Free Statistics

of Irreproducible Research!

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 computationTue, 09 Dec 2014 07:18:00 +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/09/t1418109491myxjnm729jdapds.htm/, Retrieved Sun, 19 May 2024 12:36:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264319, Retrieved Sun, 19 May 2024 12:36:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [paper] [2014-12-09 07:18:00] [c15d474939d69eac0efd26ce7542850f] [Current]
Feedback Forum

Post a new message
Dataseries X:
2.04564
0.470972
1.04564
0.470972
0.470972
-0.529028
1.04564
1.47097
0.470972
-0.529028
1.47097
-0.529028
0.470972
1.04564
1.04564
1.04962
0.470972
-0.954362
1.47495
0.0456382
-0.529028
0.470972
0.0456382
0.470972
0.470972
-4.52903
1.47495
1.04962
0.470972
-0.954362
-1.95436
1.04564
0.470972
1.47097
-0.529028
-0.529028
-3.95436
-0.525046
0.470972
1.04564
-0.529028
0.470972
0.470972
1.47097
-1.52505
2.04564
0.470972
0.474954
1.04564
-0.525046
0.474954
-0.525046
-0.529028
1.47495
-0.529028
1.47097
0.0496194
-1.52903
-3.95038
1.47495
1.47097
0.0456382
-0.954362
1.47097
1.04564
0.474954
0.0456382
1.47097
-2.52903
-0.950381
1.04564
1.04564
1.04962
0.474954
0.0456382
2.04564
1.47495
1.04564
-2.95038
-0.529028
1.04962
-0.950381
0.0496194
-0.525046
-3.95038
0.474954
0.474954
1.04962
1.04962
-1.95038
-1.52505
-1.52505
-0.525046
0.0496194
-0.525046
1.04962
1.47495
0.474954
0.474954
-0.525046
1.47495
0.0496194
-1.52505
-0.525046
0.474954
-0.950381
-4.95038
-2.95038
1.04962
-0.950381
1.04962
-1.52505
-0.337616
-0.337616
-0.337616
1.66238
0.241031
-1.33363
0.662384
1.23705
1.66238
0.23705
-0.76295
0.662384
-1.33762
-1.33762
1.66238
-1.33762
1.66637
0.662384
0.662384
2.23705
-1.33762
-3.33762
0.662384
-2.33762
-0.337616
1.66238
2.23705
0.662384
-1.33363
0.662384
-4.33762
0.662384
0.23705
2.23705
-3.33762
-0.76295
-3.76295
0.662384
1.23705
1.66238
0.662384
1.23705
1.66637
0.666366
1.66238
0.23705
0.241031
-0.758969
1.24103
-1.33363
0.23705
0.662384
1.24103
-0.333634
2.24103
1.66637
2.24103
-0.758969
0.666366
2.24103
1.66637
0.662384
0.662384
0.666366
0.662384
1.23705
1.66637
-0.76295
2.24103
1.24103
0.662384
1.24103
0.666366
2.24103
2.24103
2.24103
1.24103
0.666366
0.666366
-4.33363
-1.33363
-1.75897
-2.75897
1.66637
1.23705
-3.75897
-0.758969
-0.758969
1.66238
-0.333634
-0.76295
2.24103
-0.337616
1.24103
0.662384
0.666366
-6.75897
-1.33363
1.66637
1.66238
-0.76295
1.24103
1.23705
-2.33762
-1.33363
1.66238
1.66637
2.24103
-0.76295
-2.75897
0.662384
-3.33762
-1.76295
1.24103
-0.76295
0.666366
1.66238
0.23705
-2.76295
1.23705
0.666366
0.662384
-3.33762
1.66238
1.24103
-1.33762
-4.33762
-0.333634
1.23705
0.241031
-0.333634
1.24103
-0.333634
-0.333634
-0.76295
0.662384
1.23705
-0.337616
1.66238
2.23705
-0.333634
0.241031
0.662384
1.24103
-1.33363
2.23705
-0.758969
-2.33363
-0.333634
2.24103
-1.33363
-0.333634
-1.75897
1.66637
1.24103
-2.75897
1.66637
0.666366
0.241031
0.666366
-5.75897
1.24103
0.666366
2.23705
1.24103
1.66637
-0.954791
0.0452086
1.61987
1.04521
2.04521
2.04521
0.0452086
1.61987
-4.95479
-2.38013
-1.38013
1.04521
-0.376144
2.04521
1.62386
-2.38013
0.0452086
0.0452086
1.04521
-1.38013
-1.38013
0.619874
0.0452086
1.61987
-0.954791
0.0452086
1.04521
1.61987
0.0452086
2.61987
1.04521
-0.380126
1.61987
-1.38013
-3.38013
-0.380126
-2.38013
-1.95479
2.04521
-1.95479
-0.380126
0.619874
-0.380126
2.62386
-0.380126
