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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 computationMon, 15 Dec 2014 11:09:09 +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/15/t1418641773p3zqd3tobwv7tb5.htm/, Retrieved Thu, 16 May 2024 14:44:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268106, Retrieved Thu, 16 May 2024 14:44:02 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsBootstrap Plot Totale score negatieve waarden
Estimated Impact52
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Paper data] [2014-12-15 11:09:09] [99d5c1073827aabbadf7ab1e7da1d584] [Current]
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Dataseries X:
-5.83435
0.520857
2.01706
1.56008
-0.435736
1.5845
-2.52814
1.71658
-4.20364
1.96279
1.45397
4.11738
-3.36967
-1.5354
0.496876
-0.367061
2.58771
0.887249
0.765678
-1.9172
0.811842
0.0994056
-0.0619229
-0.832395
2.67465
-2.1701
0.819508
-0.140187
-0.159958
2.6357
-1.52884
2.09492
0.597659
0.193102
0.590058
-0.959794
-3.1729
-0.177719
-1.7751
1.24801
-7.04864
0.255649
1.7881
-0.722119
2.39788
-0.572888
0.553691
0.932192
-1.39445
-1.29741
2.56517
1.68875
-1.11895
6.69345
2.29565
0.0929099
2.36183
2.67768
0.804831
-2.7031
1.47995
3.14126
-1.39678
-0.868783
-0.868783
2.5788
-1.93308
2.21161
-0.923156
-3.46045
-1.56053
-1.34966
1.69499
0.37593
-2.68681
1.11047
3.77494
-1.13683
-0.481589
0.196616
-1.41269
-0.0409315
-2.51228
3.798
0.824082
1.72501
-2.35417
-0.0538119
2.3925
1.79739
-0.310735
1.47677
-2.01105
0.676646
-1.53968
3.09956
-1.9098
1.28972
-0.476764
2.06309
1.52847
-0.109352
-0.804591
-3.24925
-0.21182
-1.32318
-1.49469
1.9435
-1.12175
1.01981
-0.938225
0.254103
2.07022
-4.05078
-1.34169
1.35196
1.79235
3.68594
2.11515
2.47714
1.73232
2.67536
2.599
1.04955
0.17628
-3.31733
-3.60664
-6.61108
1.06116
-2.44435
-1.19159
-0.32077
-1.72546
-1.29338
1.44696
2.29565
3.26251
-1.54146
-0.614586
-0.784826
-0.271011
-0.183026
-1.01176
-2.37531
-2.05635
0.357935
1.03557
-3.50001
1.22632
-3.79077
-6.09324
-1.31671
-6.04646
0.0929099
-0.97034
-3.1224
1.44696
2.08834
0.793685
0.974958
2.03432
2.25919
-0.765007
1.54896
0.218191
0.777107
2.68224
0.612756
0.323036
1.74414
-2.08948




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268106&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]6 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=268106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268106&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.36381-0.26052-0.133452.6374e-070.104010.26080.363890.163260.23747
median-0.27141-0.18303-0.0409320.19310.255650.553690.677820.215560.29658
midrange-1.6373-1.6253-1.4094-0.17759-0.177590.300110.429550.68951.2318
mode-6.0469-2.5131-0.868780.741681.4472.60293.33711.73522.3157
mode k.dens-1.2734-1.198-0.183791.05281.54881.84722.02351.03081.7326

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.36381 & -0.26052 & -0.13345 & 2.6374e-07 & 0.10401 & 0.2608 & 0.36389 & 0.16326 & 0.23747 \tabularnewline
median & -0.27141 & -0.18303 & -0.040932 & 0.1931 & 0.25565 & 0.55369 & 0.67782 & 0.21556 & 0.29658 \tabularnewline
midrange & -1.6373 & -1.6253 & -1.4094 & -0.17759 & -0.17759 & 0.30011 & 0.42955 & 0.6895 & 1.2318 \tabularnewline
mode & -6.0469 & -2.5131 & -0.86878 & 0.74168 & 1.447 & 2.6029 & 3.3371 & 1.7352 & 2.3157 \tabularnewline
mode k.dens & -1.2734 & -1.198 & -0.18379 & 1.0528 & 1.5488 & 1.8472 & 2.0235 & 1.0308 & 1.7326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268106&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]-0.36381[/C][C]-0.26052[/C][C]-0.13345[/C][C]2.6374e-07[/C][C]0.10401[/C][C]0.2608[/C][C]0.36389[/C][C]0.16326[/C][C]0.23747[/C][/ROW]
[ROW][C]median[/C][C]-0.27141[/C][C]-0.18303[/C][C]-0.040932[/C][C]0.1931[/C][C]0.25565[/C][C]0.55369[/C][C]0.67782[/C][C]0.21556[/C][C]0.29658[/C][/ROW]
[ROW][C]midrange[/C][C]-1.6373[/C][C]-1.6253[/C][C]-1.4094[/C][C]-0.17759[/C][C]-0.17759[/C][C]0.30011[/C][C]0.42955[/C][C]0.6895[/C][C]1.2318[/C][/ROW]
[ROW][C]mode[/C][C]-6.0469[/C][C]-2.5131[/C][C]-0.86878[/C][C]0.74168[/C][C]1.447[/C][C]2.6029[/C][C]3.3371[/C][C]1.7352[/C][C]2.3157[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.2734[/C][C]-1.198[/C][C]-0.18379[/C][C]1.0528[/C][C]1.5488[/C][C]1.8472[/C][C]2.0235[/C][C]1.0308[/C][C]1.7326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268106&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-0.36381-0.26052-0.133452.6374e-070.104010.26080.363890.163260.23747
median-0.27141-0.18303-0.0409320.19310.255650.553690.677820.215560.29658
midrange-1.6373-1.6253-1.4094-0.17759-0.177590.300110.429550.68951.2318
mode-6.0469-2.5131-0.868780.741681.4472.60293.33711.73522.3157
mode k.dens-1.2734-1.198-0.183791.05281.54881.84722.02351.03081.7326



Parameters (Session):
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')