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Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationSat, 13 Dec 2014 10:11:20 +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/13/t14184654913wteznofuggrbqq.htm/, Retrieved Thu, 16 May 2024 21:03:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266935, Retrieved Thu, 16 May 2024 21:03:02 +0000
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Original text written by user:
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Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Paper] [2014-12-13 10:11:20] [c15d474939d69eac0efd26ce7542850f] [Current]
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Dataseries X:
0.903353
0.335297
-0.0966473
0.335297
2.3353
-1.6647
-0.0966473
-0.664703
2.3353
1.3353
1.3353
-2.6647
-0.664703
-0.0966473
0.903353
-0.990963
-0.664703
2.90335
0.440982
-2.09665
-1.6647
1.3353
1.90335
-2.6647
2.3353
-2.6647
5.44098
0.00903701
-0.664703
-2.09665
-0.0966473
-0.0966473
2.3353
-2.6647
1.3353
-1.6647
-1.09665
-2.55902
0.335297
2.90335
1.3353
-0.664703
1.3353
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0.903353
-2.6647
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0.903353
2.44098
-0.559018
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-2.6647
0.440982
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1.3353
0.00903701
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4.44098
3.3353
3.90335
-1.09665
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-0.0966473
-2.55902
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1.00904
3.90335
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2.00904
0.440982
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1.44098
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3.3353
2.00904
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1.00904
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2.44098
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0.00903701
2.00904
2.00904
1.44098
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1.44098
1.00904
0.440982
2.00904
3.44098
0.440982
1.44098
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0.440982
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0.00903701
1.00904
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0.00903701
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5.9882
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3.9882
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2.9882
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3.9882
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0.988201
3.42015
-0.0117987
5.42015
0.314462
0.314462
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0.882517
0.988201
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2.31446
0.988201
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2.9882
2.9882
2.9882
2.42015
2.42015
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1.9882
3.42015
3.88252
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0.314462
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1.42015
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2.88252
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4.31446
0.314462
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1.9882
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0.314462
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0.420146
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1.9882
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0.882517
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0.420146
2.9882
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1.42015
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0.882517
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0.988201
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3.37847
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0.94653
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0.94653
0.94653
-2.05347
-2.05347




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266935&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266935&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266935&T=0

As an alternative you can also use a 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'Gertrude Mary Cox' @ cox.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.16943-0.11719-0.0539067.1411e-070.0380660.116410.148240.0718630.091972
median-0.11748-0.11748-0.05347-0.05347-0.032634-0.032634-0.0117990.0348130.020836
midrange0.151330.356510.377720.651330.651330.651330.935360.164460.27361
mode-3.0326-2.0326-2.0326-2.0326-0.0326340.390560.967371.05692
mode k.dens-0.7528-0.60626-0.18946-0.0704480.00652520.129270.330210.278120.19598

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.16943 & -0.11719 & -0.053906 & 7.1411e-07 & 0.038066 & 0.11641 & 0.14824 & 0.071863 & 0.091972 \tabularnewline
median & -0.11748 & -0.11748 & -0.05347 & -0.05347 & -0.032634 & -0.032634 & -0.011799 & 0.034813 & 0.020836 \tabularnewline
midrange & 0.15133 & 0.35651 & 0.37772 & 0.65133 & 0.65133 & 0.65133 & 0.93536 & 0.16446 & 0.27361 \tabularnewline
mode & -3.0326 & -2.0326 & -2.0326 & -2.0326 & -0.032634 & 0.39056 & 0.96737 & 1.0569 & 2 \tabularnewline
mode k.dens & -0.7528 & -0.60626 & -0.18946 & -0.070448 & 0.0065252 & 0.12927 & 0.33021 & 0.27812 & 0.19598 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266935&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]-0.16943[/C][C]-0.11719[/C][C]-0.053906[/C][C]7.1411e-07[/C][C]0.038066[/C][C]0.11641[/C][C]0.14824[/C][C]0.071863[/C][C]0.091972[/C][/ROW]
[ROW][C]median[/C][C]-0.11748[/C][C]-0.11748[/C][C]-0.05347[/C][C]-0.05347[/C][C]-0.032634[/C][C]-0.032634[/C][C]-0.011799[/C][C]0.034813[/C][C]0.020836[/C][/ROW]
[ROW][C]midrange[/C][C]0.15133[/C][C]0.35651[/C][C]0.37772[/C][C]0.65133[/C][C]0.65133[/C][C]0.65133[/C][C]0.93536[/C][C]0.16446[/C][C]0.27361[/C][/ROW]
[ROW][C]mode[/C][C]-3.0326[/C][C]-2.0326[/C][C]-2.0326[/C][C]-2.0326[/C][C]-0.032634[/C][C]0.39056[/C][C]0.96737[/C][C]1.0569[/C][C]2[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.7528[/C][C]-0.60626[/C][C]-0.18946[/C][C]-0.070448[/C][C]0.0065252[/C][C]0.12927[/C][C]0.33021[/C][C]0.27812[/C][C]0.19598[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266935&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266935&T=1

As an alternative you can also use a QR Code:  

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

Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.16943-0.11719-0.0539067.1411e-070.0380660.116410.148240.0718630.091972
median-0.11748-0.11748-0.05347-0.05347-0.032634-0.032634-0.0117990.0348130.020836
midrange0.151330.356510.377720.651330.651330.651330.935360.164460.27361
mode-3.0326-2.0326-2.0326-2.0326-0.0326340.390560.967371.05692
mode k.dens-0.7528-0.60626-0.18946-0.0704480.00652520.129270.330210.278120.19598



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')