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Author

*The author of this computation has been verified*

R Software Module

rwasp_bootstrapplot.wasp

Title produced by software

Blocked Bootstrap Plot - Central Tendency

Date of computation

Fri, 11 Dec 2015 14:54:35 +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/11/t1449845713t12g7zox7u539ml.htm/, Retrieved Thu, 16 May 2024 19:11:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285969, Retrieved Thu, 16 May 2024 19:11:59 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

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

-       [Blocked Bootstrap Plot - Central Tendency] [Blocked Bootstrap...] [2015-12-11 14:54:35] [4bfd98e85da8d65970e2f1f883f9da72] [Current]

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Dataseries X:

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-0.000830616394377666
-0.00217087736686087
-0.00074420026186913
0.000866119841126281
0.00110874338598884
-0.00331026578193515
-0.00351361885042004
-0.00153118277282079
-0.0015144884022786
0.00128356993671869
0.00149155950956391
-0.00384596939991697
-0.00500439657674154
0.000907992107513854
-0.00276740950533674
0.00194123854071811
-0.00727148922439195
0.00446821195533995
0.0025675129169935
0.00111852907130615
0.000177657731460723
-0.0023080927267596
-0.00441769167207709
0.00162132463434916
-0.00049312198322883
0.000423749128236736
0.00511269751267707
0.00234072362027584
-0.000725515857422071
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0.00210677907882325
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0.00270728256038345
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0.00411699520424155
-0.002112949845863
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0.0044677884107055
0.00273416738878983
0.000797867790875228
0.00159034082068879
-0.00019429421298422
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0.0031536832361225
0.00142341371089048
0.00773690474936147
-0.00122796502113955
-0.000890201308304278
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-0.00195899271811672
-0.00308854990361402
0.00173856199021156
0.000489803948920651
-7.72593594471491e-05
-0.0014929759440083
0.000355095235735099
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0.000147208797153351
1.32222078370964e-05
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0.000503575269585197
0.000851149357146722

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285969&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]7 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=285969&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285969&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	7 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	-0.00043527	-0.00028663	-0.00010893	-5.6411e-07	0.0001279	0.00028686	0.00043683	0.00018375	0.00023684
median	-0.00046342	-0.00042367	-7.7259e-05	-3.1904e-06	0.00017094	0.00035111	0.00047175	0.00024089	0.0002482
midrange	-0.0014016	-0.0010794	4.5911e-06	0.00023271	0.00031513	0.0016109	0.0019991	0.00092331	0.00031054
mode	-0.0030908	-0.0019098	-0.00046797	-5.6411e-07	0.00050907	0.0022401	0.0030563	0.0011575	0.00097705
mode k.dens	-0.00067246	-0.00056747	-0.00032953	-9.2152e-05	0.0002963	0.00074027	0.0010775	0.00041378	0.00062583

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.00043527 & -0.00028663 & -0.00010893 & -5.6411e-07 & 0.0001279 & 0.00028686 & 0.00043683 & 0.00018375 & 0.00023684 \tabularnewline
median & -0.00046342 & -0.00042367 & -7.7259e-05 & -3.1904e-06 & 0.00017094 & 0.00035111 & 0.00047175 & 0.00024089 & 0.0002482 \tabularnewline
midrange & -0.0014016 & -0.0010794 & 4.5911e-06 & 0.00023271 & 0.00031513 & 0.0016109 & 0.0019991 & 0.00092331 & 0.00031054 \tabularnewline
mode & -0.0030908 & -0.0019098 & -0.00046797 & -5.6411e-07 & 0.00050907 & 0.0022401 & 0.0030563 & 0.0011575 & 0.00097705 \tabularnewline
mode k.dens & -0.00067246 & -0.00056747 & -0.00032953 & -9.2152e-05 & 0.0002963 & 0.00074027 & 0.0010775 & 0.00041378 & 0.00062583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285969&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]-0.00043527[/C][C]-0.00028663[/C][C]-0.00010893[/C][C]-5.6411e-07[/C][C]0.0001279[/C][C]0.00028686[/C][C]0.00043683[/C][C]0.00018375[/C][C]0.00023684[/C][/ROW]
[ROW][C]median[/C][C]-0.00046342[/C][C]-0.00042367[/C][C]-7.7259e-05[/C][C]-3.1904e-06[/C][C]0.00017094[/C][C]0.00035111[/C][C]0.00047175[/C][C]0.00024089[/C][C]0.0002482[/C][/ROW]
[ROW][C]midrange[/C][C]-0.0014016[/C][C]-0.0010794[/C][C]4.5911e-06[/C][C]0.00023271[/C][C]0.00031513[/C][C]0.0016109[/C][C]0.0019991[/C][C]0.00092331[/C][C]0.00031054[/C][/ROW]
[ROW][C]mode[/C][C]-0.0030908[/C][C]-0.0019098[/C][C]-0.00046797[/C][C]-5.6411e-07[/C][C]0.00050907[/C][C]0.0022401[/C][C]0.0030563[/C][C]0.0011575[/C][C]0.00097705[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.00067246[/C][C]-0.00056747[/C][C]-0.00032953[/C][C]-9.2152e-05[/C][C]0.0002963[/C][C]0.00074027[/C][C]0.0010775[/C][C]0.00041378[/C][C]0.00062583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285969&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285969&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	-0.00043527	-0.00028663	-0.00010893	-5.6411e-07	0.0001279	0.00028686	0.00043683	0.00018375	0.00023684
median	-0.00046342	-0.00042367	-7.7259e-05	-3.1904e-06	0.00017094	0.00035111	0.00047175	0.00024089	0.0002482
midrange	-0.0014016	-0.0010794	4.5911e-06	0.00023271	0.00031513	0.0016109	0.0019991	0.00092331	0.00031054
mode	-0.0030908	-0.0019098	-0.00046797	-5.6411e-07	0.00050907	0.0022401	0.0030563	0.0011575	0.00097705
mode k.dens	-0.00067246	-0.00056747	-0.00032953	-9.2152e-05	0.0002963	0.00074027	0.0010775	0.00041378	0.00062583

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 = 500 ; par2 = 12 ; par3 = 5 ; par4 = P1 P5 Q1 Q3 P95 P99 ;

Parameters (R input):

par1 = 500 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code