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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 computationWed, 17 Dec 2014 13:02:42 +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/17/t1418821415gjou0mmaa8dg3wa.htm/, Retrieved Thu, 31 Oct 2024 22:51:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270160, Retrieved Thu, 31 Oct 2024 22:51:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
- R  D  [Bootstrap Plot - Central Tendency] [] [2014-12-16 12:13:50] [ea990983fba95a758c0bb6d29c9aee24]
-    D      [Bootstrap Plot - Central Tendency] [] [2014-12-17 13:02:42] [6260c34aa94cecca073345f42e0d4b5d] [Current]
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Dataseries X:
2.27878
-2.43865
1.01911
1.17429
-4.54167
-4.03695
-1.03567
2.9517
0.80204
-0.837639
-4.47299
-0.673222
-6.24955
-0.222041
-0.842843
-2.23695
-0.355717
-0.67778
-2.03441
-2.47848
-1.24568
-4.53594
0.950453
-0.88427
2.56197
-0.0880259
-2.25486
1.70146
1.78015
-0.726582
0.149253
-3.19132
-0.534637
-4.64911
-2.11522
-3.92337
-1.44429
-1.66301
-0.58718
0.187236
-2.57035
-1.58774
-4.72078
-1.42338
1.6146
-0.519972
0.90107
-1.19494
-0.421266
-4.10355
3.02783
0.89771
-0.545044
-4.68677
-1.04336
0.860812
0.631278
-1.95189
-5.27645
3.04536
-1.1757
1.91598
-2.02699
1.15179
-3.46775
3.13959
-1.00419
-1.60542
-2.1159
0.867855
2.16997
0.968735
-0.739383
0.462632
-1.99779
2.48991
-4.28261
0.553669
0.367079
1.37026
0.139563
-1.93379
-3.74217
-0.835695
2.80752
-1.20875
-4.56487
-1.29144
0.165984
0.0545751
-0.636713
-2.07835
2.71022
2.05601
-5.05084
-0.475207
-2.73348
-4.05602
-1.04742
-3.14493
0.394693
2.56433
-3.67797
-0.357122
-0.994008
-1.77291
-1.71668
2.43346
-2.1173
2.1549
-4.99022
-4.07447
-1.22453
0.710628
0.815685
-4.41132
0.93485
2.01451
2.34409
0.464308
2.22939
-3.26068
2.75125
-2.02032
3.30499
0.149275
3.79794
-3.44131
-0.744276
0.667127
-3.46653
1.914
1.60139
1.50836
-1.26558
1.52186
0.0717225
0.842287
-1.57718
3.32145
-3.94641
0.187115
-1.35496
0.535289
3.40553
-0.128224
0.829029
1.53358
0.864294
1.90462
-0.576156
-0.505801
0.475416
-3.60682
1.22662
-5.0929
1.7122
1.50747
0.0389016
2.62539
0.51466
2.28382
1.78913
-1.43264
-0.0932147
4.48913
-0.866257
1.70307
4.02764
3.82879
1.45455
1.63885
3.72291
-0.382151
-2.18125
0.378994
3.69185
1.25213
-0.235385
-0.235385
2.94542
-0.642367
1.86502
1.13091
-3.3044
-0.907048
0.56014
1.54351
0.187515
-1.79305
1.28892
4.23108
-0.214014
1.93337
0.0374179
0.756772
2.46073
-1.33615
3.16083
2.01898
0.913682
-2.89103
0.0111026
3.62291
1.987
2.042
1.98216
-0.124265
0.402593
-0.591543
3.59821
-2.56271
2.92182
0.843685
1.22904
1.45914
2.58379
1.61571
-2.58824
-1.43181
-0.49251
-1.02239
2.23121
1.35358
2.56876
1.27987
0.228299
0.55086
-3.03383
0.976561
1.35435
2.68299
2.52611
1.67317
3.89407
2.59098
2.2305
3.23353
1.60202
1.85316
-1.27359
-1.0002
-5.16748
1.21575
-3.13956
-1.39788
-0.423432
-1.46249
-3.244
2.63642
3.82879
3.10225
0.898857
0.239049
0.794247
-0.206259
-0.605549
-1.17034
-2.02746
-2.34408
1.55487
1.92777
-1.16585
1.7781
-1.83026
-5.98172
0.813998
-4.7296
1.45455
1.7155
-1.4597
2.63642
1.65632
0.79325
1.66331
1.80595
3.79405
1.44069
4.21719
0.644125
0.921526
1.08211
1.90783
1.65331
1.759
-0.604929




