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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationWed, 12 Nov 2014 10:40:45 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/12/t1415789000ze9mpruq6ymfej2.htm/, Retrieved Sun, 19 May 2024 14:15:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253757, Retrieved Sun, 19 May 2024 14:15:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Blocked Bootstrap Plot - Central Tendency] [] [2014-11-12 10:40:45] [6993448de96b8662e47595bfdf466bf3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0.488399
0.255808
-0.186125
0.320883
0.393149
-0.123977
-0.454936
-0.163254
-0.234585
0.315784
0.0130534
-0.223887
0.370063
-0.360923
-0.15303
-0.175245
0.0398728
-0.164754
0.144993
-0.149785
0.124197
-0.32803
0.348919
-0.0989053
-0.147267
-0.440958
-0.408703
-0.251195
0.319684
-0.400873
0.288353
0.307179
0.60582
-0.219987
0.103435
-0.13759
0.315228
0.543727
0.0632975
0.058023
-0.203453
0.330273
-0.151718
0.0121755
-0.43381
0.105633
-0.149379
0.0681825
-0.235978
-0.710379
0.0986947
0.0883681
0.370829
-0.44999
0.488395
-0.484624
0.128524
0.0846028
0.350228
-0.386669
-0.411451
0.14764
-0.418541
0.300747
0.101746
0.147228
-0.180858
0.267838
0.308677
0.474163
0.313293
0.0464136
-0.469437
-0.188928
-0.216832
0.310743
-0.200895
-0.438967
0.0557948
0.213303
-0.435254
0.0533301
0.228512
-0.197451
0.207736
-0.482107
-0.419238
-0.488864
-0.194186
0.0629988
-0.434371
-0.454393
0.285177
0.272406
0.0710038
-0.177558
0.238798
0.0237009
-0.420761
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0.32632
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0.596711
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0.345666
0.598968
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0.0655658
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0.342298
0.0306006
0.0527948
0.311461
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0.0340914
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0.0638229
0.25075
0.222702
0.536084
-0.210141
0.332732
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0.352234
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0.0985749
0.0390797
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0.538648
0.548975
0.573405
0.273745
-0.179567
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0.0683372
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0.515036
0.302734
0.29515
0.308334
0.0599758
-0.435403
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0.248282
-0.212131
-0.533668
-0.2594
-0.312867
-0.0949919
-0.0401132
-0.0288831
-0.0202405

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253757&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]3 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=253757&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253757&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean-0.01693786803797474.63759493670894e-050.01799304208860760.02662614196493540.0349309101265823
median-0.00370328750.036585550.054562450.04655114769793310.0582657375
midrange-0.0522795-0.05227950.0360760.04775850230291110.0883555

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & -0.0169378680379747 & 4.63759493670894e-05 & 0.0179930420886076 & 0.0266261419649354 & 0.0349309101265823 \tabularnewline
median & -0.0037032875 & 0.03658555 & 0.05456245 & 0.0465511476979331 & 0.0582657375 \tabularnewline
midrange & -0.0522795 & -0.0522795 & 0.036076 & 0.0477585023029111 & 0.0883555 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253757&T=1

[TABLE]
[ROW][C]Estimation Results of Blocked Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]-0.0169378680379747[/C][C]4.63759493670894e-05[/C][C]0.0179930420886076[/C][C]0.0266261419649354[/C][C]0.0349309101265823[/C][/ROW]
[ROW][C]median[/C][C]-0.0037032875[/C][C]0.03658555[/C][C]0.05456245[/C][C]0.0465511476979331[/C][C]0.0582657375[/C][/ROW]
[ROW][C]midrange[/C][C]-0.0522795[/C][C]-0.0522795[/C][C]0.036076[/C][C]0.0477585023029111[/C][C]0.0883555[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253757&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253757&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean-0.01693786803797474.63759493670894e-050.01799304208860760.02662614196493540.0349309101265823
median-0.00370328750.036585550.054562450.04655114769793310.0582657375
midrange-0.0522795-0.05227950.0360760.04775850230291110.0883555


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = 500 ; par2 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
if (par2 < 3) par2 = 3
if (par2 > length(x)) par2 = length(x)
library(lattice)
library(boot)
boot.stat <- function(s)
{
s.mean <- mean(s)
s.median <- median(s)
s.midrange <- (max(s) + min(s)) / 2
c(s.mean, s.median, s.midrange)
}
(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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot4.png')
densityplot(~r$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$t[,3]))
colnames(z) <- list('mean','median','midrange')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[1])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,1])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$t[,2],0.25)[[1]]
q3 <- quantile(r$t[,2],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[2])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,2])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$t[,3],0.25)[[1]]
q3 <- quantile(r$t[,3],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[3])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,3])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')