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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationThu, 18 Dec 2014 17:03:32 +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/Dec/18/t1418923683dro5b9wbbhb6ef6.htm/, Retrieved Sun, 19 May 2024 21:02:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271162, Retrieved Sun, 19 May 2024 21:02:30 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2014-11-04 10:08:09] [32b17a345b130fdf5cc88718ed94a974]
-       [Central Tendency] [WS7 3] [2014-11-12 20:26:33] [81f624c2f0b20a2549c93e7c3dccf981]
- RMPD      [Bootstrap Plot - Central Tendency] [BS ET] [2014-12-18 17:03:32] [64070895b9a6ba473199a431c837c7d3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2,30928
1,44306
3,30928
-2,55694
2,44306
-8,55694
1,30928
1,44306
0,443064
-1,55694
1,44306
-5,55694
-0,556936
0,30928
-0,69072
-4,24631
-0,556936
2,30928
-0,11253
-3,69072
-4,55694
2,44306
2,30928
-2,55694
4,44306
-11,5569
8,88747
0,753686
2,44306
-2,69072
-2,69072
0,30928
4,44306
-2,55694
2,44306
0,443064
-8,69072
-7,11253
1,44306
7,30928
2,44306
2,44306
3,44306
3,44306
-3,11253
4,30928
-5,55694
-2,11253
-2,69072
3,88747
1,88747
-4,11253
-1,55694
-0,11253
-2,55694
6,44306
1,75369
-7,55694
-11,2463
7,88747
7,44306
4,30928
2,30928
-1,55694
0,30928
-1,11253
-0,69072
-1,55694
-3,55694
-0,246314
6,30928
0,30928
2,75369
1,88747
-4,69072
7,44306
-2,69072
2,88747
1,30928
-7,24631
3,44306
2,75369
-7,24631
2,75369
-1,11253
-9,24631
1,88747
-4,11253
2,75369
4,75369
-1,24631
-0,11253
-1,11253
-1,11253
2,75369
-2,11253
0,753686
4,88747
1,88747
2,88747
-0,11253
-0,11253
-0,246314
-13,1125
-4,11253
-1,11253
-8,24631
-11,2463
-4,24631
-1,24631
-1,24631
-0,246314
-2,11253
2,44306
1,44306
0,443064
4,44306
10,7537
-4,11253
6,44306
4,30928
2,44306
-0,69072
-0,69072
2,44306
-1,55694
-5,55694
-0,556936
-4,55694
2,88747
4,44306
4,44306
0,30928
-1,55694
-4,55694
-1,55694
-1,55694
2,44306
0,443064
3,30928
-1,55694
-1,11253
-0,556936
-17,5569
2,44306
4,30928
1,30928
-5,55694
-2,69072
-11,6907
3,44306
-1,69072
3,44306
3,44306
0,30928
3,88747
-1,11253
7,44306
4,30928
7,75369
-1,24631
8,75369
-0,11253
0,30928
2,44306
7,75369
0,88747
8,75369
3,88747
1,75369
2,75369
6,88747
0,753686
7,88747
4,44306
4,44306
-1,11253
-0,556936
-2,69072
-1,11253
1,30928
3,75369
-1,24631
4,44306
-1,24631
-2,11253
5,75369
5,75369
5,75369
4,75369
-2,11253
4,88747
-11,1125
-4,11253
-6,24631
-2,24631
8,88747
6,30928
-15,2463
-3,24631
1,75369
2,44306
0,88747
-1,69072
0,753686
-0,556936
2,75369
-4,55694
1,88747
-19,2463
-3,11253
3,88747
2,44306
-6,69072
-1,24631
5,30928
-9,55694
-3,11253
2,44306
2,88747
9,75369
-0,69072
-4,24631
8,44306
-4,55694
0,30928
2,75369
-2,69072
1,88747
2,44306
0,30928
-14,6907
1,30928
2,88747
-0,556936
-16,5569
3,44306
2,75369
-4,55694
-17,5569
-6,11253
5,30928
-2,24631
-1,11253
7,75369
4,88747
1,88747
-1,69072
-0,556936
3,30928
-1,55694
3,44306
0,30928
1,88747
-4,24631
6,44306
6,75369
0,88747
3,30928
-1,24631
-6,11253
0,88747
7,75369
-11,1125
-1,11253
-2,24631
1,88747
-0,246314
-6,24631
5,88747
1,88747
-3,24631
3,88747
-17,2463
6,75369
-1,11253
4,30928
2,75369
2,88747