0.0491898
1.04919
-1.95081
-2.95479
-2.38013
-2.95479
-1.37614
-2.95479
-0.954791
-1.38013
-1.95479
-0.376144
-1.38013
0.623856
-0.380126
-0.380126
1.61987
0.0491898
2.62386
-2.37614
0.0452086
0.0452086
1.04521
1.04919
0.619874
0.0452086
2.04521
-0.380126
0.619874
1.61987
-1.37614
-1.95479
-1.38013
1.62386
-1.37614
0.623856
-0.380126
-1.38013
0.0491898
-0.95081
1.04919
2.62386
0.619874
0.0491898
2.62386
0.0452086
0.0491898
-0.954791
1.04919
1.04521
0.623856
-1.38013
-0.380126
1.62386
0.623856
-0.95081
-2.38013
-0.376144
0.0452086
1.62386
0.619874
1.04919
2.62386
1.04919
0.0452086
-2.37614
-0.954791
0.619874
1.04919
-0.380126
0.0491898
1.04521
0.0491898
1.62386
2.04919
0.0491898
1.61987
1.04521
2.04521
-0.954791
1.04521
1.61987
2.04521
0.623856
-0.376144
-1.95081
1.61987
1.04521
-0.95081
-2.38013
2.61987
-0.95081
-1.95479
1.04919
2.04521
-2.38013
0.0491898
-1.95479
1.04919
0.0491898
1.61987
-0.380126
2.04521
-1.37614
-0.95081
0.0491898
2.61987
-0.954791
-0.376144
2.04919
2.62386
-0.376144
-0.95081
2.04919
1.04521
-0.954791
-0.376144
0.619874
0.0491898
-4.38013
-0.380126
2.04919
-0.95081
-1.95479
-0.954791
1.04521
-3.95479
-0.95081
1.04919
2.62386
2.04919
1.62386
2.62386
0.0491898
-1.95081
0.0452086
2.04919
2.04521
1.04521
1.04919
-0.380126
2.04521
1.04521
2.04919
1.62386
0.0491898
1.04919
0.0452086
-1.95479
0.619874
0.0491898
-0.95081
0.623856
-0.376144
2.04919
-0.954791
0.0452086
-2.95479
1.62386
-0.95081
-1.37614
0.0491898
1.04521
0.432444
0.432444
0.432444
0.432444
1.43244
0.857778
-3.1462
0.432444
0.432444
0.432444
0.432444
-1.56756
-2.56756
-0.567556
2.43244
-2.56756
-2.56756
-4.14222
1.43244
-3.56756
1.43244
1.85778
1.43244
1.43244
-0.146203
1.43244
1.43244
0.432444
-1.56756
0.432444
0.432444
-2.56756
-0.567556
-1.14222
-0.567556
-0.567556
-2.56756
-1.56756
-4.1462
1.43244
0.428462
-0.567556
-2.56756
-2.56756
0.857778
-2.56756
1.43244
-0.567556
-2.56756
-0.142222
1.43244
-2.56756
-1.56756
2.43244
-4.56756
-0.567556
-1.14222
-3.14222
-1.56756
-3.56756
-1.14222
1.43244
1.43244
-2.56756
-0.146203
-1.56756
-3.56756
1.85778
-2.14222
-1.56756
1.85778
1.43244
-2.56756
2.43244
-2.56756
-2.14222
-1.14222
-3.14222
-4.14222
-3.56756
1.43244
-0.567556
1.85778
2.43244
1.43244
1.43244
-2.56756
1.43244
0.853797
1.43244
1.43244
0.432444
-1.56756
2.43244
-0.567556
2.43244
0.432444
1.43244
1.43244
-0.567556
1.43244
0.432444
1.43244
-1.56756
-1.14222
-0.567556
-0.567556
-1.56756
-0.567556
-0.567556
0.857778
-2.56756
1.43244
-1.56756
-0.567556
-1.56756
-1.56756
2.43244
-1.56756
0.432444
0.432444
0.432444
2.43244
-1.14222
0.432444
-2.56756
0.432444
-3.14222
0.432444
-3.56756
-1.56756
-0.142222
1.43244
2.43244
2.43244
-0.567556
0.432444
-4.56756
-1.56756
-0.567556
0.432444
-0.567556
1.43244
2.43244
-1.14222
-3.56756
2.43244
1.43244
-0.567556
-0.567556
1.43244
-1.56756
1.85778
0.857778
-0.567556
0.857778
-0.95081
0.623856
-2.37614
0.623856
-2.37614
-0.376144
1.62386
0.623856
-1.37614
1.04919
1.62386
2.62386
1.04919
-2.37614
0.623856
0.623856
2.62386
0.623856
0.0491898
-1.37614
1.62386
0.623856
-1.37614
1.62386
2.62386
1.62386
-0.376144
-4.37614
2.04919
2.62386
-2.37614
0.623856
2.62386
0.0491898
1.04919
-1.37614
-0.95081
-0.95081
-3.37614
-2.37614
1.62386
-0.376144
-1.37614
0.623856
0.623856
-1.37614
0.623856
1.62386
-0.376144
-0.376144
1.04919
-0.376144
1.62386
1.62386
0.623856
-0.376144
-3.37614
1.62386
1.62386
1.62386
0.623856
-1.37614
-1.37614




