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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationThu, 14 May 2009 12:44:53 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/May/14/t1242326711xmzgud9bj2hkyik.htm/, Retrieved Sun, 28 Apr 2024 21:20:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=40090, Retrieved Sun, 28 Apr 2024 21:20:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Mean Plot] [] [2009-05-07 16:38:34] [595bbfb6ab1e20d51262e8b831f4c453]
- RMPD  [(Partial) Autocorrelation Function] [] [2009-05-14 18:27:23] [96b01d8cb0304fe86f721affdc70b94f]
- RMP     [Bootstrap Plot - Central Tendency] [] [2009-05-14 18:33:11] [96b01d8cb0304fe86f721affdc70b94f]
-           [Bootstrap Plot - Central Tendency] [] [2009-05-14 18:35:30] [96b01d8cb0304fe86f721affdc70b94f]
- RMPD          [Blocked Bootstrap Plot - Central Tendency] [] [2009-05-14 18:44:53] [5ece983fa688b54e830000b964b580e8] [Current]
-   PD            [Blocked Bootstrap Plot - Central Tendency] [] [2009-05-14 18:47:09] [96b01d8cb0304fe86f721affdc70b94f]
-   P               [Blocked Bootstrap Plot - Central Tendency] [] [2009-05-14 18:49:15] [96b01d8cb0304fe86f721affdc70b94f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3851,3
3851,8
3854,1
3858,4
3861,6
3856,3
3855,8
3860,4
3855,1
3839,5
3833
3833,6
3826,8
3818,2
3811,4
3806,8
3810,3
3818,2
3858,9
3867,8
3872,3
3873,3
3876,7
3882,6
3883,5
3882,2
3888,1
3893,7
3901,9
3914,3
3930,3
3948,3
3971,5
3990,1
3993
3998
4015,8
4041,2
4060,7
4076,7
4103
4125,3
4139,7
4146,7
4158
4155,1
4144,8
4148,2
4142,5
4142,1
4145,4
4146,3
4143,5
4149,2
4158,9
4166,1
4179,1
4194,4
4211,7
4226,3
4235,8
4243,6
4258,7
4278,2
4298
4315,1
4334,3
4356
4374
4395,5
4417,8
4432,8
4446,3

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40090&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40090&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40090&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean4007.679452054794044.327397260274074.0883561643849.409929084280466.4089041095895
median3930.34015.84136.1106.596964079754205.799999999999
midrange4083.654126.554126.5545.127061307433942.9000000000001

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 4007.67945205479 & 4044.32739726027 & 4074.08835616438 & 49.4099290842804 & 66.4089041095895 \tabularnewline
median & 3930.3 & 4015.8 & 4136.1 & 106.596964079754 & 205.799999999999 \tabularnewline
midrange & 4083.65 & 4126.55 & 4126.55 & 45.1270613074339 & 42.9000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40090&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]4007.67945205479[/C][C]4044.32739726027[/C][C]4074.08835616438[/C][C]49.4099290842804[/C][C]66.4089041095895[/C][/ROW]
[ROW][C]median[/C][C]3930.3[/C][C]4015.8[/C][C]4136.1[/C][C]106.596964079754[/C][C]205.799999999999[/C][/ROW]
[ROW][C]midrange[/C][C]4083.65[/C][C]4126.55[/C][C]4126.55[/C][C]45.1270613074339[/C][C]42.9000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40090&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40090&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
mean4007.679452054794044.327397260274074.0883561643849.409929084280466.4089041095895
median3930.34015.84136.1106.596964079754205.799999999999
midrange4083.654126.554126.5545.127061307433942.9000000000001


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 12 ;
Parameters (R input):
par1 = 50 ; 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')