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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 10:36:24 +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/t1418466991edwelmo9hpss75l.htm/, Retrieved Thu, 16 May 2024 14:50:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266952, Retrieved Thu, 16 May 2024 14:50:20 +0000
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Estimated Impact68
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 10:36:24] [c15d474939d69eac0efd26ce7542850f] [Current]
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Dataseries X:
-0.500161
-0.0154003
1.49984
-0.0154003
-1.0154
-5.0154
0.499839
-2.0154
-0.0154003
-3.0154
-2.0154
-1.0154
0.9846
-1.50016
-0.500161
-3.27641
-0.0154003
-0.500161
-0.791649
-0.500161
-2.0154
0.9846
0.499839
-0.0154003
0.9846
-1.0154
1.20835
0.723591
0.9846
0.499839
-0.500161
-0.500161
0.9846
-1.0154
0.9846
0.9846
-1.50016
-0.791649
-0.0154003
1.49984
0.9846
0.9846
0.9846
0.9846
0.208351
0.499839
-2.0154
0.208351
-3.50016
0.208351
1.20835
1.20835
-1.0154
1.20835
0.9846
0.9846
0.723591
-3.0154
-2.27641
1.20835
0.9846
0.499839
1.49984
-0.0154003
-0.500161
0.208351
-0.500161
-0.0154003
-1.0154
1.72359
0.499839
-0.500161
0.723591
0.208351
-0.500161
-2.50016
0.208351
1.49984
0.723591
-0.0154003
-1.27641
-1.27641
0.723591
1.20835
-2.27641
1.20835
-0.791649
0.723591
1.72359
0.723591
0.208351
1.20835
0.208351
1.72359
0.208351
-0.276409
0.208351
0.208351
0.208351
0.208351
-0.791649
0.723591
-4.79165
0.208351
1.20835
-3.27641
-3.27641
-1.27641
-1.27641
1.72359
-0.276409
0.208351
0.943222
0.943222
0.943222
0.943222
1.68221
1.16697
-0.0567776
1.45846
-0.0567776
1.45846
0.458462
-0.0567776
0.943222
-1.05678
-0.0567776
0.943222
1.16697
-0.0567776
0.943222
1.45846
0.943222
-1.05678
0.943222
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0.943222
0.943222
0.458462
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0.166974
0.943222
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0.943222
1.45846
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0.943222
0.458462
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0.458462
-0.0567776
-0.0567776
0.458462
1.16697
0.166974
0.943222
0.458462
1.68221
0.682213
1.68221
0.166974
0.458462
0.943222
1.68221
1.16697
1.68221
1.16697
1.68221
1.68221
1.16697
0.682213
1.16697
0.943222
0.943222
1.16697
-0.0567776
-1.54154
-0.833026
1.45846
-0.317787
1.68221
0.943222
-1.31779
-0.833026
-0.317787
-0.317787
-0.317787
-0.317787
-0.833026
1.16697
-0.833026
-0.833026
-2.31779
0.682213
1.16697
1.45846
-3.31779
-0.317787
0.682213
-0.0567776
1.16697
0.458462
1.68221
0.943222
0.682213
-0.0567776
1.16697
-4.31779
1.16697
0.166974
0.943222
-2.54154
0.682213
0.458462
-4.05678
0.166974
-0.0567776
-0.833026
1.68221
0.458462
1.68221
0.943222
0.943222
1.45846
0.682213
1.45846
0.166974
-0.0567776
0.458462
-4.54154
1.45846
1.16697
0.943222
-5.05678
-0.0567776
0.682213
-0.0567776
-5.05678
-0.833026
1.45846
-0.317787
0.166974
1.68221
1.16697
1.16697
0.458462
-0.0567776
1.45846
0.943222
0.943222
-0.541538
0.166974
-2.31779
0.943222
1.68221
-0.833026
1.45846
0.682213
-0.833026
1.16697
1.68221
-3.83303
0.166974
0.682213
1.16697
-0.317787
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0.166974
0.682213
1.16697
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1.68221
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0.682213
-1.83303
0.860468
-3.13953
1.37571
0.860468
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0.860468
1.37571
0.860468
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0.375708
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1.37571
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0.860468
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1.37571
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0.599459
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0.0842191
0.0842191
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0.860468
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1.59946
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1.59946
0.375708
0.375708
1.37571
0.0842191
0.599459
0.599459
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0.860468
0.0842191
1.37571
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0.375708
1.37571
1.37571
-1.40054
0.860468
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0.599459
1.59946
1.59946
0.375708
0.375708
0.0842191
-0.915781
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0.599459
0.375708
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0.599459
0.860468
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-0.139532
1.08422
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1.59946
-0.624292
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1.59946
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0.0842191
-0.624292
1.59946
0.860468
0.599459
1.37571
1.08422
-0.400541
0.0842191
0.860468
1.59946
0.860468
1.37571
1.08422
1.37571
1.08422
0.860468
1.08422
-0.400541
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1.08422
0.375708
-0.139532
0.860468
-0.139532
-3.13953
1.37571
0.860468
-0.400541
-0.400541
1.08422
0.375708
0.860468
1.08422
0.375708
-1.62429
-0.915781
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1.08422
0.860468
0.375708
1.08422
0.860468
0.0842191
0.0842191
1.37571
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0.0842191
0.0842191
1.37571
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0.0842191
-2.40054
1.59946
-3.91578
1.08422
0.860468
0.860468
0.599459
-2.62429
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1.37571
0.0842191
-1.91578
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-0.139532
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0.0842191
0.0842191
1.59946
1.08422
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0.0842191
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0.860468
0.860468
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1.08422
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1.59946
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1.64084
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0.640836
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1.64084
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1.64084
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0.640836
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0.640836
0.640836
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0.640836
0.125596
0.640836
0.640836
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1.1256
0.640836
0.640836
0.901845
1.64084
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1.64084
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0.125596
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0.901845
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0.640836
1.1256
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0.125596
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0.640836
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0.640836
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1.1256
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0.640836
0.125596
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0.640836
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1.64084
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1.1256
1.1256
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1.1256
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0.640836
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0.125596
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1.1256
0.640836
1.64084
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1.1256
0.640836
1.64084
1.64084
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0.901845
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1.64084
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0.640836
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0.640836
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1.64084
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0.640836
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1.08422
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1.59946
0.599459
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0.599459
1.59946
0.599459
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0.599459
-1.40054
0.0842191
0.599459
0.599459
0.599459
-0.400541
0.599459
1.59946
0.599459
0.599459
1.59946
-0.400541
-0.400541
-1.40054




