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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, 13 Nov 2015 10:00:12 +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/Nov/13/t144740882126d0ryro828v0x3.htm/, Retrieved Mon, 13 May 2024 21:31:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=283298, Retrieved Mon, 13 May 2024 21:31:32 +0000

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No (this computation is public)

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Estimated Impact

181

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-       [Blocked Bootstrap Plot - Central Tendency] [] [2015-11-13 10:00:12] [85e7a66a1e5d24b56c3cf5eab9332807] [Current]

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Histogram

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0.8917508627
0.9466125974
0.3330472133
0.329009174
0.2944940729
0.2066545576
0.8952915323
0.3471107929
0.1590303231
0.0848553798
0.3990748112
0.1643407019
0.192492134
0.3403910374
0.1262846201
0.0160076222
0.8250212908
0.9591692924
0.7931942109
0.0278372176
0.071224785
0.0736861517
0.0070384751
0.9801961348
0.1400405354
0.8396278056
0.0348253732
0.9925011027
0.721255636
0.3043213937
0.2633732126
0.7945068992
0.2146850244
0.4722570716
0.8077682322
0.8167580479
0.3716380065
0.9469035515
0.753014425
0.6933234064
0.1902105464
0.6602295103
0.1704258616
0.4610404953
0.1432012816
0.5835801756
0.5467009318
0.6244801166
0.427090408
0.922880485
0.0869950073
0.8920771858
0.6259975668
0.9166064187
0.1267146372
0.5277887083
0.804000869
0.5355470513
0.5925073475
0.9719281722
0.9388391672
0.1680017126
0.7721014544
0.1837820516
0.4463219269
0.2027634804
0.0778829202
0.3791065545
0.9249470635
0.5359394881
0.691190389
0.6900809256
0.42245374
0.1092649963
0.4144668292
0.925568188
0.5598180862
0.5216958218
0.7862405865
0.8778600788
0.0686247982
0.1787173937
0.6629335642
0.2449214953
0.4209460679
0.3977272652
0.7003618833
0.2128313702
0.3213962752
0.1436866843
0.3599294476
0.9891126366
0.9227066891
0.636986283
0.5500912131
0.5615158016
0.993768822
0.4171548573
0.011778003
0.0872080887

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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283298&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283298&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283298&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	'Gertrude Mary Cox' @ cox.wessa.net

Estimation Results of Blocked Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	0.41237	0.43764	0.47221	0.48951	0.50873	0.53905	0.54998	0.029757	0.036522
median	0.34035	0.3532	0.41846	0.45368	0.52953	0.54848	0.56068	0.06305	0.11107
midrange	0.48759	0.49397	0.49977	0.5004	0.50277	0.50489	0.51436	0.0045894	0.0030036
mode	0.077816	0.20276	0.41582	0.48951	0.5742	0.81717	0.98291	0.16955	0.15837
mode k.dens	0.078377	0.099023	0.13939	0.14939	0.57517	0.92687	0.93842	0.30161	0.43578

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 0.41237 & 0.43764 & 0.47221 & 0.48951 & 0.50873 & 0.53905 & 0.54998 & 0.029757 & 0.036522 \tabularnewline
median & 0.34035 & 0.3532 & 0.41846 & 0.45368 & 0.52953 & 0.54848 & 0.56068 & 0.06305 & 0.11107 \tabularnewline
midrange & 0.48759 & 0.49397 & 0.49977 & 0.5004 & 0.50277 & 0.50489 & 0.51436 & 0.0045894 & 0.0030036 \tabularnewline
mode & 0.077816 & 0.20276 & 0.41582 & 0.48951 & 0.5742 & 0.81717 & 0.98291 & 0.16955 & 0.15837 \tabularnewline
mode k.dens & 0.078377 & 0.099023 & 0.13939 & 0.14939 & 0.57517 & 0.92687 & 0.93842 & 0.30161 & 0.43578 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283298&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]0.41237[/C][C]0.43764[/C][C]0.47221[/C][C]0.48951[/C][C]0.50873[/C][C]0.53905[/C][C]0.54998[/C][C]0.029757[/C][C]0.036522[/C][/ROW]
[ROW][C]median[/C][C]0.34035[/C][C]0.3532[/C][C]0.41846[/C][C]0.45368[/C][C]0.52953[/C][C]0.54848[/C][C]0.56068[/C][C]0.06305[/C][C]0.11107[/C][/ROW]
[ROW][C]midrange[/C][C]0.48759[/C][C]0.49397[/C][C]0.49977[/C][C]0.5004[/C][C]0.50277[/C][C]0.50489[/C][C]0.51436[/C][C]0.0045894[/C][C]0.0030036[/C][/ROW]
[ROW][C]mode[/C][C]0.077816[/C][C]0.20276[/C][C]0.41582[/C][C]0.48951[/C][C]0.5742[/C][C]0.81717[/C][C]0.98291[/C][C]0.16955[/C][C]0.15837[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.078377[/C][C]0.099023[/C][C]0.13939[/C][C]0.14939[/C][C]0.57517[/C][C]0.92687[/C][C]0.93842[/C][C]0.30161[/C][C]0.43578[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283298&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283298&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	0.41237	0.43764	0.47221	0.48951	0.50873	0.53905	0.54998	0.029757	0.036522
median	0.34035	0.3532	0.41846	0.45368	0.52953	0.54848	0.56068	0.06305	0.11107
midrange	0.48759	0.49397	0.49977	0.5004	0.50277	0.50489	0.51436	0.0045894	0.0030036
mode	0.077816	0.20276	0.41582	0.48951	0.5742	0.81717	0.98291	0.16955	0.15837
mode k.dens	0.078377	0.099023	0.13939	0.14939	0.57517	0.92687	0.93842	0.30161	0.43578

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 = 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):

par4 <- 'P1 P5 Q1 Q3 P95 P99'
par3 <- '5'
par2 <- '12'
par1 <- '500'
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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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code