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Author

*Unverified author*

R Software Module

rwasp_bootstrapplot.wasp

Title produced by software

Blocked Bootstrap Plot - Central Tendency

Date of computation

Sun, 06 Dec 2015 14:17:53 +0000

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/2015/Dec/06/t1449411497mfe7cxkbyek5tn6.htm/, Retrieved Thu, 16 May 2024 15:36:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285284, Retrieved Thu, 16 May 2024 15:36:13 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

105

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

-       [Blocked Bootstrap Plot - Central Tendency] [] [2015-12-06 14:17:53] [9de61432ca342460988ae3c030b81fa6] [Current]

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Dataseries X:

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Histogram

Boxplots

-12
-12
-8
-6
-2
4
3
5
8
5
3
6
15
12
11
12
14
18
15
16
-1
-5
-6
-5
-2
-9
-9
-12
-16
-19
-30
-26
-22
-31
-33
-31
-27
-29
-33
-27
-22
-23
-23
-15
-15
-24
-18
-14
-7
-12
-12
-15
-16
-17
-13
-8
-13
-13
-11
-16
-34
-35
-38
-32
-37
-39
-31
-30
-29
-36
-41
-42
-33
-43
-41
-34
-32
-36
-37
-30
-32
-30
-21
-19
-6
-11
-11

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	'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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285284&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285284&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Estimation Results of Blocked Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-25.829	-24.784	-19.724	-16.241	-14.422	-5.4483	-2.8949	5.3997	5.3017
median	-30	-29	-22	-16	-13	-6.9	1.04	7.1995	9
midrange	-23.775	-22.275	-14	-12.5	-12.125	-9.725	-7.5	3.678	1.875
mode	-33	-31	-26.75	-12	-12	12	13.53	12.694	14.75
mode k.dens	-34.325	-32.728	-31.706	-12.514	-12.54	5.7301	12.027	13.097	19.166

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -25.829 & -24.784 & -19.724 & -16.241 & -14.422 & -5.4483 & -2.8949 & 5.3997 & 5.3017 \tabularnewline
median & -30 & -29 & -22 & -16 & -13 & -6.9 & 1.04 & 7.1995 & 9 \tabularnewline
midrange & -23.775 & -22.275 & -14 & -12.5 & -12.125 & -9.725 & -7.5 & 3.678 & 1.875 \tabularnewline
mode & -33 & -31 & -26.75 & -12 & -12 & 12 & 13.53 & 12.694 & 14.75 \tabularnewline
mode k.dens & -34.325 & -32.728 & -31.706 & -12.514 & -12.54 & 5.7301 & 12.027 & 13.097 & 19.166 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285284&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]-25.829[/C][C]-24.784[/C][C]-19.724[/C][C]-16.241[/C][C]-14.422[/C][C]-5.4483[/C][C]-2.8949[/C][C]5.3997[/C][C]5.3017[/C][/ROW]
[ROW][C]median[/C][C]-30[/C][C]-29[/C][C]-22[/C][C]-16[/C][C]-13[/C][C]-6.9[/C][C]1.04[/C][C]7.1995[/C][C]9[/C][/ROW]
[ROW][C]midrange[/C][C]-23.775[/C][C]-22.275[/C][C]-14[/C][C]-12.5[/C][C]-12.125[/C][C]-9.725[/C][C]-7.5[/C][C]3.678[/C][C]1.875[/C][/ROW]
[ROW][C]mode[/C][C]-33[/C][C]-31[/C][C]-26.75[/C][C]-12[/C][C]-12[/C][C]12[/C][C]13.53[/C][C]12.694[/C][C]14.75[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-34.325[/C][C]-32.728[/C][C]-31.706[/C][C]-12.514[/C][C]-12.54[/C][C]5.7301[/C][C]12.027[/C][C]13.097[/C][C]19.166[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285284&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	-25.829	-24.784	-19.724	-16.241	-14.422	-5.4483	-2.8949	5.3997	5.3017
median	-30	-29	-22	-16	-13	-6.9	1.04	7.1995	9
midrange	-23.775	-22.275	-14	-12.5	-12.125	-9.725	-7.5	3.678	1.875
mode	-33	-31	-26.75	-12	-12	12	13.53	12.694	14.75
mode k.dens	-34.325	-32.728	-31.706	-12.514	-12.54	5.7301	12.027	13.097	19.166

Figure 1

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Figure 2

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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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