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266952&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266952&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266952&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'Herman Ole Andreas Wold' @ wold.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.12939-0.086734-0.0398633.3574e-070.0411780.0820730.112060.0547290.081041
median0.0842190.166970.208350.375710.375710.458460.599460.119250.16736
midrange-1.6873-1.6666-1.6666-1.6666-1.6666-1.6666-1.64540.0166210
mode-0.35916-0.359160.599460.640840.640840.860471.64080.361710.041377
mode k.dens-0.3774-0.0292320.636310.665480.940761.01731.60450.34850.30445

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.12939 & -0.086734 & -0.039863 & 3.3574e-07 & 0.041178 & 0.082073 & 0.11206 & 0.054729 & 0.081041 \tabularnewline
median & 0.084219 & 0.16697 & 0.20835 & 0.37571 & 0.37571 & 0.45846 & 0.59946 & 0.11925 & 0.16736 \tabularnewline
midrange & -1.6873 & -1.6666 & -1.6666 & -1.6666 & -1.6666 & -1.6666 & -1.6454 & 0.016621 & 0 \tabularnewline
mode & -0.35916 & -0.35916 & 0.59946 & 0.64084 & 0.64084 & 0.86047 & 1.6408 & 0.36171 & 0.041377 \tabularnewline
mode k.dens & -0.3774 & -0.029232 & 0.63631 & 0.66548 & 0.94076 & 1.0173 & 1.6045 & 0.3485 & 0.30445 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266952&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]-0.12939[/C][C]-0.086734[/C][C]-0.039863[/C][C]3.3574e-07[/C][C]0.041178[/C][C]0.082073[/C][C]0.11206[/C][C]0.054729[/C][C]0.081041[/C][/ROW]
[ROW][C]median[/C][C]0.084219[/C][C]0.16697[/C][C]0.20835[/C][C]0.37571[/C][C]0.37571[/C][C]0.45846[/C][C]0.59946[/C][C]0.11925[/C][C]0.16736[/C][/ROW]
[ROW][C]midrange[/C][C]-1.6873[/C][C]-1.6666[/C][C]-1.6666[/C][C]-1.6666[/C][C]-1.6666[/C][C]-1.6666[/C][C]-1.6454[/C][C]0.016621[/C][C]0[/C][/ROW]
[ROW][C]mode[/C][C]-0.35916[/C][C]-0.35916[/C][C]0.59946[/C][C]0.64084[/C][C]0.64084[/C][C]0.86047[/C][C]1.6408[/C][C]0.36171[/C][C]0.041377[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.3774[/C][C]-0.029232[/C][C]0.63631[/C][C]0.66548[/C][C]0.94076[/C][C]1.0173[/C][C]1.6045[/C][C]0.3485[/C][C]0.30445[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266952&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266952&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-0.12939-0.086734-0.0398633.3574e-070.0411780.0820730.112060.0547290.081041
median0.0842190.166970.208350.375710.375710.458460.599460.119250.16736
midrange-1.6873-1.6666-1.6666-1.6666-1.6666-1.6666-1.64540.0166210
mode-0.35916-0.359160.599460.640840.640840.860471.64080.361710.041377
mode k.dens-0.3774-0.0292320.636310.665480.940761.01731.60450.34850.30445



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')