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264319&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264319&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264319&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'Sir Maurice George Kendall' @ kendall.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.15104-0.094178-0.039712-2.1617e-070.049570.101870.129170.0627760.089282
median0.049190.049190.241030.432440.432440.470970.470970.131790.19141
midrange-2.0676-2.0676-2.0676-2.0676-1.5676-1.1633-0.971850.354860.5
mode-0.57756-0.567560.489931.43241.43241.43241.62390.679840.94251
mode k.dens0.470050.54640.603590.768351.14431.51951.55060.361230.54071

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.15104 & -0.094178 & -0.039712 & -2.1617e-07 & 0.04957 & 0.10187 & 0.12917 & 0.062776 & 0.089282 \tabularnewline
median & 0.04919 & 0.04919 & 0.24103 & 0.43244 & 0.43244 & 0.47097 & 0.47097 & 0.13179 & 0.19141 \tabularnewline
midrange & -2.0676 & -2.0676 & -2.0676 & -2.0676 & -1.5676 & -1.1633 & -0.97185 & 0.35486 & 0.5 \tabularnewline
mode & -0.57756 & -0.56756 & 0.48993 & 1.4324 & 1.4324 & 1.4324 & 1.6239 & 0.67984 & 0.94251 \tabularnewline
mode k.dens & 0.47005 & 0.5464 & 0.60359 & 0.76835 & 1.1443 & 1.5195 & 1.5506 & 0.36123 & 0.54071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264319&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]-0.15104[/C][C]-0.094178[/C][C]-0.039712[/C][C]-2.1617e-07[/C][C]0.04957[/C][C]0.10187[/C][C]0.12917[/C][C]0.062776[/C][C]0.089282[/C][/ROW]
[ROW][C]median[/C][C]0.04919[/C][C]0.04919[/C][C]0.24103[/C][C]0.43244[/C][C]0.43244[/C][C]0.47097[/C][C]0.47097[/C][C]0.13179[/C][C]0.19141[/C][/ROW]
[ROW][C]midrange[/C][C]-2.0676[/C][C]-2.0676[/C][C]-2.0676[/C][C]-2.0676[/C][C]-1.5676[/C][C]-1.1633[/C][C]-0.97185[/C][C]0.35486[/C][C]0.5[/C][/ROW]
[ROW][C]mode[/C][C]-0.57756[/C][C]-0.56756[/C][C]0.48993[/C][C]1.4324[/C][C]1.4324[/C][C]1.4324[/C][C]1.6239[/C][C]0.67984[/C][C]0.94251[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.47005[/C][C]0.5464[/C][C]0.60359[/C][C]0.76835[/C][C]1.1443[/C][C]1.5195[/C][C]1.5506[/C][C]0.36123[/C][C]0.54071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264319&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264319&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-0.15104-0.094178-0.039712-2.1617e-070.049570.101870.129170.0627760.089282
median0.049190.049190.241030.432440.432440.470970.470970.131790.19141
midrange-2.0676-2.0676-2.0676-2.0676-1.5676-1.1633-0.971850.354860.5
mode-0.57756-0.567560.489931.43241.43241.43241.62390.679840.94251
mode k.dens0.470050.54640.603590.768351.14431.51951.55060.361230.54071



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')