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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationThu, 27 Dec 2012 15:12:14 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/27/t13566391569sr6z9scjsx6xff.htm/, Retrieved Thu, 31 Oct 2024 22:47:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204785, Retrieved Thu, 31 Oct 2024 22:47:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Autocorrelatie In...] [2012-11-12 10:59:56] [41982c7b3984978a38ca838fef047984]
- RMPD    [Bootstrap Plot - Central Tendency] [Bootstrap Plot (m...] [2012-12-27 20:12:14] [97ff841fcf87514e420f2e9629cfd808] [Current]
- R P       [Bootstrap Plot - Central Tendency] [Bootstrap Plot (m...] [2012-12-27 20:14:03] [41982c7b3984978a38ca838fef047984]
-   P         [Bootstrap Plot - Central Tendency] [Bootstrap Plot (m...] [2012-12-27 20:16:33] [41982c7b3984978a38ca838fef047984]
- RMPD        [Blocked Bootstrap Plot - Central Tendency] [Bootstrap Plot (g...] [2012-12-27 20:38:33] [41982c7b3984978a38ca838fef047984]
- R  D          [Blocked Bootstrap Plot - Central Tendency] [Bootstrap Plot (g...] [2012-12-27 20:42:04] [41982c7b3984978a38ca838fef047984]
-                 [Blocked Bootstrap Plot - Central Tendency] [Bootstrap Plot (g...] [2012-12-27 20:44:01] [41982c7b3984978a38ca838fef047984]
- RM D            [Variability] [Spreidingsmaten S...] [2012-12-27 20:59:10] [41982c7b3984978a38ca838fef047984]
- RM D            [Standard Deviation Plot] [Spreidingsgrafiek...] [2012-12-27 21:03:45] [41982c7b3984978a38ca838fef047984]
- RM D            [Standard Deviation-Mean Plot] [Spreidings- en ge...] [2012-12-27 21:11:16] [41982c7b3984978a38ca838fef047984]
- RM D            [Variability] [Spreidingsmaten g...] [2012-12-27 21:17:49] [41982c7b3984978a38ca838fef047984]
- RM              [Standard Deviation Plot] [Spreidingsgrafiek...] [2012-12-27 21:20:17] [41982c7b3984978a38ca838fef047984]
- RM              [Standard Deviation-Mean Plot] [Spreidings- en ge...] [2012-12-27 21:24:21] [41982c7b3984978a38ca838fef047984]
- RM D            [Classical Decomposition] [Decompositie Insc...] [2012-12-27 21:30:41] [41982c7b3984978a38ca838fef047984]
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Dataset

Dataseries X:
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Histogram
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204785&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]9 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=204785&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204785&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Estimation Results of Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean23.00423.16623.3270.226050.32306
median222224.51.31122.5
midrange27.535353.53097.5
mode25252500
mode k.dens232523.1032.49660.10278

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 23.004 & 23.166 & 23.327 & 0.22605 & 0.32306 \tabularnewline
median & 22 & 22 & 24.5 & 1.3112 & 2.5 \tabularnewline
midrange & 27.5 & 35 & 35 & 3.5309 & 7.5 \tabularnewline
mode & 25 & 25 & 25 & 0 & 0 \tabularnewline
mode k.dens & 23 & 25 & 23.103 & 2.4966 & 0.10278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204785&T=1

[TABLE]
[ROW][C]Estimation Results of 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]23.004[/C][C]23.166[/C][C]23.327[/C][C]0.22605[/C][C]0.32306[/C][/ROW]
[ROW][C]median[/C][C]22[/C][C]22[/C][C]24.5[/C][C]1.3112[/C][C]2.5[/C][/ROW]
[ROW][C]midrange[/C][C]27.5[/C][C]35[/C][C]35[/C][C]3.5309[/C][C]7.5[/C][/ROW]
[ROW][C]mode[/C][C]25[/C][C]25[/C][C]25[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]23[/C][C]25[/C][C]23.103[/C][C]2.4966[/C][C]0.10278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204785&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204785&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 Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean23.00423.16623.3270.226050.32306
median222224.51.31122.5
midrange27.535353.53097.5
mode25252500
mode k.dens232523.1032.49660.10278


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 9
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Figure 10
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Figure 11
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Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 5 ; par3 = 0 ;
Parameters (R input):
par1 = 50 ; par2 = 5 ; par3 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par3 == '0') bw <- NULL
if (par3 != '0') bw <- as.numeric(par3)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
library(modeest)
library(lattice)
library(boot)
boot.stat <- function(s,i)
{
s.mean <- mean(s[i])
s.median <- median(s[i])
s.midrange <- (max(s[i]) + min(s[i])) / 2
s.mode <- mlv(s[i], method='mfv')$M
s.kernelmode <- mlv(s[i], method='kernel', bw=bw)$M
c(s.mean, s.median, s.midrange, s.mode, s.kernelmode)
}
(r <- boot(x,boot.stat, R=par1, stype='i'))
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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot7.png')
plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')
grid()
dev.off()
bitmap(file='plot8.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()
bitmap(file='plot9.png')
densityplot(~r$t[,4],col='black',main='Density Plot',xlab='mode')
dev.off()
bitmap(file='plot10.png')
densityplot(~r$t[,5],col='black',main='Density Plot',xlab='mode of kernel dens.')
dev.off()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[1],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element( a,signif( sqrt(var(r$t[,1])),par2 ) )
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$t[,2],0.25)[[1]]
q3 <- quantile(r$t[,2],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[2],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(sqrt(var(r$t[,2])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$t[,3],0.25)[[1]]
q3 <- quantile(r$t[,3],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[3],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(sqrt(var(r$t[,3])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode',header=TRUE)
q1 <- quantile(r$t[,4],0.25)[[1]]
q3 <- quantile(r$t[,4],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[4],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(sqrt(var(r$t[,4])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode k.dens',header=TRUE)
q1 <- quantile(r$t[,5],0.25)[[1]]
q3 <- quantile(r$t[,5],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[5],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(sqrt(var(r$t[,5])),par2))
a<-table.element(a,signif(q3-q1,par2))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')