FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 06 Jun 2010 21:44:12 +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/2010/Jun/06/t1275860875zj2rpuwdhqfjer7.htm/, Retrieved Sat, 27 Apr 2024 15:55:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=77808, Retrieved Sat, 27 Apr 2024 15:55:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W32
Estimated Impact149

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] [Bootstrap plot me...] [2010-06-06 21:44:12] [0291ee60c135beb64d296f3dc8feb2dc] [Current]
- RMPD    [Classical Decomposition] [Decompositie insc...] [2010-06-06 22:18:59] [e634c4f3b95bc1f95d22c44d630e9c6e]
- RMP     [Classical Decomposition] [Decompositie mega...] [2010-06-06 22:24:33] [e634c4f3b95bc1f95d22c44d630e9c6e]
- RMPD    [Exponential Smoothing] [Exp smoothing meg...] [2010-06-06 22:35:04] [e634c4f3b95bc1f95d22c44d630e9c6e]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
93.2  
96
 95.2   
 77.1
 70.9
 64.8
 70.1
 77.3
 79.5
100.6
100.7
107.1
 95.9
 82.8
 83.3
80
 80.4
 67.5
 75.7
 71.1
 89.3
101.1
105.2
114.1
 96.3
 84.4
 91.2
 81.9
 80.5
 70.4
 74.8
 75.9
 86.3
 98.7
100.9
113.8
 89.8
 84.4
 87.2
 85.6
72
 69.2
 77.5
 78.1
 94.3
 97.7
100.2
116.4
 97.1
93
96
 80.5
 76.1
 69.9
 73.6
 92.6
 94.2
 93.5
108.5
109.4
105.1
 92.5
 97.1
 81.4
 79.1
 72.1
 78.7
 87.1
 91.4
109.9
116.3
113
100
 84.8
 94.3
 87.1
 90.3
 72.4
 84.9
 92.7
 92.2
114.9
112.5
118.3
106
 91.2
 96.6
 96.3
 88.2
 70.2
 86.5
 88.2
102.8
119.1
119.2
125.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=77808&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=77808&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=77808&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean89.946354166666790.919791666666791.78307291666671.378688521375941.83671875000000
median89.391.292.352.028655724388983.05
midrange92.994.9594.951.777894487199742.04999999999998

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 89.9463541666667 & 90.9197916666667 & 91.7830729166667 & 1.37868852137594 & 1.83671875000000 \tabularnewline
median & 89.3 & 91.2 & 92.35 & 2.02865572438898 & 3.05 \tabularnewline
midrange & 92.9 & 94.95 & 94.95 & 1.77789448719974 & 2.04999999999998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=77808&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]89.9463541666667[/C][C]90.9197916666667[/C][C]91.7830729166667[/C][C]1.37868852137594[/C][C]1.83671875000000[/C][/ROW]
[ROW][C]median[/C][C]89.3[/C][C]91.2[/C][C]92.35[/C][C]2.02865572438898[/C][C]3.05[/C][/ROW]
[ROW][C]midrange[/C][C]92.9[/C][C]94.95[/C][C]94.95[/C][C]1.77789448719974[/C][C]2.04999999999998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=77808&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=77808&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
mean89.946354166666790.919791666666791.78307291666671.378688521375941.83671875000000
median89.391.292.352.028655724388983.05
midrange92.994.9594.951.777894487199742.04999999999998


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 12 ;
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')