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=270160&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=270160&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270160&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 Ronald Aylmer Fisher' @ fisher.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.3146-0.22735-0.093134-8.7063e-080.0941160.200120.275450.133380.18725
median-0.22601-0.0581530.15190.233670.462840.560140.757150.221230.31094
midrange-1.1781-1.0162-0.88021-0.88021-0.7463-0.39366-0.33880.206370.13392
mode-4.6125-2.3636-0.855461.92111.5393.32144.21731.82242.3945
mode k.dens-1.2486-0.638361.0321.27481.44551.6471.70040.658960.41352

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.3146 & -0.22735 & -0.093134 & -8.7063e-08 & 0.094116 & 0.20012 & 0.27545 & 0.13338 & 0.18725 \tabularnewline
median & -0.22601 & -0.058153 & 0.1519 & 0.23367 & 0.46284 & 0.56014 & 0.75715 & 0.22123 & 0.31094 \tabularnewline
midrange & -1.1781 & -1.0162 & -0.88021 & -0.88021 & -0.7463 & -0.39366 & -0.3388 & 0.20637 & 0.13392 \tabularnewline
mode & -4.6125 & -2.3636 & -0.85546 & 1.9211 & 1.539 & 3.3214 & 4.2173 & 1.8224 & 2.3945 \tabularnewline
mode k.dens & -1.2486 & -0.63836 & 1.032 & 1.2748 & 1.4455 & 1.647 & 1.7004 & 0.65896 & 0.41352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270160&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]-0.3146[/C][C]-0.22735[/C][C]-0.093134[/C][C]-8.7063e-08[/C][C]0.094116[/C][C]0.20012[/C][C]0.27545[/C][C]0.13338[/C][C]0.18725[/C][/ROW]
[ROW][C]median[/C][C]-0.22601[/C][C]-0.058153[/C][C]0.1519[/C][C]0.23367[/C][C]0.46284[/C][C]0.56014[/C][C]0.75715[/C][C]0.22123[/C][C]0.31094[/C][/ROW]
[ROW][C]midrange[/C][C]-1.1781[/C][C]-1.0162[/C][C]-0.88021[/C][C]-0.88021[/C][C]-0.7463[/C][C]-0.39366[/C][C]-0.3388[/C][C]0.20637[/C][C]0.13392[/C][/ROW]
[ROW][C]mode[/C][C]-4.6125[/C][C]-2.3636[/C][C]-0.85546[/C][C]1.9211[/C][C]1.539[/C][C]3.3214[/C][C]4.2173[/C][C]1.8224[/C][C]2.3945[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.2486[/C][C]-0.63836[/C][C]1.032[/C][C]1.2748[/C][C]1.4455[/C][C]1.647[/C][C]1.7004[/C][C]0.65896[/C][C]0.41352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270160&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270160&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-0.3146-0.22735-0.093134-8.7063e-080.0941160.200120.275450.133380.18725
median-0.22601-0.0581530.15190.233670.462840.560140.757150.221230.31094
midrange-1.1781-1.0162-0.88021-0.88021-0.7463-0.39366-0.33880.206370.13392
mode-4.6125-2.3636-0.855461.92111.5393.32144.21731.82242.3945
mode k.dens-1.2486-0.638361.0321.27481.44551.6471.70040.658960.41352



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')