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271162&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271162&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.71535-0.48423-0.196339.5341e-070.229720.446260.653770.299160.42606
median-0.24631-0.112530.309280.309280.753691.30931.31060.359560.44441
midrange-5.1794-5.1794-4.3347-4.2463-3.4016-3.4016-2.90160.60180.93311
mode-1.5573-1.25070.665262.44312.44312.94314.44311.46411.7778
mode k.dens-1.4654-1.2942-0.928712.4392.38662.71572.94211.64063.3153

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.71535 & -0.48423 & -0.19633 & 9.5341e-07 & 0.22972 & 0.44626 & 0.65377 & 0.29916 & 0.42606 \tabularnewline
median & -0.24631 & -0.11253 & 0.30928 & 0.30928 & 0.75369 & 1.3093 & 1.3106 & 0.35956 & 0.44441 \tabularnewline
midrange & -5.1794 & -5.1794 & -4.3347 & -4.2463 & -3.4016 & -3.4016 & -2.9016 & 0.6018 & 0.93311 \tabularnewline
mode & -1.5573 & -1.2507 & 0.66526 & 2.4431 & 2.4431 & 2.9431 & 4.4431 & 1.4641 & 1.7778 \tabularnewline
mode k.dens & -1.4654 & -1.2942 & -0.92871 & 2.439 & 2.3866 & 2.7157 & 2.9421 & 1.6406 & 3.3153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271162&T=1

[TABLE]
[ROW][C]Estimation Results of 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]-0.71535[/C][C]-0.48423[/C][C]-0.19633[/C][C]9.5341e-07[/C][C]0.22972[/C][C]0.44626[/C][C]0.65377[/C][C]0.29916[/C][C]0.42606[/C][/ROW]
[ROW][C]median[/C][C]-0.24631[/C][C]-0.11253[/C][C]0.30928[/C][C]0.30928[/C][C]0.75369[/C][C]1.3093[/C][C]1.3106[/C][C]0.35956[/C][C]0.44441[/C][/ROW]
[ROW][C]midrange[/C][C]-5.1794[/C][C]-5.1794[/C][C]-4.3347[/C][C]-4.2463[/C][C]-3.4016[/C][C]-3.4016[/C][C]-2.9016[/C][C]0.6018[/C][C]0.93311[/C][/ROW]
[ROW][C]mode[/C][C]-1.5573[/C][C]-1.2507[/C][C]0.66526[/C][C]2.4431[/C][C]2.4431[/C][C]2.9431[/C][C]4.4431[/C][C]1.4641[/C][C]1.7778[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.4654[/C][C]-1.2942[/C][C]-0.92871[/C][C]2.439[/C][C]2.3866[/C][C]2.7157[/C][C]2.9421[/C][C]1.6406[/C][C]3.3153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271162&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
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.71535-0.48423-0.196339.5341e-070.229720.446260.653770.299160.42606
median-0.24631-0.112530.309280.309280.753691.30931.31060.359560.44441
midrange-5.1794-5.1794-4.3347-4.2463-3.4016-3.4016-2.90160.60180.93311
mode-1.5573-1.25070.665262.44312.44312.94314.44311.46411.7778
mode k.dens-1.4654-1.2942-0.928712.4392.38662.71572.94211.64063.3153


Figures (Output of Computation)

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

Parameters (Session):
par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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$t[,1],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[1],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,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$t[,2],0.75)[[1]]
p01 <- quantile(r$t[,2],myq.1)[[1]]
p05 <- quantile(r$t[,2],myq.2)[[1]]
p95 <- quantile(r$t[,2],myq.3)[[1]]
p99 <- quantile(r$t[,2],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[2],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,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$t[,3],0.75)[[1]]
p01 <- quantile(r$t[,3],myq.1)[[1]]
p05 <- quantile(r$t[,3],myq.2)[[1]]
p95 <- quantile(r$t[,3],myq.3)[[1]]
p99 <- quantile(r$t[,3],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[3],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,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$t[,4],0.75)[[1]]
p01 <- quantile(r$t[,4],myq.1)[[1]]
p05 <- quantile(r$t[,4],myq.2)[[1]]
p95 <- quantile(r$t[,4],myq.3)[[1]]
p99 <- quantile(r$t[,4],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[4],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,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$t[,5],0.75)[[1]]
p01 <- quantile(r$t[,5],myq.1)[[1]]
p05 <- quantile(r$t[,5],myq.2)[[1]]
p95 <- quantile(r$t[,5],myq.3)[[1]]
p99 <- quantile(r$t[,5],myq.4)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[5],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(p95,par2))
a<-table.element(a,signif(p99,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')