & 0.06 \tabularnewline
midrange & 3.125 & 3.19 & 3.645 & 3.81 & 3.8262 & 3.92 & 3.95 & 0.16426 & 0.18125 \tabularnewline
mode & 1.13 & 1.35 & 1.8688 & 2.12 & 2.6381 & 4.0005 & 5.1268 & 0.79375 & 0.76937 \tabularnewline
mode k.dens & 1.518 & 1.5402 & 1.7438 & 1.8777 & 1.9886 & 2.0921 & 2.149 & 0.15322 & 0.24479 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310186&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P0.5[/C][C]P2.5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P97.5[/C][C]P99.5[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]2.1265[/C][C]2.3162[/C][C]2.5009[/C][C]2.613[/C][C]2.7037[/C][C]2.8816[/C][C]2.9091[/C][C]0.14927[/C][C]0.20273[/C][/ROW]
[ROW][C]median[/C][C]1.8796[/C][C]1.9248[/C][C]2.08[/C][C]2.11[/C][C]2.14[/C][C]2.2921[/C][C]2.3753[/C][C]0.085466[/C][C]0.06[/C][/ROW]
[ROW][C]midrange[/C][C]3.125[/C][C]3.19[/C][C]3.645[/C][C]3.81[/C][C]3.8262[/C][C]3.92[/C][C]3.95[/C][C]0.16426[/C][C]0.18125[/C][/ROW]
[ROW][C]mode[/C][C]1.13[/C][C]1.35[/C][C]1.8688[/C][C]2.12[/C][C]2.6381[/C][C]4.0005[/C][C]5.1268[/C][C]0.79375[/C][C]0.76937[/C][/ROW]
[ROW][C]mode k.dens[/C][C]1.518[/C][C]1.5402[/C][C]1.7438[/C][C]1.8777[/C][C]1.9886[/C][C]2.0921[/C][C]2.149[/C][C]0.15322[/C][C]0.24479[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310186&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310186&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	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	2.1265	2.3162	2.5009	2.613	2.7037	2.8816	2.9091	0.14927	0.20273
median	1.8796	1.9248	2.08	2.11	2.14	2.2921	2.3753	0.085466	0.06
midrange	3.125	3.19	3.645	3.81	3.8262	3.92	3.95	0.16426	0.18125
mode	1.13	1.35	1.8688	2.12	2.6381	4.0005	5.1268	0.79375	0.76937
mode k.dens	1.518	1.5402	1.7438	1.8777	1.9886	2.0921	2.149	0.15322	0.24479

Figure 1

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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 ; par4 = P0.5 P2.5 Q1 Q3 P97.5 P99.5 ;

Parameters (R input):

par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P0.5 P2.5 Q1 Q3 P97.5 P99.5 ;

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)
}
x<-na.omit(x)
(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')

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